Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects

The impact of social comparison on self-deception: An event-related potentials study

Self-deception refers to an individual holding inflated beliefs about their abilities, and it plays a crucial role in human behavior and decision-making. The present study employed event-related potentials (ERPs) technique to explore the neural responses to the impacts of social comparison direction and comparison gap on self-deceptive behavior. They were instructed to predict their performance in the forward-looking paradigm. Behavioral responses and neural reactions during the decision-making process were documented. The behavioral results indicated that, in contrast to the downward comparison condition, participants engaged in upward comparison exhibited more occurrences of self-deception. However, within the context of upward comparison, participants demonstrated a higher frequency of self-deception in the large gap condition compared with the small gap condition. The ERP results showed that induced self-deception under conditions with a large comparative gap between participants and their paired counterparts stimulated larger P300 and smaller N400 amplitude than under conditions with a small gap. However, when participants were in the upward comparison situation, the late positive potential (LPP) amplitude induced by self-deception behavior in the condition of a large comparison gap between participants and paired opponents was larger than that in the condition of a small comparison gap. These results indicated that individuals in the large gap group feel strong unfairness and negative emotions. More importantly, the self-deception induced by the large gap group in the upward comparison situation used fewer cognitive resources than the small gap condition, whereas the individuals in the downward comparison situation did not show the difference in cognitive resources.

Temporal context modulates cross-modality time discrimination: Electrophysiological evidence for supramodal temporal representation

Although the peripheral nervous system lacks a dedicated receptor, the brain processes temporal information through different sensory channels. A critical question is whether temporal information from different sensory modalities at different times forms modality-specific representations or is integrated into a common representation in a supramodal manner. Behavioral studies on temporal memory mixing and the central tendency effect have provided evidence for supramodal temporal representations. We aimed to provide electrophysiological evidence for this proposal by employing a cross-modality time discrimination task combined with electroencephalogram (EEG) recordings. The task maintained a fixed auditory standard duration, whereas the visual comparison duration was randomly selected from the short and long ranges, creating two different audio-visual temporal contexts. The behavioral results showed that the point of subjective equality (PSE) in the short context was significantly lower than that in the long context. The EEG results revealed that the amplitude of the contingent negative variation (CNV) in the short context was significantly higher (more negative) than in the long context in the early stage, while it was lower (more positive) in the later stage. These results suggest that the audiovisual temporal context is integrated with the auditory standard duration to generate a subjective time criterion. Compared with the long context, the subjective time criterion in the short context was shorter, resulting in earlier decision-making and a preceding decrease in CNV. Our study provides electrophysiological evidence that temporal information from different modalities inputted into the brain at different times can form a supramodal temporal representation.

Language experience shapes predictive coding of rhythmic sound sequences

Perceptual systems heavily rely on prior knowledge and predictions to make sense of the environment. Predictions can originate from multiple sources of information, including contextual short-term priors, based on isolated temporal situations, and context-independent long-term priors, arising from extended exposure to statistical regularities. While the effects of short-term predictions on auditory perception have been well-documented, how long-term predictions shape early auditory processing is poorly understood. To address this, we recorded magnetoencephalography data from native speakers of two languages with different word orders (Spanish: functor-initial vs Basque: functor-final) listening to simple sequences of binary sounds alternating in duration with occasional omissions. We hypothesized that, together with contextual transition probabilities, the auditory system uses the characteristic prosodic cues (duration) associated with the native language's word order as an internal model to generate long-term predictions about incoming non-linguistic sounds. Consistent with our hypothesis, we found that the amplitude of the mismatch negativity elicited by sound omissions varied orthogonally depending on the speaker's linguistic background and was most pronounced in the left auditory cortex. Importantly, listening to binary sounds alternating in pitch instead of duration did not yield group differences, confirming that the above results were driven by the hypothesized long-term 'duration' prior. These findings show that experience with a given language can shape a fundamental aspect of human perception - the neural processing of rhythmic sounds - and provides direct evidence for a long-term predictive coding system in the auditory cortex that uses auditory schemes learned over a lifetime to process incoming sound sequences.

The promoting effect of the absence of second-party's punishment power on third-party punishment in maintaining social fairness norms: An EEG hyper-scanning study

Third-party punishment (TPP) plays an irreplaceable role in maintaining social fairness. Punishment power is a significant area of study within economic games. However, the impact of whether or not the second-party possesses punishment power on TPP remains unexplored. The present study utilizes the high temporal resolution of EEG and time-frequency analysis, intra-barin functional connectivity analysis, inter-brain synchronization (IBS) analysis, and granger causality analysis(GCA) to comprehensively explore the neural mechanism of TPP from the perspective of third-party individual's decision-making and IBS in the real-time social interaction. Time-frequency results found that, the absence of the punishment power activated more theta-band and alpha-band power compare to when second-party has punishment power. When second-party has no punishment power, functional connection results observed stronger functional connectivity in theta band for medium unfair offers between rTPJ and PFC. Dual-brain analysis revealed that when the second-party has no punishment power, there is a significantly higher IBS in the alpha band between the frontal and frontal-central lobes of the second-party and the parietal and parietal occipital lobes of the third-party. GCA results further showed that the direction of IBS from third-party to second-party was significantly stronger than from second-party to third-party. This study demonstrates that the absence of the second-party's punishment power promote TPP, and similar cognitive process of thinking on how to maintain social fairness enhances IBS. The current study emphasizes the influence of punishment power on TPP, broadens the research perspective and contributes crucial insights into maintain social fairness.

Multimodal analysis of cortical activation in young male adults with internet gaming disorder: A resting state EEG-fNIRS study

BACKGROUND

Internet gaming disorder (IGD) has become increasingly prevalent worldwide and is recognized as a significant public health concern because of its negative consequences on individuals mental and physical health, social relationships, academic performance and overall well-being. While research on IGD has gained significant momentum in the past decade, the neural substrates underlying this disorder remains unclear. This study aims to investigate resting-state cortical activation in male subjects with IGD using a concurrent functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) hybrid system.

METHODS

Twenty-two male individuals with IGD (18-23 years old) and twenty-two male healthy, age-matched healthy controls were included in the study. Mean oxygenation of the prefrontal cortex (PFC) and whole head neural activity were measured using fNIRS and EEG respectively, during eyes-open and eyes-closed conditions at the resting state. EEG signals were decomposed into distinct frequency sub-bands with wavelet transform, followed by the analysis of the power spectral density within each band. Mean oxygenation of PFC is measured using a multichannel fNIRS system.

RESULTS

Results revealed that the individuals with IGD had significantly higher beta power in the frontal region compared to the control group. Individuals with IGD showed significantly increased PFC oxygenation compared to healthy controls. Additionally, both frontal beta power and PFC oxygenation were significantly correlated with IGD severity. However, there were no significant correlations observed between oxygenation and beta powers.

CONCLUSION

This study is the first to examine resting-state cortical activation using multimodal EEG-fNIRS system in young adults with IGD. Moreover, it provides an important contribution to the understanding of the underlying neural mechanisms of IGD and offer new insights for the diagnosis and intervention of the disorder using multimodal EEG-fNIRS system. Further studies should aim to replicate the findings of this study using a larger and more culturally diverse sample to support the neurophysiological basis of IGD.

EEG subject-dependent neurofeedback training selectively impairs declarative memories consolidation process

The process of stabilization and storage of memories, known as consolidation, can be modulated by different interventions. Research has shown that self-regulation of brain activity through Neurofeedback (NFB) during the consolidation phase significantly impacts memory stabilization. While some studies have successfully modulated the consolidation phase using traditional EEG-based Neurofeedback (NFB) that focuses on general parameters, such as training a specific frequency band at particular electrodes, they often overlook the unique and complex neurodynamics that underlie each memory content in different individuals, potentially limiting the selective modulation of memories. The main objective of this study is to investigate the effects of a Subject-Dependent NFB (SD-NFB), based on individual models created from the brain activity of each participant, on long-term declarative memories. Participants underwent an experimental protocol involving three sessions. In the first session, they learned images of faces and houses while their brain activity was recorded. This EEG data was used to create individualized models to identify brain patterns related to learning these images. Participants were then divided into three groups, with one group receiving SD-NFB to enhance brain activity linked to faces, another to houses, and a CONTROL sham group that did not receive SD-NFB. Memory performance was evaluated 24 h and seven days later using an 'old-new' recognition task, where participants distinguished between 'old' and 'new' images. The results showed that memory contents (faces or houses) whose brain patterns were trained via SD-NFB scored lower in recognition compared to untrained contents, as evidenced 24 h and seven days post-training. In summary, this study demonstrates that SD-NFB can selectively impact the consolidation of specific declarative memories. This technique could hold significant implications for clinical applications, potentially aiding in the modulation of declarative memory strength in neuropsychiatric disorders where memories are pathologically exacerbated.

