Automatic classification of artifactual ICA-components for artifact removal in EEG signals

Neural dynamics differentially encode phrases and sentences during spoken language comprehension

Human language stands out in the natural world as a biological signal that uses a structured system to combine the meanings of small linguistic units (e.g., words) into larger constituents (e.g., phrases and sentences). However, the physical dynamics of speech (or sign) do not stand in a one-to-one relationship with the meanings listeners perceive. Instead, listeners infer meaning based on their knowledge of the language. The neural readouts of the perceptual and cognitive processes underlying these inferences are still poorly understood. In the present study, we used scalp electroencephalography (EEG) to compare the neural response to phrases (e.g., the red vase) and sentences (e.g., the vase is red), which were close in semantic meaning and had been synthesized to be physically indistinguishable. Differences in structure were well captured in the reorganization of neural phase responses in delta (approximately <2 Hz) and theta bands (approximately 2 to 7 Hz),and in power and power connectivity changes in the alpha band (approximately 7.5 to 13.5 Hz). Consistent with predictions from a computational model, sentences showed more power, more power connectivity, and more phase synchronization than phrases did. Theta-gamma phase-amplitude coupling occurred, but did not differ between the syntactic structures. Spectral-temporal response function (STRF) modeling revealed different encoding states for phrases and sentences, over and above the acoustically driven neural response. Our findings provide a comprehensive description of how the brain encodes and separates linguistic structures in the dynamics of neural responses. They imply that phase synchronization and strength of connectivity are readouts for the constituent structure of language. The results provide a novel basis for future neurophysiological research on linguistic structure representation in the brain, and, together with our simulations, support time-based binding as a mechanism of structure encoding in neural dynamics.

A Novel Approach for Segment-Length Selection Based on Stationarity to Perform Effective Connectivity Analysis Applied to Resting-State EEG Signals

Connectivity among different areas within the brain is a topic that has been notably studied in the last decade. In particular, EEG-derived measures of effective connectivity examine the directionalities and the exerted influences raised from the interactions among neural sources that are masked out on EEG signals. This is usually performed by fitting multivariate autoregressive models that rely on the stationarity that is assumed to be maintained over shorter bits of the signals. However, despite being a central condition, the selection process of a segment length that guarantees stationary conditions has not been systematically addressed within the effective connectivity framework, and thus, plenty of works consider different window sizes and provide a diversity of connectivity results. In this study, a segment-size-selection procedure based on fourth-order statistics is proposed to make an informed decision on the appropriate window size that guarantees stationarity both in temporal and spatial terms. Specifically, kurtosis is estimated as a function of the window size and used to measure stationarity. A search algorithm is implemented to find the segments with similar stationary properties while maximizing the number of channels that exhibit the same properties and grouping them accordingly. This approach is tested on EEG signals recorded from six healthy subjects during resting-state conditions, and the results obtained from the proposed method are compared to those obtained using the classical approach for mapping effective connectivity. The results show that the proposed method highlights the influence that arises in the Default Mode Network circuit by selecting a window of 4 s, which provides, overall, the most uniform stationary properties across channels.

Task-Modulated Oscillation Differences in Auditory and Spoken Chinese-English Bilingual Processing: An Electroencephalography Study

Neurophysiological research on the bilingual activity of interpretation or interpreting has been very fruitful in understanding the bilingual brain and has gained increasing popularity recently. Issues like word interpreting and the directionality of interpreting have been attended to by many researchers, mainly with localizing techniques. Brain structures such as the dorsolateral prefrontal cortex have been repeatedly identified during interpreting. However, little is known about the oscillation and synchronization features of interpreting, especially sentence-level overt interpreting. In this study we implemented a Chinese-English sentence-level overt interpreting experiment with electroencephalography on 43 Chinese-English bilinguals and compared the oscillation and synchronization features of interpreting with those of listening, speaking and shadowing. We found significant time-frequency power differences in the delta-theta (1–7 Hz) and gamma band (above 30 Hz) between motor and silent tasks. Further theta-gamma coupling analysis revealed different synchronization networks in between speaking, shadowing and interpreting, indicating an idea-formulation dependent mechanism. Moreover, interpreting incurred robust right frontotemporal gamma coactivation network compared with speaking and shadowing, which we think may reflect the language conversion process inherent in interpreting.

Development of an Adaptive Artifact Subspace Reconstruction Based on Hebbian/Anti-Hebbian Learning Networks for Enhancing BCI Performance

Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c = 1-10 for activity-specific BCI applications and a moderate range of for the benchmark dataset and general BCI applications.

Cortical Reorganization Following Psychoeducational Counselling and Residual Inhibition Therapy (RIT) in Individuals with Tinnitus

Introduction Psychoeducational counselling and residual inhibition therapy (RIT) are traditional approaches used in many clinics to manage tinnitus. However, neurophysiological studies to evaluate posttreatment perceptual and functional cortical changes in humans are scarce.

Objectives The present study aims to explore whether cortical auditory-evoked potentials (CAEPs; N1 and P3) reflect the effect of modified RIT and psychoeducational counselling, and whether there is a correlation between the behavioral and electrophysiological measures.

Methods Ten participants with continuous and bothersome tinnitus underwent a session of psychoeducational counselling and modified RIT. Perceptual measures and CAEPs were recorded pre- and posttreatment. Further, the posttreatment measures were compared with age and gender-matched historical control groups.

Results Subjectively, 80% of the participants reported a reduction in the loudness of their tinnitus. Objectively, there was a significant reduction in the posttreatment amplitude of N1 and P3, with no alterations in latency. There was no correlation between the perceived difference in tinnitus loudness and the difference in P3 amplitude (at Pz).

Conclusion The perceptual and functional (as evidenced by sensory, N1, and cognitive, P3 reduction) changes after a single session of RIT and psychoeducational counselling are suggestive of plastic changes at the cortical level. The current study serves as preliminary evidence that event-related potentials (ERPs) can be used to quantify the physiological changes that occur after the intervention for tinnitus.

Cortical mechanisms of visual hypersensitivity in women at risk for chronic pelvic pain

ABSTRACT

Increased sensory sensitivity across non-nociceptive modalities is a common symptom of chronic pain conditions and is associated with chronic pain development. Providing a better understanding of the brain-behavior relationships that underlie multimodal hypersensitivity (MMH) may clarify the role of MMH in the development of chronic pain. We studied sensory hypersensitivity in a cohort of women (n = 147) who had diary confirmation of menstrual status and were enriched with risk factors for chronic pelvic pain, such as dysmenorrhea and increased bladder sensitivity. We administered 2 experimental tasks to evaluate the cross-modal relationship between visual and visceral sensitivity. Visual sensitivity was probed by presenting participants with a periodic pattern-reversal checkerboard stimulus presented across 5 brightness intensities during electroencephalography recording. Self-reported visual unpleasantness ratings for each brightness intensity were simultaneously assessed. Visceral sensitivity was evaluated with an experimental bladder-filling task associated with early clinical symptoms of chronic pelvic pain. Visually evoked cortical activity increased with brightness intensity across the entire scalp, especially at occipital electrode sites. Visual stimulation-induced unpleasantness was associated with provoked bladder pain and evoked primary visual cortex activity. However, the relationship between unpleasantness and cortical activity was moderated by provoked bladder pain. These results demonstrate that activity in the primary visual cortex is not greater in individuals with greater visceral sensitivity. We hypothesize that downstream interpretation or integration of this signal is amplified in individuals with visceral hypersensitivity. Future studies aimed at reducing MMH in chronic pain conditions should prioritize targeting of cortical mechanisms responsible for aberrant downstream sensory integration.

