Imaging human EEG dynamics using independent component analysis

Theta Oscillations Alternate With High Amplitude Neocortical Population Within Synchronized States

Synchronized states are marked by large-amplitude low-frequency oscillations in the cortex. These states can be seen during quiet waking or slow-wave sleep. Within synchronized states, previous studies have noted a plethora of different types of activity, including delta oscillations (0.5-4 Hz) and slow oscillations (<1 Hz) in the neocortex and large- and small- irregular activity in the hippocampus. However, it is not still fully characterized how neural populations contribute to the synchronized state. Here we apply independent component analysis to parse which populations are involved in different kinds of neocortical activity, and find two populations that alternate throughout synchronized states. One population broadly affects neocortical deep layers, and is associated with larger amplitude slower neocortical oscillations. The other population exhibits theta-frequency oscillations that are not easily observed in raw field potential recordings. These theta oscillations apparently come from below the neocortex, suggesting hippocampal origin, and are associated with smaller amplitude faster neocortical oscillations. Relative involvement of these two alternating populations may indicate different modes of operation within synchronized states.

Parafoveal previews and lexical frequency in natural reading: Evidence from eye movements and fixation-related potentials

Participants' eye movements and electroencephalogram (EEG) signal were recorded as they read sentences displayed according to the gaze-contingent boundary paradigm. Two target words in each sentence were manipulated for lexical frequency (high vs. low frequency) and parafoveal preview of each target word (identical vs. string of random letters vs. string of Xs). Eye movement data revealed visual parafoveal-on-foveal (PoF) effects, as well as foveal visual and orthographic preview effects and word frequency effects. Fixation-related potentials (FRPs) showed visual and orthographic PoF effects as well as foveal visual and orthographic preview effects. Our results replicated the early preview positivity effect (Dimigen, Kliegl, & Sommer, 2012) in the X-string preview condition, and revealed different neural correlates associated with a preview comprised of a string of random letters relative to a string of Xs. The former effects seem likely to reflect difficulty associated with the integration of parafoveal and foveal information, as well as feature overlap, while the latter reflect inhibition, and potentially disruption, to processing underlying reading. Interestingly, and consistent with Kretzschmar, Schlesewsky, and Staub (2015), no frequency effect was reflected in the FRP measures. The findings provide insight into the neural correlates of parafoveal processing and written word recognition in reading and demonstrate the value of utilizing ecologically valid paradigms to study well established phenomena that occur as text is read naturally. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Contributions of non-primary cortical sources to auditory temporal processing

Temporal processing is essential for speech perception and directional hearing. However, the number and locations of cortical sources involved in auditory temporal processing are still a matter of debate. Using source reconstruction of human EEG responses, we show that, in addition to primary sources in the auditory cortices, sources outside the auditory cortex, designated as non-primary sources, are involved in auditory temporal processing. Non-primary sources within the left and right motor areas, the superior parietal lobe and the right occipital lobe were activated by amplitude-modulated stimuli, and were involved in the functional network. The robustness of these findings was checked for different stimulation conditions. The non-primary sources showed weaker phase-locking and lower activity than primary sources. These findings suggest that the non-primary sources belong to the non-primary auditory pathway. This pathway and non-primary sources detected in motor area explain how, in temporal prediction of upcoming stimuli and motor theory of speech perception, the motor area receives auditory inputs.

Neural Processing of Dynamic Animated Social Interactions in Young Children With Autism Spectrum Disorder: A High-Density Electroencephalography Study

Background: Atypical neural processing of social visual information contributes to impaired social cognition in autism spectrum disorder. However, evidence for early developmental alterations in neural processing of social contingencies is scarce. Most studies in the literature have been conducted in older children and adults. Here, we aimed to investigate alterations in neural processing of social visual information in children with autism spectrum disorder compared to age-matched typically developing peers. Methods: We used a combination of 129-channel electroencephalography and high-resolution eye-tracking to study differences in the neural processing of dynamic cartoons containing human-like social interactions between 14 male children with autism spectrum disorder and 14 typically developing male children, aged 2-5 years. Using a microstate approach, we identified four prototypical maps in both groups and compared the temporal characteristics and inverse solutions (activation of neural sources) of these maps between groups. Results: Inverse solutions of the group maps that were most dominant during free viewing of the dynamic cartoons indicated decreased prefrontal and cingulate activation, impaired activation of the premotor cortex, and increased activation of parietal, temporal, occipital, and cerebellar regions in children with autism spectrum disorder compared to their typically developing peers. Conclusions: Our findings suggest that impairments in brain regions involved in processing social contingencies embedded in dynamic cartoons are present from an early age in autism spectrum disorder. To the best of our knowledge, this is the first study to investigate neural processing of social interactions of children with autism spectrum disorder using dynamic semi-naturalistic stimuli.

