Real-Time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG

Probing the Neurodynamic Mechanisms of Cognitive Flexibility in Depressed Individuals with Autism Spectrum Disorder

Introduction: Autism spectrum disorder (ASD) is characterized by deficits in social behavior and executive function (EF), particularly in cognitive flexibility. Whether transcranial magnetic stimulation (TMS) can improve cognitive outcomes in patients with ASD remains an open question. We examined the acute effects of prefrontal TMS on cortical excitability and fluid cognition in individuals with ASD who underwent TMS for refractory major depression. Methods: We analyzed data from an open-label pilot study involving nine participants with ASD and treatment-resistant depression who received 30 sessions of accelerated theta burst stimulation of the dorsolateral prefrontal cortex, either unilaterally or bilaterally. Electroencephalography data were collected at baseline and 1, 4, and 12-weeks posttreatment and analyzed using a mixed-effects linear model to assess changes in regional cortical excitability using three models of spectral parametrization. Fluid cognition was measured using the National Institutes of Health Toolbox Cognitive Battery. Results: Prefrontal TMS led to a decrease in prefrontal cortical excitability and an increase in right temporoparietal excitability, as measured using spectral exponent analysis. This was associated with a significant improvement in the NIH Toolbox Fluid Cognition Composite score and the Dimensional Change Card Sort subtest from baseline to 12 weeks posttreatment (t = 3.79, p = 0.005, n = 9). Improvement in depressive symptomatology was significant (HDRS-17, F (3, 21) = 28.49, p < 0.001) and there was a significant correlation between cognitive improvement at week 4 and improvement in depression at week 12 (r = 0.71, p = 0.05). Conclusion: These findings link reduced prefrontal excitability in patients with ASD and improvements in cognitive flexibility. The degree to which these mechanisms can be generalized to ASD populations without Major Depressive Disorder remains a compelling question for future research.

The effects of gamma-tACS on cognitive impairment in multiple sclerosis: A randomized, double-blind, sham-controlled, pilot study

BACKGROUND

Multiple sclerosis (MS) often causes impairment in working memory (WM), information processing speed (IPS), and verbal memory (VM). These deficits are linked to disrupted neural oscillatory activity. Transcranial alternating current stimulation (tACS), which modulates cortical oscillations, may hold promise for treating cognitive impairment in MS.

OBJECTIVES

To evaluate online and offline effects of gamma (γ)-tACS on WM, IPS, and VM while assessing changes in brain rhythms using electroencephalography (EEG).

METHODS

Thirty-six MS patients with single-domain impairment in WM (12), IPS (13), or VM (11) underwent γ-tACS and sham-tACS over the left dorsolateral prefrontal cortex (DLPFC) (WM, IPS) or precuneus (VM). Cognitive performance was assessed pre-tACS (T0), during (T1), and post-tACS (T2) using the Digit Span Backward (DSBW) for WM, Symbol Digit Modalities Test (SDMT) for IPS, and Rey Auditory Verbal Learning Test (RAVLT) for VM. EEG was recorded at T0 and T2 to analyze local power spectral density and local-to-global connectivity.

RESULTS

DSBW, SDMT, and RAVLT scores transiently improved during γ-tACS and not during sham. IPS-impaired patients showed a reduction in spectral power across all frequency bands, at the stimulation site, post-DLPFC γ-tACS.

CONCLUSION

γ-tACS briefly improves WM, IPS, and VM in MS patients, warranting further trials of this non-invasive intervention.

Positive mood enhances gender stereotype activation during semantic integration and re-analysis

Gender stereotypes are deeply rooted in language, and their activation can be influenced by various factors. Behavioural evidence suggests that both positive and negative moods can modulate responses to stereotype-laden linguistic content. Early research also highlights the role of colour-gender associations in language processing. However, the neurocognitive mechanisms underlying the interaction between mood, gender stereotype activation, and colour-gender associations remain underexplored. Here, we provide novel neurocognitive evidence that a positive mood actively facilitates access to stereotype knowledge during the stage of semantic integration and re-analysis. Female participants induced into positive or negative moods made stereotype congruency judgments about sentences that were either congruent or incongruent with gender stereotypes, preceded by gendered (pink/blue circles) or gender-neutral (white circles) visual cues. First, the results showed smaller N200 amplitudes in a positive compared to negative mood only for sentences preceded by gender-neutral cues, suggesting that gender-driven expectancies evoked by gendered cues can override mood effects during early lexico-semantic processing. Second, we found smaller N400 amplitudes in a positive compared to negative mood, indicating overall facilitation of lexico-semantic processing in a positive mood, irrespective of stereotype congruency. Finally, we observed larger Late Positive Complex (LPC) amplitudes for stereotypically incongruent than congruent sentences only in a positive mood, pointing to gender stereotype knowledge modulating semantic integration and reanalysis processes in a positive but not negative mood.

Comparative analysis of early visual processes across presentation modalities: The event-related potential evoked by real-life, virtual reality, and planar objects

Characteristics of real-life objects, such as binocular depth, potentially yield visual processes going beyond what examinations of planar pictures as experimental cues can reveal. While virtual reality (VR) is used to approximate real-life features in experimental settings, this approach fundamentally hinges on whether the distinct modalities are processed in a similar way. To examine which stages of early visual processing depend on modality-specific characteristics, our study compares the electrophysiological responses to 2D (PC), VR, and real-life (RL) objects. To this end, participants passively explored abstract objects in one of these modalities followed by active exploration in a delayed matching-to-sample-task. Our results indicate that all modalities fundamentally yield comparable visual processes. Remarkably, our RL setup evoked the P1-N1-P2 complex corresponding to the well-established ERP morphology. However, the magnitude of the ERP response during real-life visual processing was more comparable to the response to VR than to PC. Indicating effects of stereoscopy on the earliest processing stages, the P1 differentiated only between PC and RL, and the N1 differentiated PC from both other conditions. In contrast, the P2 distinguished VR from both other conditions, which potentially indicated stereoscopic visual fatigue. Complementary analysis of the alpha-band response revealed higher attentional demands in response to PC and VR compared with RL, ruling out that the ERP-based results are exclusively driven by attentional effects. Whereas comparable fundamental processes are likely occurring under all modalities, our study advises the use of VR if the processes' magnitude is of relevance, emphasizing its value to approximate real-life visual processing.

Brain-Controlled Hand Exoskeleton Based on Augmented Reality-Fused Stimulus Paradigm

Advancements in brain-machine interfaces (BMIs) have led to the development of novel rehabilitation training methods for people with impaired hand function. However, contemporary hand exoskeleton systems predominantly adopt passive control methods, leading to low system performance. In this work, an active brain-controlled hand exoskeleton system is proposed that uses a novel augmented reality-fused stimulus (AR-FS) paradigm as a human-machine interface, which enables users to actively control their fingers to move. Considering that the proposed AR-FS paradigm generates movement artifacts during hand movements, an enhanced decoding algorithm is designed to improve the decoding accuracy and robustness of the system. In online experiments, participants performed online control tasks using the proposed system, with an average task time cost of 16.27 s, an average output latency of 1.54 s, and an average correlation instantaneous rate (CIR) of 0.0321. The proposed system shows 35.37% better efficiency, 8.03% reduced system delay, and 35.28% better stability than the traditional system. This study not only provides an efficient rehabilitation solution for people with impaired hand function but also expands the application prospects of brain-control technology in areas such as human augmentation, patient monitoring, and remote robotic interaction. The video in Graphical Abstract Video demonstrates the user's process of operating the proposed brain-controlled hand exoskeleton system.

Intersubject neural similarity reveals the development trajectory of recognition memory in children

Recognition memory improves with child development, but the neural mechanisms underlying such improvement and the developmental variation remain poorly understood. Herein, we investigated how the neural representations during the encoding and retrieval phases of recognition memory change with age, using representational similarity analysis in a sample of children aged 6-13 years (n = 137). Our results indicated that the encoding and retrieval phases have distinct neural patterns of development. Similarly, using a model-free approach, we confirmed that there is a key developmental stage (about 9-10 years old) for the neural representation during the encoding phase, whereas the neural representation during the retrieval phase tends to be stable with child development. Additionally, we identified that the neural similarity between the encoding and retrieval phases in children is primarily located in the left parietal-occipital region. Overall, these findings refine the developmental process underlying memory representation and enhance our understanding of the neural mechanisms of recognition memory.

Mind the road: attention related neuromarkers during automated and manual simulated driving captured with a new mobile EEG sensor system

Background

Decline in vigilance due to fatigue is a common concern in traffic safety. Partially automated driving (PAD) systems can aid driving but decrease the driver's vigilance over time, due to reduced task engagement. Mobile EEG solutions can obtain neural information while operating a vehicle. The purpose of this study was to investigate how the behavior and brain activity associated with vigilance (i.e., alpha, beta and theta power) differs between PAD and manual driving, as well as changes over time, and how these effects can be detected using two different EEG systems.