Effect of background luminance of visual stimulus on elicited steady-state visual evoked potentials

Steady-state visual evoked potential (SSVEP)-based brain– computer interfaces (BCIs) have been widely studied. Considerable progress has been made in the aspects of stimulus coding, electroencephalogram processing, and recognition algorithms to enhance system performance. The properties of SSVEP have been demonstrated to be highly sensitive to stimulus luminance. However, thus far, there have been very few reports on the impact of background luminance on the system performance of SSVEP-based BCIs. This study investigated the impact of stimulus background luminance on SSVEPs. Specifically, this study compared two types of background luminance, i.e., (1) black luminance [red, green, blue (rgb): (0, 0, 0)] and (2) gray luminance [rgb: (128, 128, 128)], and determined their effect on the classification performance of SSVEPs at the stimulus frequencies of 9, 11, 13, and 15 Hz. The offline results from nine healthy subjects showed that compared with the gray background luminance, the black background luminance induced larger SSVEP amplitude and larger signal-to-noise ratio, resulting in a better classification accuracy. These results suggest that the background luminance of visual stimulus has a considerable effect on the SSVEP and therefore has a potential to improve the BCI performance.

SRI-EEG: State-Based Recurrent Imputation for EEG Artifact Correction

Electroencephalogram (EEG) signals are often used as an input modality for Brain Computer Interfaces (BCIs). While EEG signals can be beneficial for numerous types of interaction scenarios in the real world, high levels of noise limits their usage to strictly noise-controlled environments such as a research laboratory. Even in a controlled environment, EEG is susceptible to noise, particularly from user motion, making it highly challenging to use EEG, and consequently BCI, as a ubiquitous user interaction modality. In this work, we address the EEG noise/artifact correction problem. Our goal is to detect physiological artifacts in EEG signal and automatically replace the detected artifacts with imputed values to enable robust EEG sensing overall requiring significantly reduced manual effort than is usual. We present a novel EEG state-based imputation model built upon a recurrent neural network, which we call SRI-EEG, and evaluate the proposed method on three publicly available EEG datasets. From quantitative and qualitative comparisons with six conventional and neural network based approaches, we demonstrate that our method achieves comparable performance to the state-of-the-art methods on the EEG artifact correction task.

A Brief Summary of EEG Artifact Handling

There are various obstacles in the way of use of EEG. Among these, the major obstacles are the artifacts. While some artifacts are avoidable, due to the nature of the EEG techniques there are inevitable artifacts as well. Artifacts can be categorized as internal/physiological or external/non-physiological. The most common internal artifacts are ocular or muscular origins. Internal artifacts are difficult to detect and remove, because they contain signal information as well. For both resting state EEG and ERP studies, artifact handling needs to be carefully carried out in order to retain the maximal signal. Therefore, an effective management of these inevitable artifacts is critical for the EEG based researches. Many researchers from various fields studied this challenging phenomenon and came up with some solutions. However, the developed methods are not well known by the real practitioners of EEG as a tool because of their limited knowledge about these engineering approaches. They still use the traditional visual inspection of the EEG. This work aims to inform the researchers working in the field of EEG about the artifacts and artifact management options available in order to increase the awareness of the available tools such as EEG preprocessing pipelines.

Electroconvulsive Therapy-Induced Changes in Functional Brain Network of Major Depressive Disorder Patients: A Longitudinal Resting-State Electroencephalography Study

Objectives

Several studies have shown abnormal network topology in patients with major depressive disorder (MDD). However, changes in functional brain networks associated with electroconvulsive therapy (ECT) remission based on electroencephalography (EEG) signals have yet to be investigated.

Methods

Nineteen-channel resting-state eyes-closed EEG signals were collected from 24 MDD patients pre- and post-ECT treatment. Functional brain networks were constructed by using various coupling methods and binarization techniques. Changes in functional connectivity and network metrics after ECT treatment and relationships between network metrics and clinical symptoms were explored.

Results

ECT significantly increased global efficiency, edge betweenness centrality, local efficiency, and mean degree of alpha band after ECT treatment, and an increase in these network metrics had significant correlations with decreased depressive symptoms in repeated measures correlation. In addition, ECT regulated the distribution of hubs in frontal and occipital lobes.

Conclusion

ECT modulated the brain’s global and local information-processing patterns. In addition, an ECT-induced increase in network metrics was associated with clinical remission.

Significance

These findings might present the evidence for us to understand how ECT regulated the topology organization in functional brain networks of clinically remitted depressive patients.

Temporal continuity of self: Long autocorrelation windows mediate self-specificity

The self is characterized by an intrinsic temporal component consisting in continuity across time. On the neural level, this temporal continuity manifests in the brain's intrinsic neural timescales (INT) that can be measured by the autocorrelation window (ACW). Recent EEG studies reveal a relationship between resting state ACW and self-consciousness. However, it remains unclear whether ACW exhibits different degrees of task-related changes during self-specific compared to non-self-specific activities. To this end, participants in our study initially recorded an eight-minute autobiographical narrative. Following a resting-state session, participants were presented with their own narrative and the narrative of a stranger while undergoing concurrent EEG recording. Behaviorally, subjects evaluated both of the narratives and indicated their perceptions of positivity or negativity on a moment-to-moment basis by positioning a cursor relative to the center of the computer screen. Our results indicate: (a) greater spatial extension and velocity in the behavioral cursor movement during the self narrative assessment compared to the non-self narrative assessment; and (b) longer neural ACWs in response to the self- compared to the non-self narrative and rest. These findings demonstrate the importance of longer temporal windows in neural activity measured by ACWs for self-specificity. More broadly, the results highlight the relevance of temporal continuity for the self on the neural level. Such temporal continuity may, correspondingly, also manifest on the psychological level as a "common currency" between brain and self.

Musical and linguistic syntactic processing in agrammatic aphasia: An ERP study

Language and music rely on complex sequences organized according to syntactic principles that are implicitly understood by enculturated listeners. Across both domains, syntactic processing involves predicting and integrating incoming elements into higher-order structures. According to the Shared Syntactic Integration Resource Hypothesis (SSIRH; Patel, 2003), musical and linguistic syntactic processing rely on shared resources for integrating incoming elements (e.g., chords, words) into unfolding sequences. One prediction of the SSIRH is that people with agrammatic aphasia (whose deficits are due to syntactic integration problems) should present with deficits in processing musical syntax. We report the first neural study to test this prediction: event-related potentials (ERPs) were measured in response to musical and linguistic syntactic violations in a group of people with agrammatic aphasia (n=7) compared to a group of healthy controls (n=14) using an acceptability judgement task. The groups were matched with respect to age, education, and extent of musical training. Violations were based on morpho-syntactic relations in sentences and harmonic relations in chord sequences. Both groups presented with a significant P600 response to syntactic violations across both domains. The aphasic participants presented with a reduced-amplitude posterior P600 compared to the healthy adults in response to linguistic, but not musical, violations. Participants with aphasia did however present with larger frontal positivities in response to violations in both domains. Intriguingly, extent of musical training was associated with larger posterior P600 responses to syntactic violations of language and music in both groups. Overall, these findings are not consistent with the predictions of the SSIRH, and instead suggest that linguistic, but not musical, syntactic processing may be selectively impaired in stroke-induced agrammatic aphasia. However, the findings also suggest a relationship between musical training and linguistic syntactic processing, which may have clinical implications for people with aphasia, and motivates more research on the relationship between these two domains.