Heartbeat-related spectral perturbation of electroencephalogram reflects dynamic interoceptive attention states in the trial-by-trial classification analysis

Attending to heartbeats for interoceptive awareness initiates distinct electrophysiological responses synchronized with the R-peaks of an electrocardiogram (ECG), such as the heartbeat-evoked potential (HEP). Beyond HEP, this study proposes heartbeat-related spectral perturbation (HRSP), a time-frequency map of the R-peak locked electroencephalogram (EEG), and explores its characteristics in identifying interoceptive attention states using a classification approach. HRSPs of EEG brain components specified by independent component analysis (ICA) were used for the offline and online classification of interoceptive states. A convolutional neural network (CNN) designed specifically for HRSP was applied to publicly available data from a binary-state experiment (attending to self-heartbeats and white noise) and data from our four-state classification experiment (attending to self-heartbeats, white noise, time passage, and toe) with diverse input feature conditions of HRSP. From the dynamic state perspective, we evaluated the primary frequency bands of HRSP and the minimal number of averaging epochs required to reflect changing interoceptive attention states without compromising accuracy. We also assessed the utility of group ICA and models for classifying HRSP in new participants. The CNN for trial-by-trial HRSP with actual R-peaks demonstrated significantly higher classification accuracy than HRSP with sham, i.e., randomly positioned, R-peaks. Gradient-weighted class activation mapping highlighted the prominent role of theta and alpha bands between 200-600 ms post-R-peak-features absent in classifications using sham HRSPs. Online classification benefits from employing a group ICA and classification model, ensuring reliable accuracy without individual EEG precollection. These results suggest HRSP's potential to reflect interoceptive attention states, proposing transformative implications for clinical applications.

The impact of face masks on face-to-face neural tracking of speech: Auditory and visual obstacles

Face masks provide fundamental protection against the transmission of respiratory viruses but hamper communication. We estimated auditory and visual obstacles generated by face masks on communication by measuring the neural tracking of speech. To this end, we recorded the EEG while participants were exposed to naturalistic audio-visual speech, embedded in 5-talker noise, in three contexts: (i) no-mask (audio-visual information was fully available), (ii) virtual mask (occluded lips, but intact audio), and (iii) real mask (occluded lips and degraded audio). Neural tracking of lip movements and of the sound envelope of speech was measured through backward modeling, that is, by reconstructing stimulus properties from neural activity. Behaviorally, face masks increased perceived listening difficulty and phonological errors in speech content retrieval. At the neural level, we observed that the occlusion of the mouth abolished lip tracking and dampened neural tracking of the speech envelope at the earliest processing stages. By contrast, degraded acoustic information related to face mask filtering altered neural tracking of speech envelope at later processing stages. Finally, a consistent link emerged between the increment of perceived listening difficulty and the drop in reconstruction performance of speech envelope when attending to a speaker wearing a face mask. Results clearly dissociated the visual and auditory impact of face masks on the neural tracking of speech. While the visual obstacle related to face masks hampered the ability to predict and integrate audio-visual speech, the auditory filter generated by face masks impacted neural processing stages typically associated with auditory selective attention. The link between perceived difficulty and neural tracking drop also provides evidence of the impact of face masks on the metacognitive levels subtending face-to-face communication.

EEG decoding reveals task-dependent recoding of sensory information in working memory

Working memory (WM) supports future behavior by retaining perceptual information obtained in the recent past. The present study tested the hypothesis that WM recodes sensory information in a format that better supports behavioral goals. We recorded EEG while participants performed color delayed-estimation tasks where the colorwheel for the response was either randomly rotated or held fixed across trials. Accordingly, observers had to remember the exact colors in the Rotation condition, whereas they could prepare for a response based on the fixed mapping between the colors and their corresponding locations on the colorwheel in the No-Rotation condition. Results showed that the color reports were faster and more precise in the No-Rotation condition even when exactly the same set of colors were tested in both conditions. To investigate how the color information was maintained in the brain, we decoded the color using a multivariate EEG classification method. The decoding was limited to the stimulus encoding period in the Rotation condition, whereas it continued to be significant during the maintenance period in the No-Rotation condition, indicating that the color information was actively maintained in the condition. Follow-up analyses suggested that the prolonged decoding was not merely driven by the covert shift of attention but rather by the recoding of sensory information into an action-oriented response format. Together, these results provide converging evidence that WM flexibly recodes sensory information depending on the specific task context to optimize subsequent behavioral performance.

A Network Approach to Understanding the Role of Executive Functioning and Alpha Oscillations in Inattention and Hyperactivity-Impulsivity Symptoms of ADHD

OBJECTIVE

ADHD is a prevalent neurodevelopmental disorder characterized by symptoms of inattention and hyperactivity-impulsivity. Impairments in executive functioning (EF) are central to models of ADHD, while alpha-band spectral power event-related decreases (ERD) have emerged as a putative electroencephalography (EEG) biomarker of EF in ADHD. Little is known about the roles of EF and alpha ERD and their interactions with symptoms of ADHD.

METHOD

We estimated network models of ADHD symptoms and integrated alpha ERD measures into the symptom network.

RESULTS

EF emerges as a bridge network node connecting alpha ERD and the hyperactivity/impulsivity and inattention symptoms. We found that EF most closely relates to a subset of symptoms, namely the motoric symptoms, "seat" (difficulty staying seated), and "runs" (running or climbing excessively).

CONCLUSIONS

EF functions as a bridge node connecting alpha ERD and the ADHD symptom network. Motoric-type symptoms and EF deficits may constitute important nodes in the interplay between behavior/symptoms, cognition, and neurophysiological markers of ADHD.

Musical tension is affected by metrical structure dynamically and hierarchically

As the basis of musical emotions, dynamic tension experience is felt by listeners as music unfolds over time. The effects of musical harmonic and melodic structures on tension have been widely investigated, however, the potential roles of metrical structures in tension perception remain largely unexplored. This experiment examined how different metrical structures affect tension experience and explored the underlying neural activities. The electroencephalogram (EEG) was recorded and subjective tension was rated simultaneously while participants listened to music meter sequences. On large time scale of whole meter sequences, it was found that different overall tension and low-frequency (1 ~ 4 Hz) steady-state evoked potentials were elicited by metrical structures with different periods of strong beats, and the higher overall tension was associated with metrical structure with the shorter intervals between strong beats. On small time scale of measures, dynamic tension fluctuations within measures was found to be associated with the periodic modulations of high-frequency (10 ~ 25 Hz) neural activities. The comparisons between the same beats within measures and across different meters both on small and large time scales verified the contextual effects of meter on tension induced by beats. Our findings suggest that the overall tension is determined by temporal intervals between strong beats, and the dynamic tension experience may arise from cognitive processing of hierarchical temporal expectation and attention, which are discussed under the theoretical frameworks of metrical hierarchy, musical expectation and dynamic attention.

Machine learning of brain-specific biomarkers from EEG

BACKGROUND

Electroencephalography (EEG) has a long history as a clinical tool to study brain function, and its potential to derive biomarkers for various applications is far from exhausted. Machine learning (ML) can guide future innovation by harnessing the wealth of complex EEG signals to isolate relevant brain activity. Yet, ML studies in EEG tend to ignore physiological artefacts, which may cause problems for deriving biomarkers specific to the central nervous system (CNS).