Semantic anomaly detection in school-aged children during natural sentence reading - A study of fixation-related brain potentials

In this study, we investigated the effects of context-related semantic anomalies on the fixation-related brain potentials of 12-13-year-old Finnish children in grade 6 during sentence reading. The detection of such anomalies is typically reflected in the N400 event-related potential. We also examined whether the representation invoked by the sentence context extends to the orthographic representation level by replacing the final words of the sentence with an anomalous word neighbour of a plausible word. The eye-movement results show that the anomalous word neighbours of plausible words cause similar first-fixation and gaze duration reactions, as do other anomalous words. Similarly, we observed frontal negativity in the fixation-related potential of the unrelated anomalous words and in the anomalous word neighbours. This frontal negativity was larger in both anomalous conditions than in the response elicited by the plausible condition. We thus show that the brain successfully uses context to separate anomalous words from plausible words on a single letter level during free reading. From the P600 response of the scalp waveform, we observed that the P600 was delayed in the anomalous word neighbour condition. We performed group-level decomposition on the data with ICA (independent component analysis) and analysed the time course and source structure of the decomposed data. This analysis of decomposed brain signals not only confirmed the delay of the P600 response but also revealed that the frontal negativity concealed s more typical and separate N400 response, which was similarly delayed in the anomalous word neighbour condition, as was the P600 response. Source analysis of these independent components implicated the right frontal eye field as the cortical source for the frontal negativity and the middle temporal and parietal regions as cortical sources for the components resembling the N400 and P600 responses. We interpret the delays present in N400 and P600 responses to anomalous word neighbours to reflect competition with the representation of the plausible word just one letter different.

Cortical dynamics during preparation and execution of reactive balance responses with distinct postural demands

The contributions of the cerebral cortex to human balance control are clearly demonstrated by the profound impact of cortical lesions on the ability to maintain standing balance. The cerebral cortex is thought to regulate subcortical postural centers to maintain upright balance and posture under varying environmental conditions and task demands. However, the cortical mechanisms that support standing balance remain elusive. Here, we present an EEG-based analysis of cortical oscillatory dynamics during the preparation and execution of balance responses with distinct postural demands. In our experiment, participants responded to backward movements of the support surface either with one forward step or by keeping their feet in place. To challenge the postural control system, we applied participant-specific high accelerations of the support surface such that the postural demand was low for stepping responses and high for feet-in-place responses. We expected that postural demand modulated the power of intrinsic cortical oscillations. Independent component analysis and time-frequency domain statistics revealed stronger suppression of alpha (9-13 Hz) and low-gamma (31-34 Hz) rhythms in the supplementary motor area (SMA) when preparing for feet-in-place responses (i.e., high postural demand). Irrespective of the response condition, support-surface movements elicited broadband (3-17 Hz) power increase in the SMA and enhancement of the theta (3-7 Hz) rhythm in the anterior prefrontal cortex (PFC), anterior cingulate cortex (ACC), and bilateral sensorimotor cortices (M1/S1). Although the execution of reactive responses resulted in largely similar cortical dynamics, comparison between the bilateral M1/S1 showed that stepping responses corresponded with stronger suppression of the beta (13-17 Hz) rhythm in the M1/S1 contralateral to the support leg. Comparison between response conditions showed that feet-in-place responses corresponded with stronger enhancement of the theta (3-7 Hz) rhythm in the PFC. Our results provide novel insights into the cortical dynamics of SMA, PFC, and M1/S1 during the control of human balance.

Sensorimotor activity measured via oscillations of EEG mu rhythms in speech and non-speech discrimination tasks with and without segmentation demands

Better understanding of the role of sensorimotor processing in speech and non-speech segmentation can be achieved with more temporally precise measures. Twenty adults made same/different discriminations of speech and non-speech stimuli pairs, with and without segmentation demands. Independent component analysis of 64-channel EEG data revealed clear sensorimotor mu components, with characteristic alpha and beta peaks, localized to premotor regions in 70% of participants.Time-frequency analyses of mu components from accurate trials showed that (1) segmentation tasks elicited greater event-related synchronization immediately following offset of the first stimulus, suggestive of inhibitory activity; (2) strong late event-related desynchronization in all conditions, suggesting that working memory/covert replay contributed substantially to sensorimotor activity in all conditions; (3) stronger beta desynchronization in speech versus non-speech stimuli during stimulus presentation, suggesting stronger auditory-motor transforms for speech versus non-speech stimuli. Findings support the continued use of oscillatory approaches for helping understand segmentation and other cognitive tasks.