Methods

Twenty-eight participants performed two 1-h simulated driving tasks, while wearing both a standard 24 channel EEG cap and a newly developed, unobtrusive and easy to apply 10 channel mobile EEG sensor-grid system. One scenario required manual control of the vehicle (manual) while the other required only monitoring the vehicle (PAD). Additionally, lane deviation, percentage eye-closure (PERCLOS) and subjective ratings of workload, fatigue and stress were obtained.

Results

Alpha, beta and theta power of the EEG as well as PERCLOS were higher in the PAD condition and increased over time in both conditions. The same spectral EEG effects were evident in both EEG systems. Lane deviation as an index of driving performance in the manual driving condition increased over time.

Conclusion

These effects indicate significant increases in fatigue and vigilance decrement over time while driving, and overall higher levels of fatigue and vigilance decrement associated with PAD. The EEG measures revealed significant effects earlier than the behavioral measures, demonstrating that EEG might allow faster detection of decreased vigilance than behavioral driving measures. This new, mobile EEG-grid system could be used to evaluate and improve driver monitoring systems in the field or even be used in the future as additional sensor to inform drivers of critical changes in their level of vigilance. In addition to driving, further areas of application for this EEG-sensor grid are safety critical work environments where vigilance monitoring is pivotal.

The More, the Better? Evaluating the Role of EEG Preprocessing for Deep Learning Applications

The last decade has witnessed a notable surge in deep learning applications for electroencephalography (EEG) data analysis, showing promising improvements over conventional statistical techniques. However, deep learning models can underperform if trained with bad processed data. Preprocessing is crucial for EEG data analysis, yet there is no consensus on the optimal strategies in deep learning scenarios, leading to uncertainty about the extent of preprocessing required for optimal results. This study is the first to thoroughly investigate the effects of EEG preprocessing in deep learning applications, drafting guidelines for future research. It evaluates the effects of varying preprocessing levels, from raw and minimally filtered data to complex pipelines with automated artifact removal algorithms. Six classification tasks (eye blinking, motor imagery, Parkinson's, Alzheimer's disease, sleep deprivation, and first episode psychosis) and four established EEG architectures were considered for the evaluation. The analysis of 4800 trained models revealed statistical differences between preprocessing pipelines at the intra-task level for each model and at the inter-task level for the largest model. Models trained on raw data consistently performed poorly, always ranking last in average scores. In addition, models seem to benefit more from minimal pipelines without artifact handling methods. These findings suggest that EEG artifacts may affect the performance and generalizability of deep neural networks.

Intervention technology of aural perception controllable headset for children with autism spectrum disorder

This study explored aural perception in children with autism using an aural perception test and electrophysiological responses to sound stimuli. The results demonstrated unique responses to sound stimuli at different sound intensity levels, emphasising the need for customised noise-control strategies targeting specific troublesome frequencies. To address this issue, headset intervention technology with a hybrid active noise control system integrated with an aural perception controlling function was developed for children with autism with distinct auditory perception based on their psychoacoustic characteristics. The results showed that the noise-control strategy was effective in mitigating unpleasant feelings and reducing the loudness and sharpness of daily stimuli. The proposed aural perception controllable headset can minimise noise, leading to a noticeable reduction in the magnitude of the auditory evoked potential at the midline central brain region for children with autism exposed to certain sounds, such as heavy vehicles and thunder, providing a more pleasant aural perception. A diminished auditory evoked potential response was associated with lower annoyance and pleasant aural perception. This study suggests that the proposed aural-perception-based noise-control method has the potential to alleviate behaviours related to auditory hyperreactivity in children with autism.

The influence of time and visualization on neurofeedback-guided parietal alpha downregulation and sense of presence in virtual reality

In an EEG-based near real-time neurofeedback (NF) study in two parts using high immersive virtual reality (VR) we successfully trained healthy participants to downregulate their parietal alpha power, a neurophysiological correlate previously associated with enhanced sense of presence. The first part included n = 10 participants equipped with 128 and 64 channels gel-based active EEG electrodes in 10 sessions using standard bar feedback presented on a computer monitor. Nine participants were better than random at the 10th session and four improved over time. For the second part we reduced the electrode subset to 9 sponge-based active channels (2 frontal, 7 parietal around Pz) and a portable amplifier. Participants (n = 10) were trained each session within VR using bar feedback projected on a wall in the first 5 sessions and then controlling the flow of a water fountain. Participants were able to significantly downregulate their parietal alpha power after 5 sessions and learning occurred at the group level, with 7 participants showing both improvement over time and ability to modulate. However, these results were only shown during the fountain feedback and both ability and learning were non-significant in the VR projector condition. Based on self-reports, after excluding participants performing movements and closing their eyes, no particular mental strategy, such as relaxation, breathing or mental calculus was identified to help with alpha modulation. The hypothesized behavioral effect on sense of presence was not found nor any neurophysiological changes in fronto-parietal connectivity. While NF did not improve the sense of presence, we succeeded in adapting real-time NF training for high immersive VR technology via seamlessly embedded feedback in the form of a water fountain. The study showcases that NF is possible with sponge electrodes and portable EEG that would prove convenient in end-user (at home) or clinical setup. The dataset is publicly available on Openneuro.org.

Electrical brain activations in preadolescents during a probabilistic reward-learning task reflect cognitive processes and behavior strategies

Both adults and children learn through feedback to associate environmental events and choices with reward, a process known as reinforcement learning (RL). However, tasks to assess RL-related neurocognitive processes in children have been limited. This study validated a child version of the Probabilistic Reward Learning task in preadolescents (8-12 years) while recording event-related-potential (ERPs), focusing on: (1) reward-feedback sensitivity (frontal Reward-related Positivity, RewP), (2) late attention-related responses to feedback (parietal P300), and (3) attentional shifting toward favored stimuli (N2pc). Behaviorally, as expected, preadolescents could learn stimulus-reward outcome associations, but with varying performance levels. Poor learners showed greater RewP amplitudes compared to good learners. Learning strategies (i.e., Win-Lose-Stay-Shift) were reflected by feedback-elicited P300 amplitudes. Lastly, attention shifted toward to-be-chosen stimuli, as evidenced by the N2pc, but not toward more highly rewarded stimuli as in adults. These findings provide novel insights into the neural processes underlying RL in preadolescents.

EEG reveals brain network alterations in chronic aphasia during natural speech listening

Aphasia is a common consequence of a stroke which affects language processing. In search of an objective biomarker for aphasia, we used EEG to investigate how functional network patterns in the cortex are affected in persons with post-stroke chronic aphasia (PWA) compared to healthy controls (HC) while they are listening to a story. EEG was recorded from 22 HC and 27 PWA while they listened to a 25-min-long story. Functional connectivity between scalp regions was measured with the weighted phase lag index. The Network-Based Statistics toolbox was used to detect altered network patterns and to investigate correlations with behavioural tests within the aphasia group. Differences in network geometry were assessed by means of graph theory and a targeted node-attack approach. Group-classification accuracy was obtained with a support vector machine classifier. PWA showed stronger inter-hemispheric connectivity compared to HC in the theta-band (4.5-7 Hz), whilst a weaker subnetwork emerged in the low-gamma band (30.5-49 Hz). Two subnetworks correlated with semantic fluency in PWA respectively in delta- (1-4 Hz) and low-gamma-bands. In the theta-band network, graph alterations in PWA emerged at both local and global level, whilst only local changes were found in the low-gamma-band network. Network metrics discriminated PWA and HC with AUC = 83%. Overall, we demonstrate the potential of EEG-network metrics for the development of informative biomarkers to assess natural speech processing in chronic aphasia. We hypothesize that the detected alterations reflect compensatory mechanisms associated with recovery.

Unveiling the hidden electroencephalographical rhythms during development: Aperiodic and Periodic activity in healthy subjects

OBJECTIVE

The study analyzes power spectral density (PSD) components, aperiodic (AP) and periodic (P) activity, in resting-state EEG of 240 healthy subjects from 6 to 29 years old, divided into 4 groups.

METHODS

We calculate AP and P components using the (Fitting Oscillations and One-Over-f (FOOOF)) plugging in EEGLAB. All PSD components were calculated from 1-45 Hz. Topography analysis, Spearman correlations, and regression analysis with age were computed for all components.

RESULTS

AP and P activity show different topography across frequencies and age groups. Age-related decreases in AP exponent and offset parameters lead to reduced power, while P power decreases (1-6 Hz) and increases (10-15 Hz) with age.