METHODS

We present a framework for conceptualising machine learning from CNS versus peripheral signals measured with EEG. A signal representation based on Morlet wavelets allowed us to define traditional brain activity features (e.g. log power) and alternative inputs used by state-of-the-art ML approaches based on covariance matrices. Using more than 2600 EEG recordings from large public databases (TUAB, TDBRAIN), we studied the impact of peripheral signals and artefact removal techniques on ML models in age and sex prediction analyses.

FINDINGS

Across benchmarks, basic artefact rejection improved model performance, whereas further removal of peripheral signals using ICA decreased performance. Our analyses revealed that peripheral signals enable age and sex prediction. However, they explained only a fraction of the performance provided by brain signals.

INTERPRETATION

We show that brain signals and body signals, both present in the EEG, allow for prediction of personal characteristics. While these results may depend on specific applications, our work suggests that great care is needed to separate these signals when the goal is to develop CNS-specific biomarkers using ML.

FUNDING

All authors have been working for F. Hoffmann-La Roche Ltd.

Recording Neural Reward Signals in a Naturalistic Operant Task Using Mobile-EEG and Augmented Reality

The electrophysiological response to rewards recorded during laboratory tasks has been well documented, yet little is known about the neural response patterns in a more naturalistic setting. Here, we combined a mobile-EEG system with an augmented reality headset to record event-related brain potentials (ERPs) while participants engaged in a naturalistic operant task to find rewards. Twenty-five participants were asked to navigate toward a west or east goal location marked by floating orbs, and once participants reached the goal location, the orb would then signify a reward (5 cents) or no-reward (0 cents) outcome. Following the outcome, participants returned to a start location marked by floating purple rings, and once standing in the middle, a 3 s counter signaled the next trial, for a total of 200 trials. Consistent with previous research, reward feedback evoked the reward positivity, an ERP component believed to index the sensitivity of the anterior cingulate cortex to reward prediction error signals. The reward positivity peaked ∼230 ms with a maximal at channel FCz (M = -0.695 μV, ±0.23) and was significantly different than zero (p < 0.01). Participants took ∼3.38 s to reach the goal location and exhibited a general lose-shift (68.3% ±3.5) response strategy and posterror slowing. Overall, these novel findings provide support for the idea that combining mobile-EEG with augmented reality technology is a feasible solution to enhance the ecological validity of human electrophysiological studies of goal-directed behavior and a step toward a new era of human cognitive neuroscience research that blurs the line between laboratory and reality.

Towards electrophysiological measurement of presence in virtual reality through auditory oddball stimuli

Objective.Presence is an important aspect of user experience in virtual reality (VR). It corresponds to the illusion of being physically located in a virtual environment (VE). This feeling is usually measured through questionnaires that disrupt presence, are subjective and do not allow for real-time measurement. Electroencephalography (EEG), which measures brain activity, is increasingly used to monitor the state of users, especially while immersed in VR.Approach.In this paper, we present a way of evaluating presence, through the measure of the attention dedicated to the real environment via an EEG oddball paradigm. Using breaks in presence, this experimental protocol constitutes an ecological method for the study of presence, as different levels of presence are experienced in an identical VE.Main results.Through analysing the EEG data of 18 participants, a significant increase in the neurophysiological reaction to the oddball, i.e. the P300 amplitude, was found in low presence condition compared to high presence condition. This amplitude was significantly correlated with the self-reported measure of presence. Using Riemannian geometry to perform single-trial classification, we present a classification algorithm with 79% accuracy in detecting between two presence conditions.Significance.Taken together our results promote the use of EEG and oddball stimuli to monitor presence offline or in real-time without interrupting the user in the VE.

Personality Moderates Intra-Individual Variability in EEG Microstates and Spontaneous Thoughts

Variability in brain activity that persists after accounting for overt behavioral and physiological states is often considered noise and controlled as a covariate in research. However, studying intra-individual variability in brain function can provide valuable insights into the dynamic nature of the brain. To explore this, we conducted a study on 43 participants analyzing the EEG microstate dynamics and self-reported spontaneous mental activity during five-minute resting-state recordings on two separate days with a twenty days average delay between recordings. Our results showed that the associations between EEG microstates and spontaneous cognition significantly changed from one day to another. Moreover, microstate changes were associated with changes in spontaneous cognition. Specifically, inter-day changes in Verbal thoughts about Others and future Planning were positively related to bottom-up sensory network-related microstate changes and negatively associated with top-down, attention, and salience network-related microstates. In addition, we find that personality traits are related to inter-day changes in microstates and spontaneous thoughts. Specifically, extraversion, neuroticism, agreeableness, and openness to experience moderated the relationship between inter-day changes in EEG microstates and spontaneous thoughts. Our study provides valuable information on the dynamic changes in the EEG microstate-spontaneous cognition organization, which could be essential for developing interventions and treatments for neuropsychiatric disorders.

Assessing the effectiveness of spatial PCA on SVM-based decoding of EEG data

Principal component analysis (PCA) has been widely employed for dimensionality reduction prior to multivariate pattern classification (decoding) in EEG research. The goal of the present study was to provide an evaluation of the effectiveness of PCA on decoding accuracy (using support vector machines) across a broad range of experimental paradigms. We evaluated several different PCA variations, including group-based and subject-based component decomposition and the application of Varimax rotation or no rotation. We also varied the numbers of PCs that were retained for the decoding analysis. We evaluated the resulting decoding accuracy for seven common event-related potential components (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity). We also examined more challenging decoding tasks, including decoding of face identity, facial expression, stimulus location, and stimulus orientation. The datasets also varied in the number and density of electrode sites. Our findings indicated that none of the PCA approaches consistently improved decoding performance related to no PCA, and the application of PCA frequently reduced decoding performance. Researchers should therefore be cautious about using PCA prior to decoding EEG data from similar experimental paradigms, populations, and recording setups.

Changes in visually and auditory attended audiovisual speech processing in cochlear implant users: A longitudinal ERP study

Limited auditory input, whether caused by hearing loss or by electrical stimulation through a cochlear implant (CI), can be compensated by the remaining senses. Specifically for CI users, previous studies reported not only improved visual skills, but also altered cortical processing of unisensory visual and auditory stimuli. However, in multisensory scenarios, it is still unclear how auditory deprivation (before implantation) and electrical hearing experience (after implantation) affect cortical audiovisual speech processing. Here, we present a prospective longitudinal electroencephalography (EEG) study which systematically examined the deprivation- and CI-induced alterations of cortical processing of audiovisual words by comparing event-related potentials (ERPs) in postlingually deafened CI users before and after implantation (five weeks and six months of CI use). A group of matched normal-hearing (NH) listeners served as controls. The participants performed a word-identification task with congruent and incongruent audiovisual words, focusing their attention on either the visual (lip movement) or the auditory speech signal. This allowed us to study the (top-down) attention effect on the (bottom-up) sensory cortical processing of audiovisual speech. When compared to the NH listeners, the CI candidates (before implantation) and the CI users (after implantation) exhibited enhanced lipreading abilities and an altered cortical response at the N1 latency range (90-150 ms) that was characterized by a decreased theta oscillation power (4-8 Hz) and a smaller amplitude in the auditory cortex. After implantation, however, the auditory-cortex response gradually increased and developed a stronger intra-modal connectivity. Nevertheless, task efficiency and activation in the visual cortex was significantly modulated in both groups by focusing attention on the visual as compared to the auditory speech signal, with the NH listeners additionally showing an attention-dependent decrease in beta oscillation power (13-30 Hz). In sum, these results suggest remarkable deprivation effects on audiovisual speech processing in the auditory cortex, which partially reverse after implantation. Although even experienced CI users still show distinct audiovisual speech processing compared to NH listeners, pronounced effects of (top-down) direction of attention on (bottom-up) audiovisual processing can be observed in both groups. However, NH listeners but not CI users appear to show enhanced allocation of cognitive resources in visually as compared to auditory attended audiovisual speech conditions, which supports our behavioural observations of poorer lipreading abilities and reduced visual influence on audition in NH listeners as compared to CI users.

Valence-specific EEG microstate modulations during self-generated affective states

Introduction

This study aims to explore the temporal dynamics of brain networks involved in self-generated affective states, specifically focusing on modulating these states in both positive and negative valences. The overarching goal is to contribute to a deeper understanding of the neurodynamic patterns associated with affective regulation, potentially informing the development of biomarkers for therapeutic interventions in mood and anxiety disorders.