Timing of adoption is associated with electrophysiological brain activity and externalizing problems among children adopted internationally

This study investigated middle childhood resting electroencephalography (EEG) and behavioral adjustment in 35 internationally adopted children removed from early caregiving adversity between 6 and 29 months of age. Older age of adoption was associated with more immature or atypical profiles of middle childhood cortical function, based on higher relative theta power (4-6 Hz), lower relative alpha power (7-12 Hz), lower peak alpha frequency, and lower absolute beta (13-20 Hz) and gamma (21-50 Hz) power. More immature or atypical EEG spectral power indirectly linked older age of adoption with increased risk for externalizing problems in middle childhood. The findings add to existing evidence linking duration of early adverse exposures with lasting effects on brain function and behavioral regulation even years after living in a stable adoptive family setting. Findings underscore the need to minimize and prevent children's exposures to early caregiving adversity, especially in the first years of life. They call for innovative interventions to support neurotypical development in internationally adopted children at elevated risk.

Naturalistic viewing conditions can increase task engagement and aesthetic preference but have only minimal impact on EEG quality

Free gaze and moving images are typically avoided in EEG experiments due to the expected generation of artifacts and noise. Yet for a growing number of research questions, loosening these rigorous restrictions would be beneficial. Among these is research on visual aesthetic experiences, which often involve open-ended exploration of highly variable stimuli. Here we systematically compare the effect of conservative vs. more liberal experimental settings on various measures of behavior, brain activity and physiology in an aesthetic rating task. Our primary aim was to assess EEG signal quality. 43 participants either maintained fixation or were allowed to gaze freely, and viewed either static images or dynamic (video) stimuli consisting of dance performances or nature scenes. A passive auditory background task (auditory steady-state response; ASSR) was added as a proxy measure for overall EEG recording quality. We recorded EEG, ECG and eyetracking data, and participants rated their aesthetic preference and state of boredom on each trial. Whereas both behavioral ratings and gaze behavior were affected by task and stimulus manipulations, EEG SNR was barely affected and generally robust across all conditions, despite only minimal preprocessing and no trial rejection. In particular, we show that using video stimuli does not necessarily result in lower EEG quality and can, on the contrary, significantly reduce eye movements while increasing both the participants' aesthetic response and general task engagement. We see these as encouraging results indicating that - at least in the lab - more liberal experimental conditions can be adopted without significant loss of signal quality.

Resilience-Building for Mental Health among Early Childhood Educators: A Systematic Review and Pilot-Study towards an EEG-VR Resilience Building Intervention

Resilience is a key factor that reflects a teacher's ability to utilize their emotional resources and working skills to provide high-quality teaching to children. Resilience-building interventions aim to promote positive psychological functioning and well-being. However, there is lack of evidence on whether these interventions improve the well-being or mental health of teachers in early childhood education (ECE) settings. This review examined the overall effectiveness of resilience-building interventions conducted on teachers working in the ECE field. A systematic approach is used to identify relevant studies that focus on resilience-building in countering work stress among early childhood educators. Findings from this review observed a preference of group approaches and varying durations of interventions. This review highlights the challenges of the group approach which can lead to lengthy interventions and attrition amongst participants. In addition to the concerns regarding response bias from self-report questionnaires, there is also a lack of physiological measures used to evaluate effects on mental health. The large efforts by 11 studies to integrate multiple centres into their intervention and the centre-based assessment performed by four studies highlight the need for a centre-focused approach to build resilience among teachers from various ECE centres. A pilot study is conducted to evaluate the feasibility of an integrated electroencephalography-virtual reality (EEG-VR) approach in building resilience in teachers, where the frontal brain activity can be monitored during a virtual classroom task. Overall, the findings of this review propose the integration of physiological measures to monitor changes in mental health throughout the resilience-building intervention and the use of VR as a tool to design a unique virtual environment.

Independent evaluation of the harvard automated processing pipeline for Electroencephalography 1.0 using multi-site EEG data from children with Fragile X Syndrome

BACKGROUND

The Harvard Automatic Processing Pipeline for Electroencephalography (HAPPE) is a computerized EEG data processing pipeline designed for multiple site analysis of populations with neurodevelopmental disorders. This pipeline has been validated in-house by the developers but external testing using real-world datasets remains to be done.

NEW METHOD

Resting and auditory event-related EEG data from 29 children ages 3-6 years with Fragile X Syndrome as well as simulated EEG data was used to evaluate HAPPE's noise reduction techniques, data standardization features, and data integration compared to traditional manualized processing.

RESULTS

For the real EEG data, HAPPE pipeline showed greater trials retained, greater variance retained through independent component analysis (ICA) component removal, and smaller kurtosis than the manual pipeline; the manual pipeline had a significantly larger signal-to-noise ratio (SNR). For simulated EEG data, correlation between the pure signal and processed data was significantly higher for manually-processed data compared to HAPPE-processed data. Hierarchical linear modeling showed greater signal recovery in the manual pipeline with the exception of the gamma band signal which showed mixed results.

COMPARISON WITH EXISTING METHODS

SNR and simulated signal retention was significantly greater in the manually-processed data than the HAPPE-processed data. Signal reduction may negatively affect outcome measures.

CONCLUSIONS

The HAPPE pipeline benefits from less active processing time and artifact reduction without removing segments. However, HAPPE may bias toward elimination of noise at the cost of signal. Recommended implementation of the HAPPE pipeline for neurodevelopmental populations depends on the goals and priorities of the research.

Enhancement of speech-in-noise comprehension through vibrotactile stimulation at the syllabic rate

Syllables are important building blocks of speech. They occur at a rate between 4 and 8 Hz, corresponding to the theta frequency range of neural activity in the cerebral cortex. When listening to speech, the theta activity becomes aligned to the syllabic rhythm, presumably aiding in parsing a speech signal into distinct syllables. However, this neural activity cannot only be influenced by sound, but also by somatosensory information. Here, we show that the presentation of vibrotactile signals at the syllabic rate can enhance the comprehension of speech in background noise. We further provide evidence that this multisensory enhancement of speech comprehension reflects the multisensory integration of auditory and tactile information in the auditory cortex.

Speech unfolds over distinct temporal scales, in particular, those related to the rhythm of phonemes, syllables, and words. When a person listens to continuous speech, the syllabic rhythm is tracked by neural activity in the theta frequency range. The tracking plays a functional role in speech processing: Influencing the theta activity through transcranial current stimulation, for instance, can impact speech perception. The theta-band activity in the auditory cortex can also be modulated through the somatosensory system, but the effect on speech processing has remained unclear. Here, we show that vibrotactile feedback presented at the rate of syllables can modulate and, in fact, enhance the comprehension of a speech signal in background noise. The enhancement occurs when vibrotactile pulses occur at the perceptual center of the syllables, whereas a temporal delay between the vibrotactile signals and the speech stream can lead to a lower level of speech comprehension. We further investigate the neural mechanisms underlying the audiotactile integration through electroencephalographic (EEG) recordings. We find that the audiotactile stimulation modulates the neural response to the speech rhythm, as well as the neural response to the vibrotactile pulses. The modulations of these neural activities reflect the behavioral effects on speech comprehension. Moreover, we demonstrate that speech comprehension can be predicted by particular aspects of the neural responses. Our results evidence a role of vibrotactile information for speech processing and may have applications in future auditory prosthesis.