CONCLUSIONS

We support the distinction between the AP and P components of the PSD and its possible functional changes with age. AP power is dominant in the configuration of the canonical EEG rhythms topography, although P contribution to topography is embedded in the canonical EEG topography. Some EEG canonical characteristics are similar to those of the P component, as topographies of EEG rhythms (embedded) and increases in oscillatory frequency with age.

SIGNIFICANCE

We support that spectral power parameterization improves the interpretation and neurophysiological and functional accuracy of brain processes.

Accelerated Infant Brain Rhythm Maturation in Autism

Electroencephalography (EEG) captures characteristic oscillatory shifts in infant brain rhythms over the first year of life, offering unique insights into early functional brain development and potential markers for detecting neural differences associated with autism. This study used functional principal component analysis (FPCA) to derive dynamic markers of spectral maturation from task-free EEG recordings collected at 3, 6, 9, and 12 months from 87 infants, 51 of whom were at higher likelihood of developing autism due to an older sibling diagnosed with the condition. FPCA revealed three principal components explaining over 96% of the variance in infant power spectra, with power increases between 6 and 9 Hz (FPC1) representing the most significant age-related trend, accounting for more than 71% of the variance. Notably, this oscillatory change occurred at a faster rate in infants later diagnosed with autism, indicated by a steeper trajectory of FPC1 scores between 3 and 12 months (p < 0.001). Age-related spectral changes were consistent regardless of familial likelihood status, suggesting that differences in oscillatory timing are associated with autism outcomes rather than genetic predisposition. These findings indicate that while the typical sequence of oscillatory maturation is preserved in autism, the timing of these changes is altered, underscoring the critical role of timing in autism pathophysiology and the development of potential screening tools.

Lasting and extensive consequences of left mesial temporal lobe seizures on electrical cortical activity

BACKGROUND AND OBJECTIVES

Focal epilepsies disrupt long-range networks with seizure recurrence driving both regional and global alterations in connectivity networks. While prior studies have focused on the interictal consequences, limited data exist on the direct aftermath of focal seizures. We hypothesize that mesial temporal lobe seizures lead to enduring cortical disorganization. The aim was to assess the effects of a mesial temporal lobe seizure on cortical activity and understand how the side of seizure onset influences these consequences.

METHODS

In this retrospective study, high-resolution EEG of patients with mesial temporal lobe epilepsy (mTLE) were analyzed. Groups of patients were identified based on the side of seizure onset. We compared relative powers in different frequency bands between interictal (prior to the seizure) and late postictal (one hour following the seizure) periods. Network-based statistics were employed to compare functional connectivity at source level between periods.

RESULTS

Twenty-three patients were included (13 left and 10 right mesial temporal lobe seizures). In patients with left mTLE, we observed a post-seizure increase in the relative spectral power in the delta band (p = 0.001) and a decrease in the relative spectral power in the alpha band (p = 0.013) over the left temporofrontal regions. We isolated a subnetwork that presented a decrease in connectivity strength in alpha band, primarily involving long-range left hemisphere connections (p = 0.042). We also identified a subnetwork that presented a decrease in connectivity strength in theta band, primarily involving interhemispheric connections (p = 0.039). No significant post-seizure changes were found in patients with right mTLE.

DISCUSSION

Left mesial temporal lobe seizures appear to be associated with lasting and widespread disorganization of cortical activity. We propose that the postictal state is associated with a prolonged functional deafferentation of the affected region in patients with left mTLE. This leads to a widespread disorganization of the functional networks, which may be associated with cognitive impairments and promote the progression of epilepsy. Further studies are required to fully understand the functional repercussions.

A New Perspective in Epileptic Seizure Classification: Applying the Taxonomy of Seizure Dynamotypes to Noninvasive EEG and Examining Dynamical Changes across Sleep Stages

Epilepsy, a neurological disorder characterized by recurrent unprovoked seizures, significantly impacts patient quality of life. Current classification methods focus primarily on clinical observations and electroencephalography (EEG) analysis, often overlooking the underlying dynamics driving seizures. This study uses surface EEG data to identify seizure transitions using a dynamical systems-based framework-the taxonomy of seizure dynamotypes-previously examined only in invasive data. We applied principal component and independent component (IC) analysis to surface EEG recordings from 1,177 seizures in 158 patients with focal epilepsy, decomposing the signals into ICs. The ICs were visually labeled for clear seizure transitions and bifurcation morphologies (BifMs), which were then examined using Bayesian multilevel modeling in the context of clinical factors. Our analysis reveals that certain onset bifurcations (saddle node on invariant circle and supercritical Hopf) are more prevalent during wakefulness compared with their reduced rate during nonrapid eye movement (NREM) sleep, particularly NREM3. We discuss the possible implications of our results in the context of modeling approaches and suggest additional avenues to continue this exploration. Furthermore, we demonstrate the feasibility of automating this classification process using machine learning, achieving high performance in identifying seizure-related ICs and classifying interspike interval changes. Our findings suggest that the noise in surface EEG may obscure certain BifMs, and we suggest technical improvements that could enhance detection accuracy. Expanding the dataset and incorporating long-term biological rhythms, such as circadian and multiday cycles, may provide a more comprehensive understanding of seizure dynamics and improve clinical decision-making.

Decoding Depth of Meditation: Electroencephalography Insights From Expert Vipassana Practitioners

Background

Meditation practices have demonstrated numerous psychological and physiological benefits, but capturing the neural correlates of varying meditative depths remains challenging. In this study, we aimed to decode self-reported time-varying meditative depth in expert practitioners using electroencephalography (EEG).

Methods

Expert Vipassana meditators (n = 34) participated in 2 separate sessions. Participants reported their meditative depth on a personally defined 1 to 5 scale using both traditional probing and a novel spontaneous emergence method. EEG activity and effective connectivity in theta, alpha, and gamma bands were used to predict meditative depth using machine/deep learning, including a novel method that fused source activity and connectivity information.

Results

We achieved significant accuracy in decoding self-reported meditative depth across unseen sessions. The spontaneous emergence method yielded improved decoding performance compared with traditional probing and correlated more strongly with postsession outcome measures. Best performance was achieved by a novel machine learning method that fused spatial, spectral, and connectivity information. Conventional EEG channel-level methods and preselected default mode network regions fell short in capturing the complex neural dynamics associated with varying meditation depths.

Conclusions

This study demonstrates the feasibility of decoding personally defined meditative depth using EEG. The findings highlight the complex, multivariate nature of neural activity during meditation and introduce spontaneous emergence as an ecologically valid and less obtrusive experiential sampling method. These results have implications for advancing neurofeedback techniques and enhancing our understanding of meditative practices.

Visual information processing of 2D, virtual 3D and real-world objects marked by theta band responses: Visuospatial processing and cognitive load as a function of modality

While pictures share global similarities with the real-world objects they depict, the latter have unique characteristics going beyond 2D representations. Due to its three-dimensional presentation mode, Virtual Reality (VR) is increasingly used to further approach real-world visual processing, yet it remains unresolved to what extent VR yields process comparable to real-world processes. Consequently, our study examined visuospatial processing by a triangular comparison of 2D objects, virtual 3D objects and real 3D objects. The theta band response (TBR) was analysed as an electrophysiological correlate of visual processing, allowing for the differentiation of predominantly stimulus-driven processes mirrored in the evoked response and internal, complex processing reflected in the induced response. Our results indicate that the differences between conditions driven by sensory features go beyond a binary division into 2D and 3D materials but are based on further sensory features: The evoked posterior TBR differentiated between all conditions but revealed fewer differences between processing of real-world and VR objects. Moreover, the induced midfrontal TBR indicated higher cognitive load for 2D objects compared to VR and real-world objects, while no difference between both latter conditions was revealed. In conclusion, our results demonstrate that the transferability of 2D- and VR-based findings to real-world processes depends to some degree on whether predominantly sensory stimulus features or higher cognitive processes are examined. Yet although VR and real-world processes are not to be equated based on our results, their comparison yielded fewer significant differences relative to the PC condition, advising the use of VR to examine visuospatial processing.

A roadmap for improving data quality through standards for collaborative intelligence in human-robot applications

Collaborative intelligence (CI) involves human-machine interactions and is deemed safety-critical because their reliable interactions are crucial in preventing severe injuries and environmental damage. As these applications become increasingly data-driven, the reliability of CI applications depends on the quality of data, shaping the system's ability to interpret and respond in diverse and often unpredictable environments. In this regard, it is important to adhere to data quality standards and guidelines, thus facilitating the advancement of these collaborative systems in industry. This study presents the challenges of data quality in CI applications within industrial environments, with two use cases that focus on the collection of data in Human-Robot Interaction (HRI). The first use case involves a framework for quantifying human and robot performance within the context of naturalistic robot learning, wherein humans teach robots using intuitive programming methods within the domain of HRI. The second use case presents real-time user state monitoring for adaptive multi-modal teleoperation, that allows for a dynamic adaptation of the system's interface, interaction modality and automation level based on user needs. The article proposes a hybrid standardization derived from established data quality-related ISO standards and addresses the unique challenges associated with multi-modal HRI data acquisition. The use cases presented in this study were carried out as part of an EU-funded project, Collaborative Intelligence for Safety-Critical Systems (CISC).