Methods

Utilizing EEG microstate analysis during self-generated affective states, we investigated the temporal dynamics of five distinct microstates across different conditions, including baseline resting state and self-generated states of positive valence (e.g., awe, contentment) and negative valence (e.g., anger, fear).

Results

The study revealed noteworthy modulations in microstate dynamics during affective states. Additionally, valence-specific mechanisms of spontaneous affective regulation were identified. Negative valence affective states were characterized by the heightened presence of attention-associated microstates and reduced occurrence of salience-related microstates during negative valence states. In contrast, positive valence affective states manifested a prevalence of microstates related to visual/autobiographical memory and a reduced presence of auditory/language-associated microstates compared to both baseline and negative valence states.

Discussion

This study contributes to the field by employing EEG microstate analysis to discern the temporal dynamics of brain networks involved in self-generated affective states. Insights from this research carry significant implications for understanding neurodynamic patterns in affective regulation. The identification of valence-specific modulations and mechanisms has potential applications in developing biomarkers for mood and anxiety disorders, offering novel avenues for therapeutic interventions.

Willed Attentional Selection of Visual Features: An EEG Study

Visual selective attention studies generally tend to apply cuing paradigms to instructively direct observers' attention to certain locations, features or objects. However, in real situations, attention in humans often flows spontaneously without any specific instructions. Recently, a concept named "willed attention" was raised in visuospatial attention, in which participants are free to make volitional attention decisions. Several ERP components during willed attention were found, along with a perspective that ongoing alpha activity may bias the subsequent attentional choice. However, it remains unclear whether similar neural mechanisms exist in feature- or object-based willed attention. Here, we included choice cues and instruct cues in a feature-based selective attention paradigm, allowing participants to freely choose or to be instructed to attend a color for the subsequent target detection task. Pre-cue ongoing alpha oscillations, cue-evoked potentials and target-related steady-state visual evoked potentials (SSVEPs) were simultaneously measured as markers of attentional processing. As expected, SSVEP responses were similarly modulated by attention between choice and instruct cue trials. Similar to the case of spatial attention, a willed-attention component (Willed Attention Component, WAC) was isolated during the cue-related choice period by comparing choice and instruct cues. However, pre-cue ongoing alpha oscillations did not predict the color choice (yellow vs blue), as indicated by the chance level decoding accuracy (50%). Overall, our results revealed both similarities and differences between spatial and feature-based willed attention, and thus extended the understanding toward the neural mechanisms of volitional attention.

Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN

This paper presents an advanced approach for EEG artifact removal and motor imagery classification using a combination of Four Class Iterative Filtering and Filter Bank Common Spatial Pattern Algorithm with a Modified Deep Neural Network (DNN) classifier. The research aims to enhance the accuracy and reliability of BCI systems by addressing the challenges posed by EEG artifacts and complex motor imagery tasks. The methodology begins by introducing FCIF, a novel technique for ocular artifact removal, utilizing iterative filtering and filter banks. FCIF's mathematical formulation allows for effective artifact mitigation, thereby improving the quality of EEG data. In tandem, the FC-FBCSP algorithm is introduced, extending the Filter Bank Common Spatial Pattern approach to handle four-class motor imagery classification. The Modified DNN classifier enhances the discriminatory power of the FC-FBCSP features, optimizing the classification process. The paper showcases a comprehensive experimental setup, featuring the utilization of BCI Competition IV Dataset 2a & 2b. Detailed preprocessing steps, including filtering and feature extraction, are presented with mathematical rigor. Results demonstrate the remarkable artifact removal capabilities of FCIF and the classification prowess of FC-FBCSP combined with the Modified DNN classifier. Comparative analysis highlights the superiority of the proposed approach over baseline methods and the method achieves the mean accuracy of 98.575%.

Event-related theta and gamma band oscillatory dynamics during visuo-spatial sequence memory in younger and older adults

Visuo-spatial working memory (VSWM) for sequences is thought to be crucial for daily behaviors. Decades of research indicate that oscillations in the gamma and theta bands play important functional roles in the support of visuo-spatial working memory, but the vast majority of that research emphasizes measures of neural activity during memory retention. The primary aims of the present study were (1) to determine whether oscillatory dynamics in the Theta and Gamma ranges would reflect item-level sequence encoding during a computerized spatial span task, (2) to determine whether item-level sequence recall is also related to these neural oscillations, and (3) to determine the nature of potential changes to these processes in healthy cognitive aging. Results indicate that VSWM sequence encoding is related to later (∼700 ms) gamma band oscillatory dynamics and may be preserved in healthy older adults; high gamma power over midline frontal and posterior sites increased monotonically as items were added to the spatial sequence in both age groups. Item-level oscillatory dynamics during the recall of VSWM sequences were related only to theta-gamma phase amplitude coupling (PAC), which increased monotonically with serial position in both age groups. Results suggest that, despite a general decrease in frontal theta power during VSWM sequence recall in older adults, gamma band dynamics during encoding and theta-gamma PAC during retrieval play unique roles in VSWM and that the processes they reflect may be spared in healthy aging.

Neural correlates of confidence during decision formation in a perceptual judgment task

When we make a decision, we also estimate the probability that our choice is correct or accurate. This probability estimate is termed our degree of decision confidence. Recent work has reported event-related potential (ERP) correlates of confidence both during decision formation (the centro-parietal positivity component; CPP) and after a decision has been made (the error positivity component; Pe). However, there are several measurement confounds that complicate the interpretation of these findings. More recent studies that overcome these issues have so far produced conflicting results. To better characterise the ERP correlates of confidence we presented participants with a comparative brightness judgment task while recording electroencephalography. Participants judged which of two flickering squares (varying in luminance over time) was brighter on average. Participants then gave confidence ratings ranging from "surely incorrect" to "surely correct". To elicit a range of confidence ratings we manipulated both the mean luminance difference between the brighter and darker squares (relative evidence) and the overall luminance of both squares (absolute evidence). We found larger CPP amplitudes in trials with higher confidence ratings. This association was not simply a by-product of differences in relative evidence (which covaries with confidence) across trials. We did not identify postdecisional ERP correlates of confidence, except when they were artificially produced by pre-response ERP baselines. These results provide further evidence for neural correlates of processes that inform confidence judgments during decision formation.

Multi-Algorithm Artifact Correction (MAAC) procedure part one: Algorithm and example

The Multi-Algorithm Artifact Correction (MAAC) procedure is presented for electroencephalographic (EEG) data, as made freely available in the open-source EP Toolkit (Dien, 2010). First the major EEG artifact correction methods (regression, spatial filters, principal components analysis, and independent components analysis) are reviewed. Contrary to the dominant approach of picking one method that is thought to be most effective, this review concludes that none are globally superior, but rather each has strengths and weaknesses. Then each of the major artifact types are reviewed (Blink, Corneo-Retinal Dipole, Saccadic Spike Potential, and Movement). For each one, it is proposed that one of the major correction methods is best matched to address it, resulting in the MAAC procedure. The MAAC itself is then presented, as implemented in the EP Toolkit, in order to provide a sense of the user experience. The primary goal of this present paper is to make the conceptual argument for the MAAC approach.

Contralateral delay activity and alpha lateralization reflect retinotopic and screen-centered reference frames in visual memory

The visual system represents objects in a lateralized manner, with contralateral cortical hemispheres responsible for left and right visual hemifields. This organization extends to visual short-term memory (VSTM), as evidenced by electrophysiological indices of VSTM maintenance: contralateral delay activity (CDA) and alpha-band lateralization. However, it remains unclear if VSTM represents object locations in gaze-centered (retinotopic) or screen-centered (spatiotopic) coordinates, especially after eye movements. In two experiments, participants encoded the colors of target objects and made a lateral saccade during the maintenance interval, thereby shifting the object's location on the retina. A non-lateralized probe stimulus was then presented at the new fixation for a change detection task. The CDA maintained lateralization towards the target's original retinotopic location, unaffected by subsequent saccades, and did not invert polarity even when a saccade brought that location into the opposite hemifield. We also found conventional alpha lateralization towards the target's location before a saccade. After a saccade, however, alpha was lateralized towards the screen center regardless of the target's original location, even in a control condition without any memory requirements. This suggests that post-saccadic alpha-band lateralization reflects attentional processes unrelated to memory, while pre- and post-saccade CDA reflect VSTM maintenance in a retinotopic reference frame.