Inter-trial theta phase consistency during face processing in infants is associated with later emerging autism

A growing body of research suggests that consistency in cortical activity may be a promising neurophysiological marker of autism spectrum disorder (ASD). In the current study we examined inter-trial coherence, a measure of phase consistency across trials, in the theta range (t-ITC: 3-6 Hz), as theta has been implicated in the processing of social and emotional stimuli in infants and adults. The sample included infants who had an older sibling with a confirmed ASD diagnosis and typically developing (TD) infants with no family history of ASD. The data were collected as part of the British Autism Study of Infant Siblings (BASIS) study. Infants between 6 and 10 months of age (M_age  = 7.34, SD_age  = 1.21) performed a visual face processing task that included faces and scrambled, "face noise", stimuli. Follow-up assessments in higher likelihood infants were completed at 24 and again at 36 months to determine diagnostic outcomes. Analysis focused on posterior t-ITC during early (0-200 ms) and late (200-500 ms) visual processing stages commonly investigated in infant studies. t-ITC over posterior scalp regions during late stage face processing was significantly higher in TD and higher likelihood infants without ASD (HRA-), indicating reduced consistency in theta-band responses in higher likelihood infants who eventually receive a diagnosis of ASD (HRA+). These findings indicate that the temporal dynamics of theta during face processing relate to ASD outcomes. Reduced consistency of oscillatory dynamics at basic levels of infant sensory processing could have downstream effects on learning and social communication. LAY SUMMARY: We examined the consistency in brain responses to faces in infants at lower or higher familial likelihood for autism. Our results show that the consistency of EEG responses was lower during face processing in higher likelihood infants who eventually received a diagnosis of autism. These findings highlight that reduced consistency in brain activity during face processing in the first year of life is related to emerging autism.

Neural Encoding of Active Multi-Sensing Enhances Perceptual Decision-Making via a Synergistic Cross-Modal Interaction

Most perceptual decisions rely on the active acquisition of evidence from the environment involving stimulation from multiple senses. However, our understanding of the neural mechanisms underlying this process is limited. Crucially, it remains elusive how different sensory representations interact in the formation of perceptual decisions. To answer these questions, we used an active sensing paradigm coupled with neuroimaging, multivariate analysis, and computational modeling to probe how the human brain processes multisensory information to make perceptual judgments. Participants of both sexes actively sensed to discriminate two texture stimuli using visual (V) or haptic (H) information or the two sensory cues together (VH). Crucially, information acquisition was under the participants' control, who could choose where to sample information from and for how long on each trial. To understand the neural underpinnings of this process, we first characterized where and when active sensory experience (movement patterns) is encoded in human brain activity (EEG) in the three sensory conditions. Then, to offer a neurocomputational account of active multisensory decision formation, we used these neural representations of active sensing to inform a drift diffusion model of decision-making behavior. This revealed a multisensory enhancement of the neural representation of active sensing, which led to faster and more accurate multisensory decisions. We then dissected the interactions between the V, H, and VH representations using a novel information-theoretic methodology. Ultimately, we identified a synergistic neural interaction between the two unisensory (V, H) representations over contralateral somatosensory and motor locations that predicted multisensory (VH) decision-making performance.SIGNIFICANCE STATEMENT In real-world settings, perceptual decisions are made during active behaviors, such as crossing the road on a rainy night, and include information from different senses (e.g., car lights, slippery ground). Critically, it remains largely unknown how sensory evidence is combined and translated into perceptual decisions in such active scenarios. Here we address this knowledge gap. First, we show that the simultaneous exploration of information across senses (multi-sensing) enhances the neural encoding of active sensing movements. Second, the neural representation of active sensing modulates the evidence available for decision; and importantly, multi-sensing yields faster evidence accumulation. Finally, we identify a cross-modal interaction in the human brain that correlates with multisensory performance, constituting a putative neural mechanism for forging active multisensory perception.

Multiple-Resampling Cross-Spectral Analysis: An Unbiased Tool for Estimating Fractal Connectivity With an Application to Neurophysiological Signals

Investigating scale-free (i.e., fractal) functional connectivity in the brain has recently attracted increasing attention. Although numerous methods have been developed to assess the fractal nature of functional coupling, these typically ignore that neurophysiological signals are assemblies of broadband, arrhythmic activities as well as oscillatory activities at characteristic frequencies such as the alpha waves. While contribution of such rhythmic components may bias estimates of fractal connectivity, they are also likely to represent neural activity and coupling emerging from distinct mechanisms. Irregular-resampling auto-spectral analysis (IRASA) was recently introduced as a tool to separate fractal and oscillatory components in the power spectrum of neurophysiological signals by statistically summarizing the power spectra obtained when resampling the original signal by several non-integer factors. Here we introduce multiple-resampling cross-spectral analysis (MRCSA) as an extension of IRASA from the univariate to the bivariate case, namely, to separate the fractal component of the cross-spectrum between two simultaneously recorded neural signals by applying the same principle. MRCSA does not only provide a theoretically unbiased estimate of the fractal cross-spectrum (and thus its spectral exponent) but also allows for computing the proportion of scale-free coupling between brain regions. As a demonstration, we apply MRCSA to human electroencephalographic recordings obtained in a word generation paradigm. We show that the cross-spectral exponent as well as the proportion of fractal coupling increases almost uniformly over the cortex during the rest-task transition, likely reflecting neural desynchronization. Our results indicate that MRCSA can be a valuable tool for scale-free connectivity studies in characterizing various cognitive states, while it also can be generalized to other applications outside the field of neuroscience.

Investigating the Potential Use of EEG for the Objective Measurement of Auditory Presence

Presence is the sense of being in a virtual environment when physically situated in another place. It is one of the key components of the overall virtual reality (VR) experience, as well as other immersive audio applications. However, there is no standardized method for measuring presence. In our previous study, we explored the possibility of using electroencephalography (EEG) to measure presence by using questionnaires as a reference. It was found that an increase in the subjective presence level was correlated with an increase in the theta/beta ratio (an index derived from EEG). In the present study, we re-analyzed the original data and found that the peak alpha frequency (PAF), another EEG index, may also have the potential to reflect the change in the subjective presence level. Specifically, an increase in the subjective presence level was found to be correlated with a decrease in PAF. Together with our previous study, these results indicate the potential use of EEG for the objective measurement of presence in the future.

Ensemble Deep Neural Network for Automatic Classification of EEG Independent Components

OBJECTIVE

Independent component analysis (ICA) is commonly used to remove noisy artifacts from multi-channel scalp electroencephalogram (EEG) signals. ICA decomposes EEG into different independent components (ICs) and then, experts remove the noisy ones. This process is highly time-consuming and experts are not always available. To surpass this drawback, research is going on to develop models to automatically conduct IC classification. Current state-of-the-art models use power spectrum densities (PSDs) and topoplots to classify ICs. The performance of these methods may be limited by disregarding the IC time-series that would contribute to fully simulate the visual inspection performed by experts.

METHODS

We present a novel ensemble deep neural network that combines time-series, PSDs, and topoplots to classify ICs. Moreover, we study the ability to use our model in transfer learning approaches.

RESULTS

Experimental results showed that using time-series improves IC classification. Results also indicated that transfer learning obtained higher performance than simply training a new model from scratch.

CONCLUSION

Researchers should develop IC classifiers using the three sources of information. Moreover, transfer learning approaches should be considered when producing new deep learning models.

SIGNIFICANCE

This work improves IC classification, enhancing the automatic removal of EEG artifacts. Additionally, since labelled ICs are generally not publicly available, the possibility of using our model in transfer learning studies may motivate other researchers to develop their own classifiers.