Biomechanical and neurocognitive performance outcomes of walking with transtibial limb loss while challenged by a concurrent task

Individuals who have sustained loss of a lower limb may require adaptations in sensorimotor and control systems to effectively utilize a prosthesis, and the interaction of these systems during walking is not clearly understood for this patient population. The aim of this study was to concurrently evaluate temporospatial gait mechanics and cortical dynamics in a population with and without unilateral transtibial limb loss (TT). Utilizing motion capture and electroencephalography, these outcomes were simultaneously collected while participants with and without TT completed a concurrent task of varying difficulty (low- and high-demand) while seated and walking. All participants demonstrated a wider base of support and more stable gait pattern when walking and completing the high-demand concurrent task. The cortical dynamics were similarly modulated by the task demand for both groups, to include a decrease in the novelty-P3 component and increase in the frontal theta/parietal alpha ratio power when completing the high-demand task, although specific differences were also observed. These findings confirm and extend prior efforts indicating that dual-task walking can negatively affect walking mechanics and/or neurocognitive performance. However, there may be limited additional cognitive and/or biomechanical impact of utilizing a prosthesis in a stable, protected environment in TT who have acclimated to ambulating with a prosthesis. These results highlight the need for future work to evaluate interactions between these cognitive-motor control systems for individuals with more proximal levels of lower limb loss, and in more challenging (ecologically valid) environments.

A touching Sight: EEG/ERP correlates for the vicarious processing of affectionate touch

Observers can simulate aspects of other people's tactile experiences. We asked whether they do so when faced with full-body social interactions, whether emerging representations go beyond basic sensorimotor mirroring, and whether they depend on processing goals and inclinations. In an EEG/ERP study, we presented line-drawn, dyadic interactions with and without affectionate touch. In an explicit and an implicit task, participants categorized images into touch versus no-touch and same versus opposite sex interactions, respectively. Modulations of central Rolandic rhythms implied that affectionate touch displays engaged sensorimotor mechanisms. Additionally, the late positive potential (LPP) being larger for images with as compared to without touch pointed to an involvement of higher order socio-affective mechanisms. Task and sex modulated touch perception. Sensorimotor responding, indexed by Rolandic rhythms, was fairly independent of the task but appeared less effortful in women than in men. Touch induced socio-affective responding, indexed by the LPP, declined from explicit to implicit processing in women and disappeared in men. In sum, this study provides first evidence that vicarious touch from full-body social interactions entails shared sensorimotor as well as socio-affective experiences. Yet, mental representations of touch at a socio-affective level are more likely when touch is goal relevant and observers are female. Together, these results outline the conditions under which touch in visual media may be usefully employed to socially engage observers.

Modeling brain dynamic state changes with adaptive mixture independent component analysis

There is a growing interest in neuroscience in assessing the continuous, endogenous, and nonstationary dynamics of brain network activity supporting the fluidity of human cognition and behavior. This non-stationarity may involve ever-changing formation and dissolution of active cortical sources and brain networks. However, unsupervised approaches to identify and model these changes in brain dynamics as continuous transitions between quasi-stable brain states using unlabeled, noninvasive recordings of brain activity have been limited. This study explores the use of adaptive mixture independent component analysis (AMICA) to model multichannel electroencephalographic (EEG) data with a set of ICA models, each of which decomposes an adaptively learned portion of the data into statistically independent sources. We first show that AMICA can segment simulated quasi-stationary EEG data and accurately identify ground-truth sources and source model transitions. Next, we demonstrate that AMICA decomposition, applied to 6-13 channel scalp recordings from the CAP Sleep Database, can characterize sleep stage dynamics, allowing 75% accuracy in identifying transitions between six sleep stages without use of EEG power spectra. Finally, applied to 30-channel data from subjects in a driving simulator, AMICA identifies models that account for EEG during faster and slower response to driving challenges, respectively. We show changes in relative probabilities of these models allow effective prediction of subject response speed and moment-by-moment characterization of state changes within single trials. AMICA thus provides a generic unsupervised approach to identifying and modeling changes in EEG dynamics. Applied to continuous, unlabeled multichannel data, AMICA may likely be used to detect and study any changes in cognitive states.