Improving the detection of sleep slow oscillations in electroencephalographic data

Study objectives

We aimed to build a tool which facilitates manual labeling of sleep slow oscillations (SOs) and evaluate the performance of traditional sleep SO detection algorithms on such a manually labeled data set. We sought to develop improved methods for SO detection.

Method

SOs in polysomnographic recordings acquired during nap time from ten older adults were manually labeled using a custom built graphical user interface tool. Three automatic SO detection algorithms previously used in the literature were evaluated on this data set. Additional machine learning and deep learning algorithms were trained on the manually labeled data set.

Results

Our custom built tool significantly decreased the time needed for manual labeling, allowing us to manually inspect 96,277 potential SO events. The three automatic SO detection algorithms showed relatively low accuracy (max. 61.08%), but results were qualitatively similar, with SO density and amplitude increasing with sleep depth. The machine learning and deep learning algorithms showed higher accuracy (best: 99.20%) while maintaining a low prediction time.

Conclusions

Accurate detection of SO events is important for investigating their role in memory consolidation. In this context, our tool and proposed methods can provide significant help in identifying these events.

ORCA: A picture database of object-scene arrangements for cross-cultural and aging research

In recent years, cross-cultural research on the modulation of basic cognitive processes by culture has intensified - also from an aging perspective. Despite this increased research interest, only a few cross-culturally normed non-verbal stimulus sets are available to support cross-cultural cognitive research in younger and older adults. Here we present the ORCA (Official Rating of Complex Arrangements) picture database, which includes a total of 720 object-scene compositions sorted into 180 quadruples (e.g., two different helmets placed in two different deserts). Each quadruple contains visually and semantically matched pairs of objects and pairs of scenes with varying degrees of semantic fit between objects and scenes. A total of 95 younger and older German and Chinese adults rated every object-scene pair on object familiarity and semantic fit between object and scene. While the ratings were significantly correlated between cultures and age groups, small but significant culture and age differences emerged. Object familiarity was higher for older adults than younger adults and for German participants than for Chinese participants. Semantic fit was rated lower by German older adults and Chinese younger adults as compared to German younger adults and Chinese older adults. Due to the large number of stimuli, our database is particularly well suited for cognitive and neuroscientific research on cross-cultural and age-related differences in perception, attention, and memory.

Maximizing signal-to-noise ratio and statistical power in ERP measurement: Single sites versus multi-site average clusters

One important decision in every event-related potential (ERP) experiment is which electrode site(s) to use in quantifying the ERP component of interest. A common approach is to measure the ERP from a single electrode site, typically the site where the ERP component is largest. Alternatively, two or more electrode sites in a given spatial region are averaged together, and the ERP is measured from the resulting multi-site cluster. The goal of the present study was to systematically compare these two measurement approaches across a range of outcome measures and ERP components to determine whether measuring from a single electrode site or an average of multiple sites yields consistently better results. We examined seven common ERP components from the open-source ERP CORE dataset that span a range of neurocognitive processes: N170, mismatch negativity (MMN), N2pc, N400, P3, lateralized readiness potential (LRP), and error-related negativity (ERN). For each component, we compared ERP amplitude, noise level, signal-to-noise ratio, and effect size at two single electrode sites and four multi-site clusters. We also used a Monte Carlo approach to simulate within-participant and between-groups experiments with known effect magnitudes to compare statistical power at single sites and multi-site clusters. Overall, measuring from a multi-site cluster produced results that were as good as or better than measuring from a single electrode site across analyses and components, indicating that the cluster-based measurement approach may be beneficial in quantifying ERPs from a range of neurocognitive domains.

The control patterns of affective processing and cognitive reappraisal: insights from brain controllability analysis

Perceiving and modulating emotions is vital for cognitive function and is often impaired in neuropsychiatric conditions. Current tools for evaluating emotional dysregulation suffer from subjectivity and lack of precision, especially when it comes to understanding emotion from a regulatory or control-based perspective. To address these limitations, this study leverages an advanced methodology known as functional brain controllability analysis. We simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data from 17 healthy subjects engaged in emotion processing and regulation tasks. We then employed a novel EEG/fMRI integration technique to reconstruct cortical activity in a high spatiotemporal resolution manner. Subsequently, we conducted functional brain controllability analysis to explore the neural network control patterns underlying different emotion conditions. Our findings demonstrated that the dorsolateral and ventrolateral prefrontal cortex exhibited increased controllability during the processing and regulation of negative emotions compared to processing of neutral emotion. Besides, the anterior cingulate cortex was notably more active in managing negative emotion than in either controlling neutral emotion or regulating negative emotion. Finally, the posterior parietal cortex emerged as a central network controller for the regulation of negative emotion. This study offers valuable insights into the cortical control mechanisms that support emotion perception and regulation.

Is Visual Prediction Impaired in Adolescents with Autism spectrum Disorder? Event-Related Potentials in a Cued Visual GO/NOGO Task

Aim: Deviant visual processing has been observed in autism spectrum disorder (ASD), manifesting as decreased P1 and P2 components of visual event-related potentials (ERPs). Alterations have been attributed to a failure of Bayesian inference, characterized by hypo-activation of top-down predictive abilities. To test this hypothesis, we measured the visual negativity (vN) as an ERP index of visual preparation hypothesized to mirror predictive brain activity. Method: ERPs in a cued visual GO/NOGO task in 63 adolescents with ASD (IQ > 70, attention-deficit hyperactivity disorder excluded) were compared with ERPs in a sex- and age-matched group of 60 typically developing (TD) controls. Results: The behavioral variables (omissions, commissions, reaction time, and reaction time variability), as well as ERP components reflecting, among other processes, cognitive control (contingent negative variation, P3 GO, P3 NOGO, N2 NOGO) did not differ between the groups. There were group differences in visually based ERPs. Besides P1 and P2 differences, the vN component differentiated the 2 groups with the highest effect size (d  =  0.74).Conclusion: This ERP study lends support to the hypothesis suggesting that a Bayesian hypo-prediction could underlie unique perceptual experiences in individuals with ASD. This could lead to a predisposition to perceive the world with reduced influence and modulation from contextual cues, prior experiences, and pre-existing expectations.

Low-frequency variability in theta activity modulates the attention-fluctuation across task and resting states

Sustained attention is not constant but fluctuates influencing our task performance. Albeit intensive investigations, it remains unclear whether the attention-fluctuation during tasks is derived from its spontaneous fluctuation in the resting state. Here, we addressed this issue by investigating the attention-fluctuation in both task and resting states, through the EEG measurement of theta-variability. We found significant rest-task modulation of theta-variability, i.e., reduced theta-variability in the task state compared to the resting state. This task and rest modulation was manifested in the low-frequency of theta-variability (<0.1 Hz). Furthermore, the low-frequency theta-variability exhibited a significant rest-task correlation, however, only the low-frequency theta-variability in the task state but not in the resting state was correlated with the behavioral performance. These findings shed light on the low-frequency feature of attention-fluctuation, and advanced our understanding of sustained attention by suggesting that the theta-variability in low-frequencies was relevant to attention level in task state.