Deciding on Optical Illusions: Reduced Alpha Power in Body Dysmorphic Disorder

BACKGROUND

Body dysmorphic disorder (BDD) is a psychiatric disorder characterized by excessive preoccupation with imagined defects in appearance. Optical illusions induce illusory effects that distort the presented stimulus, thus leading to ambiguous percepts. Using electroencephalography (EEG), we investigated whether BDD is related to differentiated perception during illusory percepts.

METHODS

A total of 18 BDD patients and 18 controls were presented with 39 optical illusions together with a statement testing whether or not they perceived the illusion. After a delay period, they were prompted to answer whether the statement was right/wrong and their degree of confidence in their answer. We investigated differences of BDD patients on task performance and self-reported confidence and analyzed the brain oscillations during decision-making using nonparametric cluster statistics.

RESULTS

Behaviorally, the BDD group exhibited reduced confidence when responding incorrectly, potentially attributed to higher levels of doubt. Electrophysiologically, the BDD group showed significantly reduced alpha power at the fronto-central and parietal scalp areas, suggesting impaired allocation of attention. Interestingly, the lower the alpha power of the identified cluster, the higher the BDD severity, as assessed by BDD psychometrics.

CONCLUSIONS

Results evidenced that alpha power during illusory processing might serve as a quantitative EEG biomarker of BDD, potentially associated with reduced inhibition of task-irrelevant areas.

Comparing impacts of meditation training in focused attention, open monitoring, and mindfulness-based cognitive therapy on emotion reactivity and regulation: Neural and subjective evidence from a dismantling study

Commonly conducted mindfulness‐based trainings such as Mindfulness‐based Stress Reduction (MBSR) and Mindfulness‐based Cognitive Therapy (MBCT) highlight training in two key forms of meditation: focused attention (FA) and open monitoring (OM). Largely unknown is what each of these mindfulness practices contributes to emotional and other important training outcomes. This dismantling trial compared the effects of structurally equivalent trainings in MBCT, FA, and OM on neural and subjective markers of emotional reactivity and regulation among community adults, with the aim to better understand which forms of training represent active ingredients in mindfulness trainings. Participants with varying levels of depressive symptoms were randomized to one of the three trainings. Before and after each 8‐week training, N = 89 participants completed a modified version of the Emotional Reactivity and Regulation Task while electroencephalographic (EEG) and self‐reported emotional responses to negative, positive, and neutral photographic images were collected. Examination of EEG‐based frontal alpha band asymmetry during passive viewing (reactivity) and active regulation phases of the task showed that FA and MBCT trainings produced significant leftward hemispheric shifts in frontal alpha asymmetry, commonly associated with a shift toward approach‐based positive affect. Self‐reported emotional responses to negative images corroborated these results, suggesting salutary changes in both emotional reactivity and regulation. OM training had limited beneficial effects, restricted to the subjective outcomes. The findings suggest that MBCT may derive its greatest benefit from training in FA rather than OM. Discussion highlights the potential value of FA training for emotional health.

In the first report comparing emotion‐relevant impacts of focused attention meditation (FA), open awareness meditation (OM), and Mindfulness‐based Cognitive Therapy (MBCT) among those with depressive symptoms, we show that FA and MBCT produced leftward hemispheric shifts in frontal alpha asymmetry, consistent with approach‐based positive affect, during an emotion reactivity and regulation task. Self‐reported emotional responses to negative images corroborated these results. The findings highlight the potential value of FA and MBCT training for emotional health.

On second thought … the influence of a second stage in the ultimatum game on decision behavior, electro-cortical correlates and their trait interrelation

Previous EEG research only investigated one stage ultimatum games (UGs). We investigated the influence of a second bargaining stage in an UG concerning behavioral responses, electro-cortical correlates and their moderations by the traits altruism, anger, anxiety, and greed in 92 participants. We found that an additional stage led to more rejection in the 2-stage UG (2SUG) and that increasing offers in the second stage compared to the first stage led to more acceptance. The FRN during a trial was linked to expectance evaluation concerning the fairness of the offers, while midfrontal theta was a marker for the needed cognitive control to overcome the respective default behavioral pattern. The FRN responses to unfair offers were more negative for either low or high altruism in the UG, while high trait anxiety led to more negative FRN responses in the first stage of 2SUG, indicating higher sensitivity to unfairness. Accordingly, the mean FRN response, representing the trait-like general electrocortical reactivity to unfairness, predicted rejection in the first stage of 2SUG. Additionally, we found that high trait anger led to more rejections for unfair offer in 2SUG in general, while trait altruism led to more rejection of unimproving unfair offers in the second stage of 2SUG. In contrast, trait anxiety led to more acceptance in the second stage of 2SUG, while trait greed even led to more acceptance if the offer was worse than in the stage before. These findings suggest, that 2SUG creates a trait activation situation compared to the UG.

Driver drowsiness estimation using EEG signals with a dynamical encoder-decoder modeling framework

Drowsiness is a leading cause of accidents on the road as it negatively affects the driver’s ability to safely operate a vehicle. Neural activity recorded by EEG electrodes is a widely used physiological correlate of driver drowsiness. This paper presents a novel dynamical modeling solution to estimate the instantaneous level of the driver drowsiness using EEG signals, where the PERcentage of eyelid CLOSure (PERCLOS) is employed as the ground truth of driver drowsiness. Applying our proposed modeling framework, we find neural features present in EEG data that encode PERCLOS. In the decoding phase, we use a Bayesian filtering solution to estimate the PERCLOS level over time. A data set that comprises 18 driving tests, conducted by 13 drivers, has been used to investigate the performance of the proposed framework. The modeling performance in estimation of PERCLOS provides robust and repeatable results in tests with manual and automated driving modes by an average RMSE of 0.117 (at a PERCLOS range of 0 to 1) and average High Probability Density percentage of 62.5%. We further hypothesized that there are biomarkers that encode the PERCLOS across different driving tests and participants. Using this solution, we identified possible biomarkers such as Theta and Delta powers. Results show that about 73% and 66% of the Theta and Delta powers which are selected as biomarkers are increasing as PERCLOS grows during the driving test. We argue that the proposed method is a robust and reliable solution to estimate drowsiness in real-time which opens the door in utilizing EEG-based measures in driver drowsiness detection systems.

Robust learning from corrupted EEG with dynamic spatial filtering

Building machine learning models using EEG recorded outside of the laboratory setting requires methods robust to noisy data and randomly missing channels. This need is particularly great when working with sparse EEG montages (1-6 channels), often encountered in consumer-grade or mobile EEG devices. Neither classical machine learning models nor deep neural networks trained end-to-end on EEG are typically designed or tested for robustness to corruption, and especially to randomly missing channels. While some studies have proposed strategies for using data with missing channels, these approaches are not practical when sparse montages are used and computing power is limited (e.g., wearables, cell phones). To tackle this problem, we propose dynamic spatial filtering (DSF), a multi-head attention module that can be plugged in before the first layer of a neural network to handle missing EEG channels by learning to focus on good channels and to ignore bad ones. We tested DSF on public EEG data encompassing ∼4,000 recordings with simulated channel corruption and on a private dataset of ∼100 at-home recordings of mobile EEG with natural corruption. Our proposed approach achieves the same performance as baseline models when no noise is applied, but outperforms baselines by as much as 29.4% accuracy when significant channel corruption is present. Moreover, DSF outputs are interpretable, making it possible to monitor the effective channel importance in real-time. This approach has the potential to enable the analysis of EEG in challenging settings where channel corruption hampers the reading of brain signals.