Theta power reduction and theta-gamma coupling desynchronization are associated with working memory interference and anxiety symptoms in panic disorder: a retrospective study

BACKGROUND

Theta-gamma coupling (TGC) describes the modulation of gamma oscillations by the theta phasic activity, which is crucial for processes such as the ordering of information during working memory (WM) performance. The mental arithmetic (MA), which involves performing calculations with numbers, is a crucial tool for evaluating and understanding the sensory processing and management abilities of WM. Evaluating TGC may provide greater insight into the neural mechanisms mediating WM deficits in panic disorder (PD).

METHODS

Medical and electroencephalography (EEG) records of psychiatric outpatient clinic between 1 March 2020 and 30 September 2023 were retrospectively reviewed. A total of 34 PD patients and 34 age- and sex-matched healthy controls (HCs) underwent EEG to assess the overall functional interaction of the brain using multi-channel EEG analysis, focusing on specific brain regions including the frontal, temporal, parietal, and occipital lobes. EEG recordings were conducted during two sessions: a 5-min eyes-closed resting-state (RS) and a subsequent 5-min eyes-closed MA. The TGC and the spectral power of the theta and gamma frequency bands, which are well known to be associated with WM, were analysed.

RESULTS

Compared to those in HCs, TGC and theta power were significantly attenuated in PD patients. When analysing both HCs and PD patients together, RS TGC and relative theta power were negatively correlated with state anxiety and perceived stress scores, respectively. In contrast, TGC and relative theta power during the MA condition were positively correlated with the MA performance. Specifically, in PD patients, RS theta power across all electrodes was significantly negatively correlated with the Hamilton Anxiety Scale (HAMA) score. Linear regression analysis revealed that theta power in the T5 channel remained negatively correlated with pathological anxiety as measured by the HAMA score, even after controlling for other confounding factors.

CONCLUSIONS

This study highlights significant alterations in TGC and theta power in PD patients. PD patients exhibit reduced TGC and theta power compared to HCs, indicating deficits in the neural mechanisms underlying anxiety and/or WM in PD. These insights contribute to a better understanding of the neural basis of WM deficits in PD and suggest potential avenues for targeted therapeutic interventions.

A comparative analysis of face and object perception in 2D laboratory and virtual reality settings: insights from induced oscillatory responses

In psychophysiological research, the use of Virtual Reality (VR) for stimulus presentation allows for the investigation of how perceptual processing adapts to varying degrees of realism. Previous time-domain studies have shown that perceptual processing involves modality-specific neural mechanisms, as evidenced by distinct stimulus-locked components. Analyzing induced oscillations across different frequency bands can provide further insights into neural processes that are not strictly phase-locked to stimulus onset. This study uses a simple perceptual paradigm presenting images of faces and cars on both a standard 2D monitor and in an immersive VR environment. To investigate potential modality-dependent differences in attention, cognitive load, and task-related post-movement processing, the induced alpha, theta and beta band responses are compared between the two modalities. No evidence was found for differences in stimulus-dependent attention or task-related post-movement processing between the 2D conditions and the realistic virtual conditions in electrode space, as posterior alpha suppression and re-synchronization of centro-parietal beta did not differ between conditions. However, source analysis revealed differences in the attention networks engaged during 2D and 3D perception. Midfrontal theta was significantly stronger in laboratory conditions, indicating higher cognitive load than in the VR environment. Exploratory analysis of posterior theta showed stronger responses in VR, possibly reflecting the processing of depth information provided only by the 3D material. In addition, the theta response seems to be generated by distinct neuronal sources under realistic virtual conditions indicating enhanced involvement of semantic information processing and social cognition.

The Effect of Sensory Reweighting on Postural Control and Cortical Activity in Parkinson's Disease: A Pilot Study

Objective

To investigate the effects of sensory reweighting on postural control and cortical activity in individuals with Parkinson's disease (PD) compared to age-matched controls using a virtual reality sensory organization test (VR-SOT).

Design

Cross-sectional pilot study.

Setting

University research laboratory.

Participants

Ten participants with idiopathic Parkinson's disease and 11 age- and sex-matched control participants without neurologic disorders.

Interventions

Not applicable.

Main Outcome Measures

Changes in center of pressure (COP) and electroencephalography (EEG) activity (ie, power) in the alpha band and the theta/beta ratio recorded during the VR-SOT were the main outcome variables.

Results

PD participants exhibited greater COP displacement, particularly in the mediolateral direction across sensory conditions. They also showed increased alpha power when relying on visual inputs and increased theta/beta ratio power when depending on somatosensory inputs.

Conclusion

PD affects sensory reweighting mechanisms involved in postural control, as evidenced by greater COP displacement and altered cortical activity. These findings emphasize the potential of EEG and VR-SOT in understanding and monitoring postural control impairments in PD.

Refining ADHD diagnosis with EEG: The impact of preprocessing and temporal segmentation on classification accuracy

BACKGROUND

EEG signals are commonly used in ADHD diagnosis, but they are often affected by noise and artifacts. Effective preprocessing and segmentation methods can significantly enhance the accuracy and reliability of ADHD classification.

METHODS

We applied filtering, ASR, and ICA preprocessing techniques to EEG data from children with ADHD and neurotypical controls. The EEG recordings were segmented, and features were extracted and selected based on statistical significance. Classification was performed using various EEG segments and channels with Machine Learning models (SVM, KNN, and XGBoost) to identify the most effective combinations for accurate ADHD diagnosis.

RESULTS

Our findings show that models trained on later EEG segments achieved significantly higher accuracy, indicating the potential role of cognitive fatigue in distinguishing ADHD. The highest classification accuracy (86.1%) was achieved using data from the P3, P4, and C3 channels, with key features such as Kurtosis, Katz fractal dimension, and power spectrums in the Delta, Theta, and Alpha bands contributing to the results.

CONCLUSION

This study highlights the importance of preprocessing and segmentation in improving the reliability of ADHD diagnosis through EEG. The results suggest that further research on cognitive fatigue and segmentation could enhance diagnostic accuracy in ADHD patients.

EEG hyperexcitability and hyperconnectivity linked to GABAergic inhibitory interneuron loss following traumatic brain injury

Traumatic brain injury represents a significant global health burden and has the highest prevalence among neurological disorders. Even mild traumatic brain injury can induce subtle, long-lasting changes that increase the risk of future neurodegeneration. Importantly, this can be challenging to detect through conventional neurological assessment. This underscores the need for more sensitive diagnostic tools, such as electroencephalography, to uncover opportunities for therapeutic intervention. Progress in the field has been hindered by a lack of studies linking mechanistic insights at the microscopic level from animal models to the macroscale phenotypes observed in clinical imaging. Our study addresses this gap by investigating a rat model of mild blast traumatic brain injury using both immunohistochemical staining of inhibitory interneurons and translationally relevant electroencephalography recordings. Although we observed no pronounced effects immediately post-injury, chronic time points revealed broadband hyperexcitability and increased connectivity, accompanied by decreased density of inhibitory interneurons. This pattern suggests a disruption in the balance between excitation and inhibition, providing a crucial link between cellular mechanisms and clinical hallmarks of injury. Our findings have significant implications for the diagnosis, monitoring, and treatment of traumatic brain injury. The emergence of electroencephalography abnormalities at chronic time points, despite the absence of immediate effects, highlights the importance of long-term monitoring in traumatic brain injury patients. The observed decrease in inhibitory interneuron density offers a potential cellular mechanism underlying the electroencephalography changes and may represent a target for therapeutic intervention. This study demonstrates the value of combining cellular-level analysis with macroscale neurophysiological recordings in animal models to elucidate the pathophysiology of traumatic brain injury. Future research should focus on translating these findings to human studies and exploring potential therapeutic strategies targeting the excitation-inhibition imbalance in traumatic brain injury.

Resting-state EEG microstate analysis of internet gaming disorder and alcohol use disorder

INTRODUCTION

To investigate the neurophysiological aspects of addiction, the microstate characteristics of internet gaming disorder (IGD), alcohol use disorder (AUD), and healthy control (HC) groups were compared using resting-state electroencephalography (EEG).

METHODS

In total, 199 young adults (75 patients with IGD, 57 patients with AUD, and 67 HCs) participated in this study. We conducted EEG microstate analysis among the groups and also compared the obtained parameters with the results of psychological assessments.