A Cortical Mechanism Linking Saliency Detection and Motor Reactivity in Rhesus Monkeys

Sudden and surprising sensory events trigger neural processes that swiftly adjust behavior. To study the phylogenesis and the mechanism of this phenomenon, we trained two male rhesus monkeys to keep a cursor inside a visual target by exerting force on an isometric joystick. We examined the effect of surprising auditory stimuli on exerted force, scalp electroencephalographic (EEG) activity, and local field potentials (LFPs) recorded from the dorsolateral prefrontal cortex. Auditory stimuli elicited (1) a biphasic modulation of isometric force, a transient decrease followed by a corrective tonic increase, and (2) EEG and LFP deflections dominated by two large negative-positive waves (N70 and P130). The EEG potential was symmetrical and maximal at the scalp vertex, highly reminiscent of the human "vertex potential." Electrocortical potentials and force were tightly coupled: the P130 amplitude predicted the magnitude of the corrective force increase, particularly in the LFPs recorded from deep rather than superficial cortical layers. These results disclose a phylogenetically preserved corticomotor mechanism supporting adaptive behavior in response to salient sensory events.Significance Statement Survival in the natural world depends on an animal's capacity to adapt ongoing behavior to abrupt unexpected events. To study the neural mechanisms underlying this capacity, we trained monkeys to apply constant force on a joystick while we recorded their brain activity from the scalp and the prefrontal cortex contralateral to the hand holding the joystick. Unexpected auditory stimuli elicited a biphasic force modulation: a transient reduction followed by a corrective adjustment. The same stimuli also elicited EEG and LFP responses, dominated by a biphasic wave that predicted the magnitude of the behavioral adjustment. These results disclose a phylogenetically preserved corticomotor mechanism supporting adaptive behavior in response to unexpected events.

Refixation behavior in naturalistic viewing: Methods, mechanisms, and neural correlates

When freely viewing a scene, the eyes often return to previously visited locations. By tracking eye movements and coregistering eye movements and EEG, such refixations are shown to have multiple roles: repairing insufficient encoding from precursor fixations, supporting ongoing viewing by resampling relevant locations prioritized by precursor fixations, and aiding the construction of memory representations. All these functions of refixation behavior are understood to be underpinned by three oculomotor and cognitive systems and their associated brain structures. First, immediate saccade planning prior to refixations involves attentional selection of candidate locations to revisit. This process is likely supported by the dorsal attentional network. Second, visual working memory, involved in maintaining task-related information, is likely supported by the visual cortex. Third, higher-order relevance of scene locations, which depends on general knowledge and understanding of scene meaning, is likely supported by the hippocampal memory system. Working together, these structures bring about viewing behavior that balances exploring previously unvisited areas of a scene with exploiting visited areas through refixations.

Bridging the gap between EEG and DCNNs reveals a fatigue mechanism of facial repetition suppression

Facial repetition suppression, a well-studied phenomenon characterized by decreased neural responses to repeated faces in visual cortices, remains a subject of ongoing debate regarding its underlying neural mechanisms. Our research harnesses advanced multivariate analysis techniques and the prowess of deep convolutional neural networks (DCNNs) in face recognition to bridge the gap between human electroencephalogram (EEG) data and DCNNs, especially in the context of facial repetition suppression. Our innovative reverse engineering approach, manipulating the neuronal activity in DCNNs and conducted representational comparisons between brain activations derived from human EEG and manipulated DCNN activations, provided insights into the underlying facial repetition suppression. Significantly, our findings advocate the fatigue mechanism as the dominant force behind the facial repetition suppression effect. Broadly, this integrative framework, bridging the human brain and DCNNs, offers a promising tool for simulating brain activity and making inferences regarding the neural mechanisms underpinning complex human behaviors.

Evaluating the effectiveness of artifact correction and rejection in event-related potential research

Eyeblinks and other large artifacts can create two major problems in event-related potential (ERP) research, namely confounds and increased noise. Here, we developed a method for assessing the effectiveness of artifact correction and rejection methods at minimizing these two problems. We then used this method to assess a common artifact minimization approach, in which independent component analysis (ICA) is used to correct ocular artifacts, and artifact rejection is used to reject trials with extreme values resulting from other sources (e.g., movement artifacts). This approach was applied to data from five common ERP components (P3b, N400, N170, mismatch negativity, and error-related negativity). Four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency) were examined for each component. We found that eyeblinks differed systematically across experimental conditions for several of the components. We also found that artifact correction was reasonably effective at minimizing these confounds, although it did not usually eliminate them completely. In addition, we found that the rejection of trials with extreme voltage values was effective at reducing noise, with the benefits of eliminating these trials outweighing the reduced number of trials available for averaging. For researchers who are analyzing similar ERP components and participant populations, this combination of artifact correction and rejection approaches should minimize artifact-related confounds and lead to improved data quality. Researchers who are analyzing other components or participant populations can use the method developed in this study to determine which artifact minimization approaches are effective in their data.

Dynamic network characteristics of adolescents with major depressive disorder: Attention network mediates the association between anhedonia and attentional deficit

Attention deficit is a critical symptom that impairs social functioning in adolescents with major depressive disorder (MDD). In this study, we aimed to explore the dynamic neural network activity associated with attention deficits and its relationship with clinical outcomes in adolescents with MDD. We included 188 adolescents with MDD and 94 healthy controls. By combining psychophysics, resting-state electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) techniques, we aimed to identify dynamic network features through the investigation of EEG microstate characteristics and related temporal network features in adolescents with MDD. At baseline, microstate analysis revealed that the occurrence of Microstate C in the patient group was lower than that in healthy controls, whereas the duration and coverage of Microstate D increased in the MDD group. Mediation analysis revealed that the probability of transition from Microstate C to D mediated anhedonia and attention deficits in the MDD group. fMRI results showed that the temporal variability of the dorsal attention network (DAN) was significantly weaker in patients with MDD than in healthy controls. Importantly, the temporal variability of DAN mediated the relationship between anhedonia and attention deficits in the patient group. After acute-stage treatment, the response prediction group (RP) showed improvement in Microstates C and D compared to the nonresponse prediction group (NRP). For resting-state fMRI data, the temporal variability of DAN was significantly higher in the RP group than in the NRP group. Overall, this study enriches our understanding of the neural mechanisms underlying attention deficits in patients with MDD and provides novel clinical biomarkers.

Expectations modulate retrieval interference during ellipsis resolution

Memory operations during language comprehension are subject to interference: retrieval is harder when items are linguistically similar to each other. We test how such interference effects might be modulated by linguistic expectations. Theories differ in how these factors might interact; we consider three possibilities: (i) predictability determines the need for retrieval, (ii) predictability affects cue-preference during retrieval, or (iii) word predictability moderates the effect of noise in memory during retrieval. We first demonstrate that expectations for a target word modulate retrieval interference in Mandarin noun-phrase ellipsis in an electroencephalography (EEG) experiment. This result obtains in globally ungrammatical sentences - termed "facilitatory interference." Such a pattern is inconsistent with theories that focus only on the need for retrieval. To tease apart cue-preferences from noisy-memory representations, we operationalize the latter using a Transformer neural network language model. Confronting the model with our stimuli reveals an interference effect, consistent with prior work, but that effect does not interact with predictability in contrast to human EEG results. Together, these data are most consistent with the hypothesis that the predictability of target items affects cue-preferences during retrieval.

The effects of pre-cue alpha and cueing strategy on age-related deficits in post-cue alpha activity and target processing during visual spatial attention

Electroencephalography alpha-band (8-13 Hz) activity during visual spatial attention declines in normal aging. We recently reported the impacts of pre-cue baseline alpha and cueing strategy on post-cue anticipatory alpha activity and target processing in visual spatial attention (Wang et al., Cerebral Cortex, 2023). However, whether these factors affected aging effects remains unaddressed. We investigated this issue in two independent experiments (n = 114) with different cueing strategies (instructional vs. probabilistic). When median-splitting young adults (YA) by their pre-cue alpha power, we found that older adults exhibited similar pre-cue and post-cue alpha activity as YA with lower pre-cue alpha, and only YA with higher pre-cue alpha showed significant post-cue alpha activity, suggesting that diminished anticipatory alpha activity was not specific to aging but likely due to a general decrease with baseline alpha. Moreover, we found that the aging effects on cue-related event-related potentials were dependent on cueing strategy but were relatively independent of pre-cue alpha. However, age-related deficits in target-related N1 attentional modulation might depend on both pre-cue alpha and cueing strategy. By considering the impacts of pre-cue alpha and cueing strategy, our findings offer new insights into age-related deficits in anticipatory alpha activity and target processing during visual spatial attention.