Analytic consistency and neural correlates of peak alpha frequency in the study of pain

BACKGROUND

Several studies have found evidence of reduced resting-state peak alpha frequency (PAF) in populations with pain. However, the stability of PAF from different analytic pipelines used to study pain has not been determined and underlying neural correlates of PAF have not been validated in humans.

NEW METHOD

For the first time we compare analytic pipelines and the relationship of PAF to activity in the whole brain and thalamus, a hypothesized generator of PAF. We collected resting-state functional magnetic resonance imaging (rs-fMRI) data and subsequently 64 channel resting-state electroencephalographic (EEG) from 47 healthy men, controls from an ongoing study of chronic prostatitis (a pain condition affecting men). We identified important variations in EEG processing for PAF from a review of 17 papers investigating the relationship between pain and PAF. We tested three progressively complex pre-processing pipelines and varied four postprocessing variables (epoch length, alpha band, calculation method, and region-of-interest [ROI]) that were inconsistent across the literature.

RESULTS

We found a single principal component, well-represented by the average PAF across all electrodes (grand-average PAF), explained > 95% of the variance across participants. We also found the grand-average PAF was highly correlated among the pre-processing pipelines and primarily impacted by calculation method and ROI. Across methods, interindividual differences in PAF were correlated with rs-fMRI-estimated activity in the thalamus, insula, cingulate, and sensory cortices.

CONCLUSIONS

These results suggest PAF is a relatively stable marker with respect to common pre and post-processing methods used in pain research and reflects interindividual differences in thalamic and salience network function.

Natural Infant-Directed Speech Facilitates Neural Tracking of Prosody

Infants prefer to be addressed with infant-directed speech (IDS). IDS benefits language acquisition through amplified low-frequency amplitude modulations. It has been reported that this amplification increases electrophysiological tracking of IDS compared to adult-directed speech (ADS). It is still unknown which particular frequency band triggers this effect. Here, we compare tracking at the rates of syllables and prosodic stress, which are both critical to word segmentation and recognition. In mother-infant dyads (n=30), mothers described novel objects to their 9-month-olds while infants' EEG was recorded. For IDS, mothers were instructed to speak to their children as they typically do, while for ADS, mothers described the objects as if speaking with an adult. Phonetic analyses confirmed that pitch features were more prototypically infant-directed in the IDS-condition compared to the ADS-condition. Neural tracking of speech was assessed by speech-brain coherence, which measures the synchronization between speech envelope and EEG. Results revealed significant speech-brain coherence at both syllabic and prosodic stress rates, indicating that infants track speech in IDS and ADS at both rates. We found significantly higher speech-brain coherence for IDS compared to ADS in the prosodic stress rate but not the syllabic rate. This indicates that the IDS benefit arises primarily from enhanced prosodic stress. Thus, neural tracking is sensitive to parents' speech adaptations during natural interactions, possibly facilitating higher-level inferential processes such as word segmentation from continuous speech.

Handling EEG artifacts and searching individually optimal experimental parameter in real time: a system development and demonstration

Objective.Neuroadaptive paradigms that systematically assess event-related potential (ERP) features across many different experimental parameters have the potential to improve the generalizability of ERP findings and may help to accelerate ERP-based biomarker discovery by identifying the exact experimental conditions for which ERPs differ most for a certain clinical population. Obtaining robust and reliable ERPs online is a prerequisite for ERP-based neuroadaptive research. One of the key steps involved is to correctly isolate electroencephalography artifacts in real time because they contribute a large amount of variance that, if not removed, will greatly distort the ERP obtained. Another key factor of concern is the computational cost of the online artifact handling method. This work aims to develop and validate a cost-efficient system to support ERP-based neuroadaptive research.Approach.We developed a simple online artifact handling method, single trial PCA-based artifact removal (SPA), based on variance distribution dichotomies to distinguish between artifacts and neural activity. We then applied this method in an ERP-based neuroadaptive paradigm in which Bayesian optimization was used to search individually optimal inter-stimulus-interval (ISI) that generates ERP with the highest signal-to-noise ratio.Main results.SPA was compared to other offline and online algorithms. The results showed that SPA exhibited good performance in both computational efficiency and preservation of ERP pattern. Based on SPA, the Bayesian optimization procedure was able to quickly find individually optimal ISI.Significance.The current work presents a simple yet highly cost-efficient method that has been validated in its ability to extract ERP, preserve ERP effects, and better support ERP-based neuroadaptive paradigm.

Dissociating Value-Based Neurocomputation from Subsequent Selection-Related Activations in Human Decision-Making

Human decision-making requires the brain to fulfill neural computation of benefit and risk and therewith a selection between options. It remains unclear how value-based neural computation and subsequent brain activity evolve to achieve a final decision and which process is modulated by irrational factors. We adopted a sequential risk-taking task that asked participants to successively decide whether to open a box with potential reward/punishment in an eight-box trial, or not to open. With time-resolved multivariate pattern analyses, we decoded electroencephalography and magnetoencephalography responses to two successive low- and high-risk boxes before open-box action. Referencing the specificity of decoding-accuracy peak to a first-stage processing completion, we set it as the demarcation and dissociated the neural time course of decision-making into valuation and selection stages. The behavioral hierarchical drift diffusion modeling confirmed different information processing in two stages, that is, the valuation stage was related to the drift rate of evidence accumulation, while the selection stage was related to the nondecision time spent in response-producing. We further observed that medial orbitofrontal cortex participated in the valuation stage, while superior frontal gyrus engaged in the selection stage of irrational open-box decisions. Afterward, we revealed that irrational factors influenced decision-making through the selection stage rather than the valuation stage.

mGluR5 binding changes during a mismatch negativity task in a multimodal protocol with [11C]ABP688 PET/MR-EEG

Currently, the metabotropic glutamate receptor 5 (mGluR5) is the subject of several lines of research in the context of neurology and is of high interest as a target for positron-emission tomography (PET). Here, we assessed the feasibility of using [¹¹C]ABP688, a specific antagonist radiotracer for an allosteric site on the mGluR5, to evaluate changes in glutamatergic neurotransmission through a mismatch-negativity (MMN) task as a part of a simultaneous and synchronized multimodal PET/MR-EEG study. We analyzed the effect of MMN by comparing the changes in nondisplaceable binding potential (BP_ND) prior to (baseline) and during the task in 17 healthy subjects by applying a bolus/infusion protocol. Anatomical and functional regions were analyzed. A small change in BP_ND was observed in anatomical regions (posterior cingulate cortex and thalamus) and in a functional network (precuneus) after the start of the task. The effect size was quantified using Kendall's W value and was 0.3. The motor cortex was used as a control region for the task and did not show any significant BP_ND changes. There was a significant ΔBP_ND between acquisition conditions. On average, the reductions in binding across the regions were - 8.6 ± 3.2% in anatomical and - 6.4 ± 0.5% in the functional network (p ≤ 0.001). Correlations between ΔBP_ND and EEG latency for both anatomical (p = 0.008) and functional (p = 0.022) regions were found. Exploratory analyses suggest that the MMN task played a role in the glutamatergic neurotransmission, and mGluR5 may be indirectly modulated by these changes.