RESULTS

The global explained variance, occurrence, and coverage of microstate C were significantly lower in the AUD group than in the IGD group. Additionally, rates of transition from microstates A, B, and D to C were significantly lower in the AUD group than in the IGD group, whereas rates of transition from microstate A to B were lower in the IGD group compared to HCs. Furthermore, the occurrence of microstate C and transition from microstate B to C were negatively correlated with the Alcohol Use Disorder Identification and Behavioural Inhibition Scale score.

CONCLUSION

There were significant differences in microstate characteristics among the groups, which correlated with the psychological scores. These findings suggest that microstate features can be used as neuromarkers in clinical settings to differentiate between addictive disorders and evaluate the pathophysiology of AUD and IGD.

Association Between Cognitive Function and the Autonomic Nervous System by Photoplethysmography

This study explored the relationship between cognitive function and the autonomic nervous system by categorizing participants into two groups based on their cognitive function scores in each domain of the SNSB-D: a High Cognitive Performance (HCP) group and a Low Cognitive Performance (LCP) group. We analyzed the Pulse Rate Variability (PRV) parameters for each group. Photoplethysmography (PPG) data were collected and processed to remove noise, and the PRV parameters in the time and frequency domains were extracted. To minimize the impact of age and years of education on the PRV parameters, we performed an adjusted analysis using a Generalized Linear Model (GLM). The analysis revealed that the autonomic nervous system, particularly the parasympathetic nervous system, was more activated in the LCP group compared to the HCP group. This finding suggests that in individuals with low cognitive function, the sympathetic nerves in the autonomic nervous system are less activated, so the parasympathetic nerves are relatively more activated. This study investigated the correlation between cognitive function and PRV parameters, highlighting the potential use of these parameters as indicators for the early diagnosis and classification of cognitive decline.

The Role of Anatomic Connectivity in Inhibitory Control Revealed by Combining Connectome-based Lesion-symptom Mapping with Event-related Potentials

Inhibitory control refers to the ability to suppress cognitive or motor processes. Current neurocognitive models indicate that this function mainly involves the anterior cingulate cortex and the inferior frontal cortex. However, how the communication between these areas influence inhibitory control performance and their functional response remains unknown. We addressed this question by injecting behavioral and electrophysiological markers of inhibitory control recorded during a Go/NoGo task as the 'symptoms' in a connectome-based lesion-symptom mapping approach in a sample of 96 first unilateral stroke patients. This approach enables us to identify the white matter tracts whose disruption by the lesions causally influences brain functional activity during inhibitory control. We found a central role of left frontotemporal and frontobasal intrahemispheric connections, as well as of the connections between the left temporoparietal and right temporal areas in inhibitory control performance. We also found that connections between the left temporal and right superior parietal areas modulate the conflict-related N2 event-related potential component and between the left temporal parietal area and right temporal and occipital areas for the inhibition P3 component. Our study supports the role of a distributed bilateral network in inhibitory control and reveals that combining lesion-symptom mapping approaches with functional indices of cognitive processes could shed new light on post-stroke functional reorganization. It may further help to refine the interpretation of classical electrophysiological markers of executive control in stroke patients.

Using mobile EEG to study auditory work strain during simulated surgical procedures

Surgical personnel face various stressors in the workplace, including environmental sounds. Mobile electroencephalography (EEG) offers a promising approach for objectively measuring how individuals perceive sounds. Because surgical performance does not necessarily decrease with higher levels of distraction, EEG could help guide noise reduction strategies that are independent of performance measures. In this study, we utilized mobile EEG to explore how a realistic soundscape is perceived during simulated laparoscopic surgery. To examine the varying demands placed on personnel in different situations, we manipulated the cognitive demand during the surgical task, using a memory task. To assess responses to the soundscape, we calculated event-related potentials for distinct sound events and temporal response functions for the ongoing soundscape. Although participants reported varying degrees of demand under different conditions, no significant effects were observed on surgical task performance or EEG parameters. However, changes in surgical task performance and EEG parameters over time were noted, while subjective results remained consistent over time. These findings highlight the importance of using multiple measures to fully understand the complex relationship between sound processing and cognitive demand. Furthermore, in the context of combined EEG and audio recordings in real-life scenarios, a sparse representation of the soundscape has the advantage that it can be recorded in a data-protected way compared to more detailed representations. However, it is unclear whether information get lost with sparse representations. Our results indicate that sparse and detailed representations are equally effective in eliciting neural responses. Overall, this study marks a significant step towards objectively investigating sound processing in applied settings.

Improved ADHD Diagnosis Using EEG Connectivity and Deep Learning through Combining Pearson Correlation Coefficient and Phase-Locking Value

Attention Deficit Hyperactivity Disorder (ADHD) is a widespread neurobehavioral disorder affecting children and adolescents, requiring early detection for effective treatment. EEG connectivity measures can reveal the interdependencies between EEG recordings, highlighting brain network patterns and functional behavior that improve diagnostic accuracy. This study introduces a novel ADHD diagnostic method by combining linear and nonlinear brain connectivity maps with an attention-based convolutional neural network (Att-CNN). Pearson Correlation Coefficient (PCC) and Phase-Locking Value (PLV) are used to create fused connectivity maps (FCMs) from various EEG frequency subbands, which are then inputted into the Att-CNN. The attention module is strategically placed after the latest convolutional layer in the CNN. The performance of different optimizers (Adam and SGD) and learning rates are assessed. The suggested model obtained 98.88%, 98.41%, 98.19%, and 98.30% for accuracy, precision, recall, and F1 Score, respectively, using the SGD optimizer in the FCM of the theta band with a learning rate of 1e-1. With the use of FCM, Att-CNN, and advanced optimizers, the proposed technique has the potential to produce trustworthy instruments for the early diagnosis of ADHD, greatly enhancing both patient outcomes and diagnostic accuracy.

Frontal alpha asymmetry predicts subsequent social decision-making: A dynamic multilevel, neural, and developmental perspective

Social motivation, the human desire to engage with others, is likely to underlie higher levels of social cognition and the formation of interpersonal relationships. Yet, this topic has been understudied in adolescents despite the critical developmental and maturational changes that occur during this period and the relevance of social motivation to clinical and neurodevelopmental disorders. Using electroencephalography (EEG) and an implicit-association paradigm (Choose-A-Movie Task; Dubey et al., 2015), we examined how brain responses underlying socially motivated decisions informed future decisions in 54 youth (aged 10-14 years) and 50 young adults (aged 18-33 years). As the first study to use this task during EEG recording, we implemented time-frequency analyses and a trial-by-trial dynamic statistical approach. Results suggested that both age groups preferred low-effort choices and increasingly preferred nonsocial choices over time. P3 amplitude also increased over time and was sensitive to effortful decisions, particularly for adults, but not social content. Both groups showed larger leftward frontal alpha asymmetry (FAA) during nonsocial feedback, and FAA predicted future decisions differently for adults than youth. The current study highlights FAA and trial-by-trial analyses as useful tools in understanding the neural mechanisms underlying socially motivated decisions, which differ across development, time, and individuals.

o-CLEAN: a novel multi-stage algorithm for the ocular artifacts' correction from EEG data in out-of-the-lab applications

In the context of electroencephalographic (EEG) signal processing, artifacts generated by ocular movements, such as blinks, are significant confounding factors. These artifacts overwhelm informative EEG features and may occur too frequently to simply remove affected epochs without losing valuable data. Correcting these artifacts remains a challenge, particularly in out-of-lab and online applications using wearable EEG systems (i.e. with low number of EEG channels, without any additional channels to track EOG).Objective.The main objective of the present work consisted in validating a novel ocular blinks artefacts correction method, named multi-stage OCuLar artEfActs deNoising algorithm (o-CLEAN), suitable for online processing with minimal EEG channels.Approach.The research was conducted considering one EEG dataset collected in highly controlled environment, and a second one collected in real environment. The analysis was performed by comparing the o-CLEAN method with previously validated state-of-art techniques, and by evaluating its performance along two dimensions: (a) the ocular artefacts correction performance (IN-Blink), and (b) the EEG signal preservation when the method was applied without any ocular artefacts occurrence (OUT-Blink).Main results.Results highlighted that (i) o-CLEAN algorithm resulted to be, at least, significantly reliable as the most validated approaches identified in scientific literature in terms of ocular blink artifacts correction, (ii) o-CLEAN showed the best performances in terms of EEG signal preservation especially with a low number of EEG channels.Significance.The testing and validation of the o-CLEAN addresses a relevant open issue in bioengineering EEG processing, especially within out-of-the-lab application. In fact, the method offers an effective solution for correcting ocular artifacts in EEG signals with a low number of available channels, for online processing, and without any specific template of the EOG. It was demonstrated to be particularly effective for EEG data gathered in real environments using wearable systems, a rapidly expanding area within applied neuroscience.