EEG correlates of anticipatory attention and target processing in children and adults during visual spatial attention

The ability of attentional orienting has been suggested to keep developing throughout childhood. Electroencephalography (EEG) studies have shown that 6-10 year old children exhibit lateralized alpha-band (8-13 Hz) activity and event-related potentials (ERPs) that are classic markers of spatial attentional orienting in adults. However, the lack of a direct comparison of these EEG correlates between children and adults in the same experiment made it difficult to evaluate developmental effects on neural activity throughout attentional stages. This study aimed to directly compare cue-related alpha activity and ERPs for the anticipatory attention stage and target-related ERPs for the target processing stage between healthy children and adults. Participants, including 19 children (6-10 years) and 23 adults (18-34 years), successfully completed a visual spatial attention task, although children responded more slowly and less consistently than adults. Both age groups exhibited significant cue-related alpha lateralization and ERPs (EDAN, ADAN, and LDAP) during anticipatory attention and significant attentional modulation of target-related N1 during target processing. However, no significant difference was found in the magnitude of attentional modulation of these EEG correlates between children and adults. These findings suggest that the neural underpinnings of anticipatory attention and target processing during visual spatial attention could have been largely developed in 6-10 year old children.

Spatiotemporal dynamics of self-generated imagery reveal a reverse cortical hierarchy from cue-induced imagery

Visual imagery allows for the construction of rich internal experience in our mental world. However, it has remained poorly understood how imagery experience derives volitionally as opposed to being cue driven. Here, using electroencephalography and functional magnetic resonance imaging, we systematically investigate the spatiotemporal dynamics of self-generated imagery by having participants volitionally imagining one of the orientations from a learned pool. We contrast self-generated imagery with cue-induced imagery, where participants imagined line orientations based on associative cues acquired previously. Our results reveal overlapping neural signatures of cue-induced and self-generated imagery. Yet, these neural signatures display substantially differential sensitivities to the two types of imagery: self-generated imagery is supported by an enhanced involvement of the anterior cortex in representing imagery contents. By contrast, cue-induced imagery is supported by enhanced imagery representations in the posterior visual cortex. These results jointly support a reverse cortical hierarchy in generating and maintaining imagery contents in self-generated versus externally cued imagery.

Behavioral and neurophysiological indices of the racial bias modulation after virtual embodiment in other-race body

Racial bias-nonconscious behavioral inclinations against people of other ethnic groups-heavily contributes to inequality and discrimination. Immersive virtual reality (IVR) can reduce implicit racial bias through the feeling of owning (embodying) a virtual body of a different "race"; however, it has been demonstrated only behaviorally for the implicit attitudes. Here, we investigated the implicit (racial IAT) and the neurophysiological (the N400 component of the event-related potentials for verbal stimuli that violated negative racial stereotypes) correlates of the embodiment-induced reduction of the implicit racial bias. After embodying a Black avatar, Caucasian participants had reduced implicit racial bias (IAT) but both groups showed the typical N400. This is the first evidence to suggest that virtual embodiment affects the evaluative component of the implicit biases but not the neurophysiological index of their cognitive component (i.e., stereotyping). This can inform interventions that promote inclusivity through the implicit/indirect procedures, such as embodiment.

Stimulus expectations do not modulate visual event-related potentials in probabilistic cueing designs

Humans and other animals can learn and exploit repeating patterns that occur within their environments. These learned patterns can be used to form expectations about future sensory events. Several influential predictive coding models have been proposed to explain how learned expectations influence the activity of stimulus-selective neurons in the visual system. These models specify reductions in neural response measures when expectations are fulfilled (termed expectation suppression) and increases following surprising sensory events. However, there is currently scant evidence for expectation suppression in the visual system when confounding factors are taken into account. Effects of surprise have been observed in blood oxygen level dependent (BOLD) signals, but not when using electrophysiological measures. To provide a strong test for expectation suppression and surprise effects we performed a predictive cueing experiment while recording electroencephalographic (EEG) data. Participants (n=48) learned cue-face associations during a training session and were then exposed to these cue-face pairs in a subsequent experiment. Using univariate analyses of face-evoked event-related potentials (ERPs) we did not observe any differences across expected (90% probability), neutral (50%) and surprising (10%) face conditions. Across these comparisons, Bayes factors consistently favoured the null hypothesis throughout the time-course of the stimulus-evoked response. When using multivariate pattern analysis we did not observe above-chance classification of expected and surprising face-evoked ERPs. By contrast, we found robust within- and across-trial stimulus repetition effects. Our findings do not support predictive coding-based accounts that specify reduced prediction error signalling when perceptual expectations are fulfilled. They instead highlight the utility of other types of predictive processing models that describe expectation-related phenomena in the visual system without recourse to prediction error signalling.

The time course of category-based attentional template pre-activation depends on the category framework

When searching for a target defined by a set of objects, attention can be directed toward task-relevant objects by creating a category-based attentional template (CAT). Previous studies have found that CAT can be activated before the onset of the target. However, the time course of CAT pre-activation and whether the category framework (prototypical or semantic) can modulate it remain unclear. To explore the time course of CAT pre-activation, we employed a rapid serial probe presentation paradigm (RSPP) with event-related potentials (ERPs). To investigate the effect of the category framework on the time course of CAT pre-activation, the target category was defined as the prototypical category (Experiment 1) or the semantic category (Experiment 2). The results showed that the prototype-based CAT was pre-activated 300 ms prior to the target, whereas the semantics-based CAT was pre-activated 1500 ms before the onset of the target. The difference in the time course of pre-activation between the two CAT types indicates that the category framework can modulate the time course of CAT pre-activation. Additionally, during the attentional selection phase, an overall comparison of the target revealed that a larger N2pc was elicited by the prototype-based CAT than by the semantics-based CAT, suggesting that the prototype-based CAT could capture more attention than the semantics-based CAT. The findings on the difference between the two CAT frameworks in the preparatory and attentional selection phases provide more evidence for categorical information in visual search and extend our understanding of the mechanism of categorical attentional selection.

Relationship between default mode network and resting-state electroencephalographic alpha rhythms in cognitively unimpaired seniors and patients with dementia due to Alzheimer's disease

Here we tested the hypothesis of a relationship between the cortical default mode network (DMN) structural integrity and the resting-state electroencephalographic (rsEEG) rhythms in patients with Alzheimer's disease with dementia (ADD). Clinical and instrumental datasets in 45 ADD patients and 40 normal elderly (Nold) persons originated from the PDWAVES Consortium (www.pdwaves.eu). Individual rsEEG delta, theta, alpha, and fixed beta and gamma bands were considered. Freeware platforms served to derive (1) the (gray matter) volume of the DMN, dorsal attention (DAN), and sensorimotor (SMN) cortical networks and (2) the rsEEG cortical eLORETA source activities. We found a significant positive association between the DMN gray matter volume, the rsEEG alpha source activity estimated in the posterior DMN nodes (parietal and posterior cingulate cortex), and the global cognitive status in the Nold and ADD participants. Compared with the Nold, the ADD group showed lower DMN gray matter, lower rsEEG alpha source activity in those nodes, and lower global cognitive status. This effect was not observed in the DAN and SMN. These results suggest that the DMN structural integrity and the rsEEG alpha source activities in the DMN posterior hubs may be related and predict the global cognitive status in ADD and Nold persons.

Promoting Simple and Engaging Brain-Computer Interface Designs for Children by Evaluating Contrasting Motion Techniques

PURPOSE

There is an increasing focus on using motion in augmentative and alternative communication (AAC) systems. In considering brain-computer interface access to AAC (BCI-AAC), motion may provide a simpler or more intuitive avenue for BCI-AAC control. Different motion techniques may be utilized in supporting competency with AAC devices including simple (e.g., zoom) and complex (behaviorally relevant animation) methods. However, how different pictorial symbol animation techniques impact BCI-AAC is unclear.

METHOD

Sixteen healthy children completed two experimental conditions. These conditions included highlighting of pictorial symbols via both functional (complex) and zoom (simple) animation to evaluate the effects of motion techniques on P300-based BCI-AAC signals and offline (predicted) BCI-AAC performance.

RESULTS

Functional (complex) animation significantly increased attentional-related P200/P300 event-related potential (ERP) amplitudes in the parieto-occipital area. Zoom (simple) animation significantly decreased N400 latency. N400 ERP amplitude was significantly greater, and occurred significantly earlier, on the right versus left side for the functional animation condition within the parieto-occipital bin. N200 ERP latency was significantly reduced over the left hemisphere for the zoom condition in the central bin. As hypothesized, elicitation of all targeted ERP components supported offline (predicted) BCI-AAC performance being similar between conditions.