Unsupervised Clustering of Individuals Sharing Selective Attentional Focus Using Physiological Synchrony

Research on brain signals as indicators of a certain attentional state is moving from laboratory environments to everyday settings. Uncovering the attentional focus of individuals in such settings is challenging because there is usually limited information about real-world events, as well as a lack of data from the real-world context at hand that is correctly labeled with respect to individuals' attentional state. In most approaches, such data is needed to train attention monitoring models. We here investigate whether unsupervised clustering can be combined with physiological synchrony in the electroencephalogram (EEG), electrodermal activity (EDA), and heart rate to automatically identify groups of individuals sharing attentional focus without using knowledge of the sensory stimuli or attentional focus of any of the individuals. We used data from an experiment in which 26 participants listened to an audiobook interspersed with emotional sounds and beeps. Thirteen participants were instructed to focus on the narrative of the audiobook and 13 participants were instructed to focus on the interspersed emotional sounds and beeps. We used a broad range of commonly applied dimensionality reduction ordination techniques-further referred to as mappings-in combination with unsupervised clustering algorithms to identify the two groups of individuals sharing attentional focus based on physiological synchrony. Analyses were performed using the three modalities EEG, EDA, and heart rate separately, and using all possible combinations of these modalities. The best unimodal results were obtained when applying clustering algorithms on physiological synchrony data in EEG, yielding a maximum clustering accuracy of 85%. Even though the use of EDA or heart rate by itself did not lead to accuracies significantly higher than chance level, combining EEG with these measures in a multimodal approach generally resulted in higher classification accuracies than when using only EEG. Additionally, classification results of multimodal data were found to be more consistent across algorithms than unimodal data, making algorithm choice less important. Our finding that unsupervised classification into attentional groups is possible is important to support studies on attentional engagement in everyday settings.

Automatic Clustering of Students by Level of Situational Interest Based on Their EEG Features

The usage of physiological measures in detecting student’s interest is often said to improve the weakness of psychological measures by decreasing the susceptibility of subjective bias. The existing methods, especially EEG-based, use classification, which needs a predefined class and complex computational to analyze. However, the predefined classes are mostly based on subjective measurement (e.g., questionnaires). This work proposed a new scheme to automatically cluster the students by the level of situational interest (SI) during learning-based lessons on their electroencephalography (EEG) features. The formed clusters are then used as ground truth for classification purposes. A simultaneous recording of EEG was performed on 30 students while attending a lecture in a real classroom. The frontal mean delta and alpha power as well as the frontal alpha asymmetry metric served as the input for k-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithms. Using the collected data, 29 models were trained within nine domain classifiers, then the classifiers with the highest performance were selected. We validated all the models through 10-fold cross-validation. The high SI group was clustered to students having lower frontal mean delta and alpha power together with negative Frontal Alpha Asymmetry (FAA). It was found that k-means performed better by giving the maximum performance assessment parameters of 100% in clustering the students into three groups: high SI, medium SI and low SI. The findings show that the DBSCAN had reduced the performance to cluster dataset without the outlier. The findings of this study give a promising option to cluster the students by their SI level, as well as address the drawbacks of the existing methods, which use subjective measures.

Home-Based Therapy After Stroke Using the Hand Spring Operated Movement Enhancer (HandSOME II)

We have developed a passive and lightweight wearable hand exoskeleton (HandSOME II) that improves range of motion and functional task practice in laboratory testing. For this longitudinal study, we recruited 15 individuals with chronic stroke and asked them to use the device at home for 1.5 h per weekday for 8 weeks. Subjects visited the clinic once per week to report progress and troubleshoot problems. Subjects were then given the HandSOME II for the next 3 months, and asked to continue to use it, but without any scheduled contact with the project team. Clinical evaluations and biomechanical testing was performed before and after the 8 week intervention and at the 3 month followup. EEG measures were taken before and after the 8 weeks of training to examine any recovery associated brain reorganization. Ten subjects completed the study. After 8 weeks of training, functional ability (Action Research Arm Test), flexor tone (Modified Ashworth Test), and real world use of the impaired limb (Motor Activity Log) improved significantly (p < 0.05). Gains in real world use were retained at the 3-month followup (p = 0.005). At both post-training and followup time points, biomechanical testing found significant gains in finger ROM and hand displacement in a reaching task (p < 0.05). Baseline functional connectivity correlated with gains in motor function, while changes in EEG functional connectivity paralleled changes in motor recovery. HandSOME II is a low-cost, home-based intervention that elicits brain plasticity and can improve functional motor outcomes in the chronic stroke population.

WeBrain: A web-based brainformatics platform of computational ecosystem for EEG big data analysis

The current evolution of 'cloud neuroscience' leads to more efforts with the large-scale EEG applications, by using EEG pipelines to handle the rapidly accumulating EEG data. However, there are a few specific cloud platforms that seek to address the cloud computational challenges of EEG big data analysis to benefit the EEG community. In response to the challenges, a WeBrain cloud platform (https://webrain.uestc.edu.cn/) is designed as a web-based brainformatics platform and computational ecosystem to enable large-scale EEG data storage, exploration and analysis using cloud high-performance computing (HPC) facilities. WeBrain connects researchers from different fields to EEG and multimodal tools that have become the norm in the field and the cloud processing power required to handle those large EEG datasets. This platform provides an easy-to-use system for novice users (even no computer programming skills) and provides satisfactory maintainability, sustainability and flexibility for IT administrators and tool developers. A range of resources are also available on https://webrain.uestc.edu.cn/, including documents, manuals, example datasets related to WeBrain, and collected links to open EEG datasets and tools. It is not necessary for users or administrators to install any software or system, and all that is needed is a modern web browser, which reduces the technical expertise required to use or manage WeBrain. The WeBrain platform is sponsored and driven by the China-Canada-Cuba international brain cooperation project (CCC-Axis, http://ccc-axis.org/), and we hope that WeBrain will be a promising cloud brainformatics platform for exploring brain information in large-scale EEG applications in the EEG community.

It's in the Timing: Reduced Temporal Precision in Neural Activity of Schizophrenia

Studies of perception and cognition in schizophrenia (SCZ) show neuronal background noise (ongoing activity) to intermittently overwhelm the processing of external stimuli. This increased noise, relative to the activity evoked by the stimulus, results in temporal imprecision and higher variability of behavioral responses. What, however, are the neural correlates of temporal imprecision in SCZ behavior? We first report a decrease in electroencephalography signal-to-noise ratio (SNR) in two SCZ datasets and tasks in the broadband (1-80 Hz), theta (4-8 Hz), and alpha (8-13 Hz) bands. SCZ participants also show lower inter-trial phase coherence (ITPC)-consistency over trials in the phase of the signal-in theta. From these ITPC results, we varied phase offsets in a computational simulation, which illustrated phase-based temporal desynchronization. This modeling also provided a necessary link to our results and showed decreased neural synchrony in SCZ in both datasets and tasks when compared with healthy controls. Finally, we showed that reduced SNR and ITPC are related and showed a relationship to temporal precision on the behavioral level, namely reaction times. In conclusion, we demonstrate how temporal imprecision in SCZ neural activity-reduced relative signal strength and phase coherence-mediates temporal imprecision on the behavioral level.