Rapid reconfiguration of cortical networks after repeated exposure to visual-vestibular conflicts

Visual-vestibular conflicts can induce motion sickness and further postural instability. Visual-vestibular habituation is recommended to reduce the symptoms of motion sickness and improve postural stability with an altered multisensory reweighting progress. However, it is unclear how the human brain reweights multisensory information after repeated exposure to visual-vestibular conflicts. Therefore, we synchronized a rotating platform and a virtual scene to present visual-vestibular congruent (natural visual stimulation) and incongruent (conflicted visual stimulation) conditions and collected EEG and center of pressure (COP) data. We constructed the effective brain connectivity of region of interest (ROI) derived from source-space EEG in theta-band activity, and quantified the postural stability and the inflow and outflow of each ROI. We found repeated exposure to congruent and incongruent conditions both decreased COP path length and increased COP complexity. Besides, we found that repeated exposure to the incongruent environment decreased the inflow into visual cortex, suggesting the brain down-weighted the less reliable visual information for postural stability. In contrast, repeated exposure to the congruent environment increased the inflow into posterior parietal cortex and the outflow from visual cortex and S1, suggesting an increase in efficiency of multisensory integration. We concluded that repeated exposure to congruent and incongruent conditions both improved postural stability with different multisensory reweighting patterns as revealed by different dynamic changes of brain networks.

Identifying neural correlates of balance impairment in traumatic brain injury using partial least squares correlation analysis

Objective.Balance impairment is one of the most debilitating consequences of traumatic brain injury (TBI). To study the neurophysiological underpinnings of balance impairment, the brain functional connectivity during perturbation tasks can provide new insights. To better characterize the association between the task-relevant functional connectivity and the degree of balance deficits in TBI, the analysis needs to be performed on the data stratified based on the balance impairment. However, such stratification is not straightforward, and it warrants a data-driven approach.Approach.We conducted a study to assess the balance control using a computerized posturography platform in 17 individuals with TBI and 15 age-matched healthy controls. We stratified the TBI participants into balance-impaired and non-impaired TBI usingk-means clustering of either center of pressure (COP) displacement during a balance perturbation task or Berg Balance Scale score as a functional outcome measure. We analyzed brain functional connectivity using the imaginary part of coherence across different cortical regions in various frequency bands. These connectivity features are then studied using the mean-centered partial least squares correlation analysis, which is a multivariate statistical framework with the advantage of handling more features than the number of samples, thus making it suitable for a small-sample study.Main results.Based on the nonparametric significance testing using permutation and bootstrap procedure, we noticed that the weakened theta-band connectivity strength in the following regions of interest significantly contributed to distinguishing balance impaired from non-impaired population, regardless of the type of stratification:left middle frontal gyrus, right paracentral lobule, precuneus, andbilateral middle occipital gyri. Significance.Identifying neural regions linked to balance impairment enhances our understanding of TBI-related balance dysfunction and could inform new treatment strategies. Future work will explore the impact of balance platform training on sensorimotor and visuomotor connectivity.

Decoding working-memory load duringn-back task performance from high channel fNIRS data

Objective.Functional near-infrared spectroscopy (fNIRS) can measure neural activity through blood oxygenation changes in the brain in a wearable form factor, enabling unique applications for research in and outside the lab and in practical occupational settings. fNIRS has proven capable of measuring cognitive states such as mental workload, often using machine learning (ML) based brain-computer interfaces (BCIs). To date, this research has largely relied on probes with channel counts from under ten to several hundred, although recently a new class of wearable NIRS devices featuring thousands of channels has emerged. This poses unique challenges for ML classification, as fNIRS is typically limited by few training trials which results in severely under-determined estimation problems. So far, it is not well understood how such high-resolution data is best leveraged in practical BCIs and whether state-of-the-art or better performance can be achieved.Approach.To address these questions, we propose an ML strategy to classify working-memory load that relies on spatio-temporal regularization and transfer learning from other subjects in a combination that, to our knowledge, has not been used in previous fNIRS BCIs. The approach can be interpreted as an end-to-end generalized linear model and allows for a high degree of interpretability using channel-level or cortical imaging approaches.Main results.We show that using the proposed methodology, it is possible to achieve state-of-the-art decoding performance with high-resolution fNIRS data. We also replicated several state-of-the-art approaches on our dataset of 43 participants wearing a 3198 dual-channel NIRS device while performing then-Back task and show that these existing methodologies struggle in the high-channel regime and are largely outperformed by the proposed pipeline.Significance.Our approach helps establish high-channel NIRS devices as a viable platform for state-of-the-art BCI and opens new applications using this class of headset while also enabling high-resolution model imaging and interpretation.

A multistrategy differential evolution algorithm combined with Latin hypercube sampling applied to a brain-computer interface to improve the effect of node displacement

Injection molding is a common plastic processing technique that allows melted plastic to be injected into a mold through pressure to form differently shaped plastic parts. In injection molding, in-mold electronics (IME) can include various circuit components, such as sensors, amplifiers, and filters. These components can be injected into the mold to form a whole within the melted plastic and can therefore be very easily integrated into the molded part. The brain-computer interface (BCI) is a direct connection pathway between a human or animal brain and an external device. Through BCIs, individuals can use their own brain signals to control these components, enabling more natural and intuitive interactions. In addition, brain-computer interfaces can also be used to assist in medical treatments, such as controlling prosthetic limbs or helping paralyzed patients regain mobility. Brain-computer interfaces can be realized in two ways: invasively and noninvasively, and in this paper, we adopt a noninvasive approach. First, a helmet model is designed according to head shape, and second, a printed circuit film is made to receive EEG signals and an IME injection mold for the helmet plastic parts. In the electronic film, conductive ink is printed to connect each component. However, improper parameterization during the injection molding process can lead to node displacements and residual stress changes in the molded part, which can damage the circuits in the electronic film and affect its performance. Therefore, in this paper, the use of the BCI molding process to ensure that the node displacement reaches the optimal value is studied. Second, the multistrategy differential evolutionary algorithm is used to optimize the injection molding parameters in the process of brain-computer interface formation. The relationship between the injection molding parameters and the actual target value is investigated through Latin hypercubic sampling, and the optimized parameters are compared with the target parameters to obtain the optimal parameter combination. Under the optimal parameters, the node displacement can be optimized from 0.585 to 0.027 mm, and the optimization rate can reach 95.38%. Ultimately, by detecting whether the voltage difference between the output inputs is within the permissible range, the reliability of the brain-computer interface after node displacement optimization can be evaluated.

Parallel EEG assessment of different sound predictability levels in tinnitus

Tinnitus denotes the perception of a non-environmental sound and might result from aberrant auditory prediction. Successful prediction of formal (e.g., type) and temporal sound characteristics facilitates the filtering of irrelevant information, also labelled as 'sensory gating' (SG). Here, we explored if and how parallel manipulations of formal prediction violations and temporal predictability affect SG in persons with and without tinnitus. Age-, education- and sex-matched persons with and without tinnitus (N = 52) participated and listened to paired-tone oddball sequences, varying in formal (standard vs. deviant pitch) and temporal predictability (isochronous vs. random timing). EEG was recorded from 128 channels and data were analyzed by means of temporal spatial principal component analysis (tsPCA). SG was assessed by amplitude suppression for the 2nd tone in a pair and was observed in P50-like activity in both timing conditions and groups. Correspondingly, deviants elicited overall larger amplitudes than standards. However, only persons without tinnitus displayed a larger N100-like deviance response in the isochronous compared to the random timing condition. This result might imply that persons with tinnitus do not benefit similarly as persons without tinnitus from temporal predictability in deviance processing. Thus, persons with tinnitus might display less temporal sensitivity in auditory processing than persons without tinnitus.

Age differences in the principal temporo-spatial components of EEG activity during a proactive interference task

Proactive interference (PI) is the disruptive effect of no longer relevant information on current working memory (WM) processing. PI effects in EEG data have been previously found to be altered in healthy aging, although it remains unclear the extent to which such changes reflect delayed or different brain mechanisms employed to overcome PI. Hence, we had twenty-six young (18-34 years) and sixteen old (53-68 years) healthy adults complete a Recent Probes task while EEG was recorded. Compared to young adults, old adults were slower, less accurate and less able to discriminate when they last saw a given stimulus, but PI effects on reaction time were greater in the former, likely due to a general difficulty that old adults had in the task. Temporo-spatial principal component analysis of the EEG data showed young and older adults to differ in terms of temporal and spatial characteristics of brain activity associated with resolving PI. YA showed a factor indicative of a medial frontal negativity (MFN) that showed greater amplitude in low compared to high PI trials. OA, in contrast, showed a late positive component (LPC), although similarly with larger amplitude in low compared to high PI trials. The modulation of the MFN component in YA may reflect the recruitment of cognitive control to overcome PI. The modulation of the LPC in OA may represent the detection of conflict between familiarity and context recollection during PI.