CONCLUSION

Study findings provide continued support for the use of animation in BCI-AAC systems for children and highlight differences in neural and attentional processing between complex and simple animation techniques.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.24085623.

Resting EEG power spectra across middle to late life: associations with age, cognition, APOE-ɛ4 carriage, and cardiometabolic burden

We investigated how resting electroencephalography (EEG) measures are associated with risk factors for late-life cognitive impairment and dementia, including age, apolipoprotein E ɛ4 (APOE-ɛ4) carriage, and cardiometabolic burden. Resting EEG was recorded from 86 adults (50-80 years of age). Participants additionally completed the Addenbrooke's Cognitive Examination (ACE) III and had blood drawn to assess APOE-ɛ4 carriage status and cardiometabolic burden. EEG power spectra were decomposed into sources of periodic and aperiodic activity to derive measures of aperiodic component slope and alpha (7-14 Hz) and beta (15-30 Hz) peak power and peak frequency. Alpha and beta peak power measures were corrected for aperiodic activity. The aperiodic component slope was correlated with ACE-III scores but not age. Alpha peak frequency decreased with age. Individuals with higher cardiometabolic burden had lower alpha peak frequencies and lower beta peak power. APOE-ɛ4 carriers had lower beta peak frequencies. Our findings suggest that the slope of the aperiodic component of resting EEG power spectra is more closely associated with measures of cognitive performance rather than chronological age in older adults.

A Brain-To-Brain Mechanism for Social Transmission of Threat Learning

Survival and adaptation in environments require swift and efficacious learning about what is dangerous. Across species, much of such threat learning is acquired socially, e.g., through the observation of others' ("demonstrators'") defensive behaviors. However, the specific neural mechanisms responsible for the integration of information shared between demonstrators and observers remain largely unknown. This dearth of knowledge is addressed by performing magnetoencephalography (MEG) neuroimaging in demonstrator-observer dyads. A set of stimuli are first shown to a demonstrator whose defensive responses are filmed and later presented to an observer, while neuronal activity is recorded sequentially from both individuals who never interacted directly. These results show that brain-to-brain coupling (BtBC) in the fronto-limbic circuit (including insula, ventromedial, and dorsolateral prefrontal cortex) within demonstrator-observer dyads predict subsequent expressions of learning in the observer. Importantly, the predictive power of BtBC magnifies when a threat is imminent to the demonstrator. Furthermore, BtBC depends on how observers perceive their social status relative to the demonstrator, likely driven by shared attention and emotion, as bolstered by dyadic pupillary coupling. Taken together, this study describes a brain-to-brain mechanism for social threat learning, involving BtBC, which reflects social relationships and predicts adaptive, learned behaviors.

Distinguishing Fine Structure and Summary Representation of Sound Textures from Neural Activity

The auditory system relies on both local and summary representations; acoustic local features exceeding system constraints are compacted into a set of summary statistics. Such compression is pivotal for sound-object recognition. Here, we assessed whether computations subtending local and statistical representations of sounds could be distinguished at the neural level. A computational auditory model was employed to extract auditory statistics from natural sound textures (i.e., fire, rain) and to generate synthetic exemplars where local and statistical properties were controlled. Twenty-four human participants were passively exposed to auditory streams while the electroencephalography (EEG) was recorded. Each stream could consist of short, medium, or long sounds to vary the amount of acoustic information. Short and long sounds were expected to engage local or summary statistics representations, respectively. Data revealed a clear dissociation. Compared with summary-based ones, auditory-evoked responses based on local information were selectively greater in magnitude in short sounds. Opposite patterns emerged for longer sounds. Neural oscillations revealed that local features and summary statistics rely on neural activity occurring at different temporal scales, faster (beta) or slower (theta-alpha). These dissociations emerged automatically without explicit engagement in a discrimination task. Overall, this study demonstrates that the auditory system developed distinct coding mechanisms to discriminate changes in the acoustic environment based on fine structure and summary representations.

Encoding, working memory, or decision: how feedback modulates time perception

The hypothesis that individuals can accurately represent temporal information within approximately 3 s is the premise of several theoretical models and empirical studies in the field of temporal processing. The significance of accurately representing time within 3 s and the universality of the overestimation contrast dramatically. To clarify whether this overestimation arises from an inability to accurately represent time or a response bias, we systematically examined whether feedback reduces overestimation at the 3 temporal processing stages of timing (encoding), working memory, and decisions proposed by the scalar timing model. Participants reproduced the time interval between 2 circles with or without feedback, while the electroencephalogram (EEG) was synchronously recorded. Behavioral results showed that feedback shortened reproduced times and significantly minimized overestimation. EEG results showed that feedback significantly decreased the amplitude of contingent negative variation (CNV) in the decision stage but did not modulate the CNV amplitude in the encoding stage or the P2-P3b amplitudes in the working memory stage. These results suggest that overestimation arises from response bias when individuals convert an accurate representation of time into behavior. Our study provides electrophysiological evidence to support the conception that short intervals under approximately 3 s can be accurately represented as "temporal gestalt."

Suppression of cortical electrostimulation artifacts using pre-whitening and null projection

Objective.Invasive brain-computer interfaces (BCIs) have shown promise in restoring motor function to those paralyzed by neurological injuries. These systems also have the ability to restore sensation via cortical electrostimulation. Cortical stimulation produces strong artifacts that can obscure neural signals or saturate recording amplifiers. While front-end hardware techniques can alleviate this problem, residual artifacts generally persist and must be suppressed by back-end methods.Approach.We have developed a technique based on pre-whitening and null projection (PWNP) and tested its ability to suppress stimulation artifacts in electroencephalogram (EEG), electrocorticogram (ECoG) and microelectrode array (MEA) signals from five human subjects.Main results.In EEG signals contaminated by narrow-band stimulation artifacts, the PWNP method achieved average artifact suppression between 32 and 34 dB, as measured by an increase in signal-to-interference ratio. In ECoG and MEA signals contaminated by broadband stimulation artifacts, our method suppressed artifacts by 78%-80% and 85%, respectively, as measured by a reduction in interference index. When compared to independent component analysis, which is considered the state-of-the-art technique for artifact suppression, our method achieved superior results, while being significantly easier to implement.Significance.PWNP can potentially act as an efficient method of artifact suppression to enable simultaneous stimulation and recording in bi-directional BCIs to biomimetically restore motor function.

Recording neural reward signals in the real-world using mobile-EEG and augmented reality

The electrophysiological response to rewards recorded during laboratory-based tasks has been well documented over the past two decades, yet little is known about the neural response patterns in 'real-world' settings. To address this issue, we combined a mobile-EEG system with an augmented reality headset (which blends high definition "holograms" within the real-world) to record event-related brain potentials (ERP) while participants navigated an operant chamber to find rewards. 25 participants (age = 18-43, Male=6, Female=19) were asked to choose between two floating holograms marking a west or east goal-location in a large room, and once participants reached the goal location, the hologram would turn into a reward (5 cents) or no-reward (0 cents) cue. Following the feedback cue, participants were required to return to a hologram marking the start location, and once standing in it, a 3 second counter hologram would initiate the next trial. This sequence was repeated until participants completed 200 trials. Consistent with previous research, reward feedback evoked the reward positivity, an ERP component believed to index the sensitivity of the anterior cingulate cortex to reward prediction error signals. The reward positivity peaked around 235ms post-feedback with a maximal at channel FCz (M=-2.60μV, SD=1.73μV) and was significantly different than zero (p < 0.01). At a behavioral level, participants took approximately 3.38 seconds to reach the goal-location and exhibited a general lose-shift (68.3% ± 3.5) response strategy and were slightly slower to return to the start location following negative feedback (2.43 sec) compared to positive feedback (2.38 sec), evidence of post-error slowing. Overall, these findings provide the first evidence that combining mobile-EEG with augmented reality technology is a feasible solution to enhance the ecological validity of human electrophysiological studies of goal-directed behavior and a step towards a new era of human cognitive neuroscience research that blurs the line between laboratory and reality.