The Influence of Mental Imagery Expertise of Pen and Paper Players versus Computer Gamers upon Performance and Electrocortical Correlates in a Difficult Mental Rotation Task

We investigated the influence of mental imagery expertise in 15 pen and paper role-players as an expert group compared to the gender-matched control group of computer role-players in the difficult Vandenberg and Kuse mental rotation task. In this task, the participants have to decide which two of four rotated figures match the target figure. The dependent measures were performance speed and accuracy. In our exploratory investigation, we further examined midline frontal theta band activation, parietal alpha band activation, and parietal alpha band asymmetry in EEG as indicator for the chosen rotation strategy. Additionally, we explored the gender influence on performance and EEG activation, although a very small female sample section was given. The expected gender difference concerning performance accuracy was negated by expertise in pen and paper role-playing women, while the gender-specific difference in performance speed was preserved. Moreover, gender differences concerning electro-cortical measures revealed differences in rotation strategy, with women using top-down strategies compared to men, who were using top-down strategies and active inhibition of associative cortical areas. These strategy uses were further moderated by expertise, with higher expertise leading to more pronounced activation patters, especially during successful performance. However, due to the very limited sample size, the findings of this explorative study have to be interpreted cautiously.

A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography

Independent Component Analysis (ICA) is a conventional approach to exclude non-brain signals such as eye movements and muscle artifacts from electroencephalography (EEG). A rejection of independent components (ICs) is usually performed in semiautomatic mode and requires experts' involvement. As also revealed by our study, experts' opinions about the nature of a component often disagree, highlighting the need to develop a robust and sustainable automatic system for EEG ICs classification. The current article presents a toolbox and crowdsourcing platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE) available via link http://alice.adase.org/. The ALICE toolbox aims to build a sustainable algorithm to remove artifacts and find specific patterns in EEG signals using ICA decomposition based on accumulated experts' knowledge. The difference from previous toolboxes is that the ALICE project will accumulate different benchmarks based on crowdsourced visual labeling of ICs collected from publicly available and in-house EEG recordings. The choice of labeling is based on the estimation of IC time-series, IC amplitude topography, and spectral power distribution. The platform allows supervised machine learning (ML) model training and re-training on available data subsamples for better performance in specific tasks (i.e., movement artifact detection in healthy or autistic children). Also, current research implements the novel strategy for consentient labeling of ICs by several experts. The provided baseline model could detect noisy IC and components related to the functional brain oscillations such as alpha and mu rhythm. The ALICE project implies the creation and constant replenishment of the IC database, which will improve ML algorithms for automatic labeling and extraction of non-brain signals from EEG. The toolbox and current dataset are open-source and freely available to the researcher community.

Temporally resolved neural dynamics underlying handwriting

How do the temporal dynamics of neural activity encode highly coordinated visual-motor behaviour? To capture the millisecond-resolved neural activations associated with fine visual-motor skills, we devised a co-registration system to simultaneously record electroencephalogram and handwriting kinematics while participants were performing four handwriting tasks (writing in Chinese/English scripts with their dominant/non-dominant hand). The neural activation associated with each stroke was clearly identified with a well-structured and reliable pattern. The functional significance of this pattern was validated by its significant associations with language, hand and the cognitive stages and kinematics of handwriting. Furthermore, the handwriting rhythmicity was found to be synchronised to the brain's ongoing theta oscillation, and the synchronisation was associated with the factor of language and hand. These major findings imply an implication between motor skill formation and the interplay between the rhythms in the brain and the peripheral systems.

Extending the "resting state hypothesis of depression" - dynamics and topography of abnormal rest-task modulation

Major depressive disorder (MDD) is characterized by changes in both rest and task states as manifested in temporal dynamics (EEG) and spatial patterns (fMRI). Are rest and task changes related to each other? Extending the "Resting state hypothesis of depression" (RSHD) (Northoff et al., 2011), we, using multimodal imaging, take a tripartite approach: (i) we conduct a review of EEG studies in MDD combining both rest and task states; (ii) we present our own EEG data in MDD on brain dynamics, i.e., intrinsic neural timescales as measured by the autocorrelation window (ACW); and (iii) we review fMRI studies in MDD to probe whether different regions exhibit different rest-task modulation. Review of EEG data shows reduced rest-task change in MDD in different measures of temporal dynamics like peak frequency (and others). Notably, our own EEG data show decreased rest-task change as measured by ACW in frontal electrodes of MDD. The fMRI data reveal that different regions exhibit different rest-task relationships (normal rest-abnormal task, abnormal rest-normal task, abnormal rest-abnormal task) in MDD. Together, we demonstrate altered spatiotemporal dynamics of rest-task modulation in MDD; this further supports and extends the key role of the spontaneous activity in MDD as proposed by the RSHD.

Sensory tricks modulate corticocortical and corticomuscular connectivity in cervical dystonia

OBJECTIVE

To examine interactions between cortical areas and between cortical areas and muscles during sensory tricks in cervical dystonia (CD).

METHODS

Thirteen CD patients and thirteen age-matched healthy controls performed forewarned reaction time tasks, sensory tricks, and two tasks replicating aspects of the tricks (moving necks/arms). Control subjects mimicked sensory tricks. Corticocortical and corticomuscular coherence values were calculated from surface electrodes placed over motor, premotor, and sensory cortical areas and dystonic muscles.

RESULTS

During initial preparation (after the warning stimulus), the only between-task difference was found in the γ-band corticocortical coherence (higher during tricks than during voluntary neck movements). With movements (before/after the imperative stimulus), the γ-band coherence of CD patients significantly increased during tricks but decreased during voluntary movements, while opposite trends were observed in healthy subjects. Additionally, the α- and β-band coherence decreased in healthy subjects during movements. Between the two patient subgroups (typical vs. forcible tricks), only those with typical tricks showed significant decrease in corticomuscular coherence during tricks.

CONCLUSIONS

Observed changes in the corticocortical coherence suggest that sensory tricks improve cortical function, which reduces corticomuscular connectivity and the dystonia.

SIGNIFICANCE

We demonstrated that sensory tricks fundamentally affect sensorimotor integration in CD, both in movement preparation and execution.

EEG Electrode Selection for a Two-Class Motor Imagery Task in a BCI Using fNIRS Prior Data

This study investigated the possibility of using functional near infrared spectroscopy (fNIRS) during right- and left-hand motor imagery tasks to select an optimum set of electroencephalography (EEG) electrodes for a brain computer interface. fNIRS has better spatial resolution allowing areas of brain activity to more readily be identified. The ReliefF algorithm was used to identify the most reliable fNIRS channels. Then, EEG electrodes adjacent to those channels were selected for classification. This study used three different classifiers of linear and quadratic discriminant analyses, and support vector machine to examine the proposed method.Clinical Relevance- Reducing the number of sensors in a BCI makes the system more usable for patients with severe disabilities.

Treating Electrical and Biopotential Artifacts in an EEG Pilot Study Experiment

With the increase in life expectancy, as well as in the performance and complexity of healthcare systems, the need for fast and accurate information has also grown. EEG devices have become more accessible and necessary in clinical practice. In daily activity, artifacts are ubiquitous in EEG signals. They arise from: environmental, experimental and physiological factors, degrade signal quality and render the affected part of the signal useless. This paper proposes an artifact cleaning pipeline including filters and algorithms to streamline the analysis process. Moreover, to better characterize and discriminate artifacts from useful EEG data, additional physiological signals and video data are used, which are correlated with subject's behavior. We quantify the performance reached by Peak Signal-to-Noise Ratio and clinical visual inspection. The entire research and data collection took place in the laboratories of XPERI Corporation.Clinical Relevance-Since the occurrence of artifacts cannot be controlled, it is essential to have a precise process of recognition, identification and elimination of noise. Therefore, it is important to distinguish EEG artifacts from abnormal activity in order to minimize the chance of EEG misinterpretation, that can lead to false diagnosis, especially regarding the study of epileptiform activities or other neurologic or psychiatric disorders (e.g. degenerative diseases, dementia, depression, sleep disorders, Alzheimer's disease, schizophrenia, etc.).