Aperiodic activity differences in individuals with high and low temporal processing efficiency

It is known that Temporal Information Processing (TIP) underpins our cognitive functioning. Previous research has focused on the relationship between TIP efficiency and oscillatory brain activity, especially the gamma rhythm; however, non-oscillatory (aperiodic or 1/f) brain activity has often been missed. Recent studies have identified the 1/f component as being important for the functioning of the brain. Therefore, the current study aimed to verify whether TIP efficiency is associated with specific EEG resting state cortical activity patterns, including oscillatory and non-oscillatory (aperiodic) brain activities. To measure individual TIP efficiency, we used two behavioral tasks in which the participant judges the order of two sounds separated by millisecond intervals. Based on the above procedure, participants were classified into two groups with high and low TIP efficiency. Using cluster-based permutation analyses, we examined between-group differences in oscillatory and non-oscillatory (aperiodic) components across the 1-90 Hz range. The results revealed that the groups differed in the aperiodic component across the 30-80 Hz range in fronto-central topography. In other words, participants with low TIP efficiency exhibited higher levels of aperiodic activity, and thus a flatter frequency spectrum compared to those with high TIP efficiency. We conclude that participants with low TIP efficiency display higher levels of 'neural noise', which is associated with poorer quality and speed of neural processing.

Reduced neural distinctiveness of speech representations in the middle-aged brain

Speech perception declines independent of hearing thresholds in middle-age, and the neurobiological reasons are unclear. In line with the age-related neural dedifferentiation hypothesis, we predicted that middle-aged adults show less distinct cortical representations of phonemes and acoustic-phonetic features relative to younger adults. In addition to an extensive audiological, auditory electrophysiological, and speech perceptual test battery, we measured electroencephalographic responses time-locked to phoneme instances (phoneme-related potential; PRP) in naturalistic, continuous speech and trained neural network classifiers to predict phonemes from these responses. Consistent with age-related neural dedifferentiation, phoneme predictions were less accurate, more uncertain, and involved a broader network for middle-aged adults compared with younger adults. Representational similarity analysis revealed that the featural relationship between phonemes was less robust in middle-age. Electrophysiological and behavioral measures revealed signatures of cochlear neural degeneration (CND) and speech perceptual deficits in middle-aged adults relative to younger adults. Consistent with prior work in animal models, signatures of CND were associated with greater cortical dedifferentiation, explaining nearly a third of the variance in PRP prediction accuracy together with measures of acoustic neural processing. Notably, even after controlling for CND signatures and acoustic processing abilities, age-group differences in PRP prediction accuracy remained. Overall, our results reveal "fuzzier" phonemic representations, suggesting that age-related cortical neural dedifferentiation can occur even in middle-age and may underlie speech perceptual challenges, despite a normal audiogram.

Characterizing Autism Spectrum Disorder Through Fusion of Local Cortical Activation and Global Functional Connectivity Using Game-Based Stimuli and a Mobile EEG System

The deficit in social interaction skills among individuals with autism spectrum disorder (ASD) is strongly influenced by personal experiences and social environments. Neuroimaging studies have previously highlighted the link between social impairment and brain activity in ASD. This study aims to develop a method for assessing and identifying ASD using a social cognitive game-based paradigm combined with electroencephalo-graphy (EEG) signaling features. Typically developing (TD) participants and autistic preadolescents and teenagers were recruited to participate in a social game while 12-channel EEG signals were recorded. The EEG signals underwent preprocessing to analyze local brain activities, including event-related potentials (ERPs) and time-frequency features. Additionally, the global brain network's functional connectivity between brain regions was evaluated using phase-lag indices (PLIs). Subsequently, machine learning models were employed to assess the neurophysiological features. Results indicated pronounced ERP components, particularly the late positive potential (LPP), in parietal regions during social training. Autistic preadolescents and teenagers exhibited lower LPP amplitudes and larger P200 amplitudes compared to TD participants. Reduced theta synchronization was also observed in the ASD group. Aberrant functional connectivity within certain time intervals was noted in the ASD group. Machine learning analysis revealed that support-vector machines achieved a sensitivity of 100%, specificity of 91.7%, and accuracy of 95.8% as part of the performance evaluation when utilizing ERP and brain oscillation features for ASD characterization. These findings suggest that social interaction difficulties in autism are linked to specific brain activation patterns. Traditional behavioral assessments face challenges of subjectivity and accuracy, indicating the potential use of social training interfaces and EEG features for cognitive assessment in ASD.

Emotion perception through the nose: how olfactory emotional cues modulate the perception of neutral facial expressions in affective disorders

Humans can decode emotional states from the body odors of the conspecifics and this type of emotional communication is particularly relevant in conditions in which social interactions are impaired, as in depression and social anxiety. The present study aimed to explore how body odors collected in happiness and fearful conditions modulate the subjective ratings, the psychophysiological response and the neural processing of neutral faces in individuals with depressive symptoms, social anxiety symptoms, and healthy controls (N = 22 per group). To this aim, electrocardiogram (ECG) and HD-EEG were recorded continuously. Heart Rate Variability (HRV) was extracted from the ECG as a measure of vagal tone, event-related potentials (ERPs) and event-related spectral perturbations (ERPSs) were extracted from the EEG. The results revealed that the HRV increased during the fear and happiness body odors conditions compared to clean air, but no group differences emerged. For ERPs data, repeated measure ANOVA did not show any significant effects. However, the ERPSs analyses revealed a late increase in delta power and a reduced beta power both at an early and a late stage of stimulus processing in response to the neutral faces presented with the emotional body odors, regardless of the presence of depressive or social anxiety symptoms. The current research offers new insights, demonstrating that emotional chemosignals serve as potent environmental cues. This represents a substantial advancement in comprehending the impact of emotional chemosignals in both individuals with and without affective disorders.

Operating in a second language lowers cognitive interference during creative idea generation: Evidence from brain oscillations in bilinguals

Tasks measuring human creativity overwhelmingly rely on both language comprehension and production. Although most of the world's population is bilingual, few studies have investigated the effects of language of operation on creative output. This is surprising given that fluent bilinguals master inhibitory control, a mechanism also at play in creative idea evaluation. Here, we compared creative output in the two languages of Polish(L1)-English(L2) bilinguals engaged in a cyclic adaptation of the Alternative Uses Task increasing the contribution of idea evaluation (convergent thinking). We show that Polish-English bilinguals suffer less cognitive interference when generating unusual uses for common objects in the L2 than the L1, without incurring a significant drop in idea originality. Right posterior alpha oscillation power, known to reflect creative thinking, increased over cycles. This effect paralleled the increase in originality ratings over cycles, and lower alpha power (8-10 Hz) was significantly greater in the L1 than the L2. Unexpectedly, we found greater beta (16.5-28 Hz) desynchronization in the L2 than the L1, suggesting that bilingual participants suffered less interference from competing mental representations when performing the task in the L2. Whereas creative output seems unaffected by language of operation overall, the drop in beta power in the L2 suggests that bilinguals are not subjected to the same level of semantic flooding in the second language as they naturally experience in their native language.

Habituation of Central and Electrodermal Responses to an Auditory Two-Stimulus Oddball Paradigm

The orienting reaction (OR) towards a new stimulus is subject to habituation, i.e., progressively attenuates with stimulus repetition. The skin conductance responses (SCRs) are known to represent a reliable measure of OR at the peripheral level. Yet, it is still a matter of debate which of the P3 subcomponents is the most likely to represent the central counterpart of the OR. The aim of the present work was to study habituation, recovery, and dishabituation phenomena intrinsic to a two-stimulus auditory oddball paradigm, one of the most-used paradigms both in research and clinic, by simultaneously recording SCRs and P3 in twenty healthy volunteers. Our findings show that the target stimulus was capable of triggering a more marked OR, as indexed by both SCRs and P3, compared to the standard stimulus, that could be due to its affective saliency and relevance for task completion; the application of temporal principal components analysis (PCA) to the P3 complex allowed us to identify several subcomponents including both early and late P3a (eP3a; lP3a), P3b, novelty P3 (nP3), and both a positive and a negative Slow Wave (+SW; -SW). Particularly, lP3a and P3b subcomponents showed a similar behavior to that observed for SCRs , suggesting them as central counterparts of OR. Finally, the P3 evoked by the first standard stimulus after the target showed a significant dishabituation phenomenon which could represent a sign of the local stimulus change. However, it did not reach a sufficient level to trigger an SCR/OR since it did not represent a salient event in the context of the task.

Phase-shifted tACS can modulate cortical alpha waves in human subjects

In the present study, we investigated traveling waves induced by transcranial alternating current stimulation in the alpha frequency band of healthy subjects. Electroencephalographic data were recorded in 12 healthy subjects before, during, and after phase-shifted stimulation with a device combining both electroencephalographic and stimulation capacities. In addition, we analyzed the results of numerical simulations and compared them to the results of identical analysis on real EEG data. The results of numerical simulations indicate that imposed transcranial alternating current stimulation induces a rotating electric field. The direction of waves induced by stimulation was observed more often during at least 30 s after the end of stimulation, demonstrating the presence of aftereffects of the stimulation. Results suggest that the proposed approach could be used to modulate the interaction between distant areas of the cortex. Non-invasive transcranial alternating current stimulation can be used to facilitate the propagation of circulating waves at a particular frequency and in a controlled direction. The results presented open new opportunities for developing innovative and personalized transcranial alternating current stimulation protocols to treat various neurological disorders.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-023-09997-1.

Classification of mental workload with EEG analysis by using effective connectivity and a hybrid model of CNN and LSTM

Estimation of mental workload from electroencephalogram (EEG) signals aims to accurately measure the cognitive demands placed on an individual during multitasking mental activities. By analyzing the brain activity of the subject, we can determine the level of mental effort required to perform a task and optimize the workload to prevent cognitive overload or underload. This information can be used to enhance performance and productivity in various fields such as healthcare, education, and aviation. In this paper, we propose a method that uses EEG and deep neural networks to estimate the mental workload of human subjects during multitasking mental activities. Notably, our proposed method employs subject-independent classification. We use the "STEW" dataset, which consists of two tasks, namely "No task" and "simultaneous capacity (SIMKAP)-based multitasking activity". We estimate the different workload levels of two tasks using a composite framework consisting of brain connectivity and deep neural networks. After the initial preprocessing of EEG signals, an analysis of the relationships between the 14 EEG channels is conducted to evaluate effective brain connectivity. This assessment illustrates the information flow between various brain regions, utilizing the direct Directed Transfer Function (dDTF) method. Then, we propose a deep hybrid model based on pre-trained Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for the classification of workload levels. The accuracy of the proposed deep model achieved 83.12% according to the subject-independent leave-subject-out (LSO) approach. The pre-trained CNN + LSTM approaches to EEG data have been found to be an accurate method for assessing the mental workload.

A machine learning artefact detection method for single-channel infant event-related potential studies

Objective. Automated detection of artefact in stimulus-evoked electroencephalographic (EEG) data recorded in neonates will improve the reproducibility and speed of analysis in clinical research compared with manual identification of artefact. Some studies use very short, single-channel epochs of EEG data with little recorded EEG per infant-for example because the clinical vulnerability of the infants limits access for recording. Current artefact-detection methods that perform well on adult data and resting-state and multi-channel data in infants are not suitable for this application. The aim of this study was to create and test an automated method of detecting artefact in single-channel 1500 ms epochs of infant EEG.Approach. A total of 410 epochs of EEG were used, collected from 160 infants of 28-43 weeks postmenstrual age. This dataset-which was balanced to include epochs of background activity and responses to visual, auditory, tactile and noxious stimuli-was presented to seven independent raters, who independently labelled the epochs according to whether or not they were able to visually identify artefacts. The data was split into a training set (340 epochs) and an independent test set (70 epochs). A random forest model was trained to identify epochs as either artefact or not artefact.Main results. This model performs well, achieving a balanced accuracy of 0.81, which is as good as manual review of data. Accuracy was not significantly related to the infant age or type of stimulus.Significance. This method provides an objective tool for automated artefact rejection for short epoch, single-channel EEG in neonates and could increase the utility of EEG in neonates in both the clinical and research setting.

Adaptive Filtering with Fitted Noise Estimate (AFFiNE): Blink Artifact Correction in Simulated and Real P300 Data

(1) Background: The electroencephalogram (EEG) is frequently corrupted by ocular artifacts such as saccades and blinks. Methods for correcting these artifacts include independent component analysis (ICA) and recursive-least-squares (RLS) adaptive filtering (-AF). Here, we introduce a new method, AFFiNE, that applies Bayesian adaptive regression spline (BARS) fitting to the adaptive filter's reference noise input to address the known limitations of both ICA and RLS-AF, and then compare the performance of all three methods. (2) Methods: Artifact-corrected P300 morphologies, topographies, and measurements were compared between the three methods, and to known truth conditions, where possible, using real and simulated blink-corrupted event-related potential (ERP) datasets. (3) Results: In both simulated and real datasets, AFFiNE was successful at removing the blink artifact while preserving the underlying P300 signal in all situations where RLS-AF failed. Compared to ICA, AFFiNE resulted in either a practically or an observably comparable error. (4) Conclusions: AFFiNE is an ocular artifact correction technique that is implementable in online analyses; it can adapt to being non-stationarity and is independent of channel density and recording duration. AFFiNE can be utilized for the removal of blink artifacts in situations where ICA may not be practically or theoretically useful.

Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability

Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy - a critical period of rapid brain development and plasticity - microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong psychometric properties (i.e., reliability) during infancy is unknown and key information for advancing our understanding of how microstates are shaped by early life experiences and whether they relate to individual differences in infant abilities. A lack of methodological resources for performing microstate analysis of infant EEG has further hindered adoption of this cutting-edge approach by infant researchers. As a result, in the current study, we systematically addressed these knowledge gaps and report that most microstate-based measurements of brain organization and functioning except for transition probabilities were stable with four minutes of video-watching resting-state data and highly internally consistent with just one minute. In addition to these results, we provide a step-by-step tutorial, accompanying website, and open-access data for performing microstate analysis using a free, user-friendly software called Cartool. Taken together, the current study supports the reliability and feasibility of using EEG microstate analysis to study infant brain development and increases the accessibility of this approach for the field of developmental neuroscience.

Electrical brain activations in preadolescents during a probabilistic reward-learning task reflect cognitive processes and behavioral strategy

Both adults and children learn through feedback which environmental events and choices are associated with higher probability of reward, an ability thought to be supported by the development of fronto-striatal reward circuits. Recent developmental studies have applied computational models of reward learning to investigate such learning in children. However, tasks and measures effective for assaying the cascade of reward-learning neural processes in children have been limited. Using a child-version of a probabilistic reward-learning task while recording event-related-potential (ERP) measures of electrical brain activity, this study examined key processes of reward learning in preadolescents (8-12 years old; n=30), namely: (1) reward-feedback sensitivity, as measured by the early-latency, reward-related, frontal ERP positivity, (2) rapid attentional shifting of processing toward favored visual stimuli, as measured by the N2pc component, and (3) longer-latency attention-related responses to reward feedback as a function of behavioral strategies (i.e., Win-Stay-Lose-Shift), as measured by the central-parietal P300. Consistent with our prior work in adults, the behavioral findings indicate preadolescents can learn stimulus-reward outcome associations, but at varying levels of performance. Neurally, poor preadolescent learners (those with slower learning rates) showed greater reward-related positivity amplitudes relative to good learners, suggesting greater reward-feedback sensitivity. We also found attention shifting towards to-be-chosen stimuli, as evidenced by the N2pc, but not to more highly rewarded stimuli as we have observed in adults. Lastly, we found the behavioral learning strategy (i.e., Win-Stay-Lose-Shift) reflected by the feedback-elicited parietal P300. These findings provide novel insights into the key neural processes underlying reinforcement learning in preadolescents.

Exploring the Association Between EEG Microstates During Resting-State and Error-Related Activity in Young Children

The error-related negativity (ERN) is a negative deflection in the electroencephalography (EEG) waveform at frontal-central scalp sites that occurs after error commission. The relationship between the ERN and broader patterns of brain activity measured across the entire scalp that support error processing during early childhood is unclear. We examined the relationship between the ERN and EEG microstates - whole-brain patterns of dynamically evolving scalp potential topographies that reflect periods of synchronized neural activity - during both a go/no-go task and resting-state in 90, 4-8-year-old children. The mean amplitude of the ERN was quantified during the -64 to 108 millisecond (ms) period of time relative to error commission, which was determined by data-driven microstate segmentation of error-related activity. We found that greater magnitude of the ERN associated with greater global explained variance (GEV; i.e., the percentage of total variance in the data explained by a given microstate) of an error-related microstate observed during the same -64 to 108 ms period (i.e., error-related microstate 3), and to greater anxiety risk as measured by parent-reported behavioral inhibition. During resting-state, six data-driven microstates were identified. Both greater magnitude of the ERN and greater GEV values of error-related microstate 3 associated with greater GEV values of resting-state microstate 4, which showed a frontal-central scalp topography. Source localization results revealed overlap between the underlying neural generators of error-related microstate 3 and resting-state microstate 4 and canonical brain networks (e.g., ventral attention) known to support the higher-order cognitive processes involved in error processing. Taken together, our results clarify how individual differences in error-related and intrinsic brain activity are related and enhance our understanding of developing brain network function and organization supporting error processing during early childhood.