A Neural Mechanism for Surprise-related Interruptions of Visuospatial Working Memory

Surprising perceptual events recruit a fronto-basal ganglia mechanism for inhibition, which suppresses motor activity following surprise. A recent study found that this inhibitory mechanism also disrupts the maintenance of verbal working memory (WM) after surprising tones. However, it is unclear whether this same mechanism also relates to surprise-related interruptions of non-verbal WM. We tested this hypothesis using a change-detection task, in which surprising tones impaired visuospatial WM. Participants also performed a stop-signal task (SST). We used independent component analysis and single-trial scalp-electroencephalogram to test whether the same inhibitory mechanism that reflects motor inhibition in the SST relates to surprise-related visuospatial WM decrements, as was the case for verbal WM. As expected, surprising tones elicited activity of the inhibitory mechanism, and this activity correlated strongly with the trial-by-trial level of surprise. However, unlike for verbal WM, the activity of this mechanism was unrelated to visuospatial WM accuracy. Instead, inhibition-independent activity that immediately succeeded the inhibitory mechanism was increased when visuospatial WM was disrupted. This shows that surprise-related interruptions of visuospatial WM are not effected by the same inhibitory mechanism that interrupts verbal WM, and instead provides evidence for a 2-stage model of distraction.

Cognitive load reduces the effects of optic flow on gait and electrocortical dynamics during treadmill walking

During navigation of complex environments, the brain must continuously adapt to both external demands, such as fluctuating sensory inputs, and internal demands, such as engagement in a cognitively demanding task. Previous studies have demonstrated changes in behavior and gait with increased sensory and cognitive load, but the underlying cortical mechanisms remain largely unknown. In the present study, in a mobile brain/body imaging (MoBI) approach, 16 young adults walked on a treadmill with high-density EEG while 3-dimensional (3D) motion capture tracked kinematics of the head and feet. Visual load was manipulated with the presentation of optic flow with and without continuous mediolateral perturbations. The effects of cognitive load were assessed by the performance of a go/no-go task on half of the blocks. During increased sensory load, participants walked with shorter and wider strides, which may indicate a more restrained pattern of gait. Interestingly, cognitive task engagement attenuated these effects of sensory load on gait. Using an independent component analysis and dipole-fitting approach, we found that cautious gait was accompanied by neuro-oscillatory modulations localized to frontal (supplementary motor area, anterior cingulate cortex) and parietal (inferior parietal lobule, precuneus) areas. Our results show suppression in alpha/mu (8-12 Hz) and beta (13-30 Hz) rhythms, suggesting enhanced activation of these regions with unreliable sensory inputs. These findings provide insight into the neural correlates of gait adaptation and may be particularly relevant to older adults who are less able to adjust to ongoing cognitive and sensory demands while walking. NEW & NOTEWORTHY The neural underpinnings of gait adaptation in humans are poorly understood. To this end, we recorded high-density EEG combined with three-dimensional body motion tracking as participants walked on a treadmill while exposed to full-field optic flow stimulation. Perturbed visual input led to a more cautious gait pattern with neuro-oscillatory modulations localized to premotor and parietal regions. Our findings show a possible brain-behavior link that might further our understanding of gait and mobility impairments.

Principal States of Dynamic Functional Connectivity Reveal the Link Between Resting-State and Task-State Brain: An fMRI Study

Task-related reorganization of functional connectivity (FC) has been widely investigated. Under classic static FC analysis, brain networks under task and rest have been demonstrated a general similarity. However, brain activity and cognitive process are believed to be dynamic and adaptive. Since static FC inherently ignores the distinct temporal patterns between rest and task, dynamic FC may be more a suitable technique to characterize the brain’s dynamic and adaptive activities. In this study, we adopted [Formula: see text]-means clustering to investigate task-related spatiotemporal reorganization of dynamic brain networks and hypothesized that dynamic FC would be able to reveal the link between resting-state and task-state brain organization, including broadly similar spatial patterns but distinct temporal patterns. In order to test this hypothesis, this study examined the dynamic FC in default-mode network (DMN) and motor-related network (MN) using Blood-Oxygenation-Level-Dependent (BOLD)-fMRI data from 26 healthy subjects during rest (REST) and a hand closing-and-opening (HCO) task. Two principal FC states in REST and one principal FC state in HCO were identified. The first principal FC state in REST was found similar to that in HCO, which appeared to represent intrinsic network architecture and validated the broadly similar spatial patterns between REST and HCO. However, the second FC principal state in REST with much shorter “dwell time” implied the transient functional relationship between DMN and MN during REST. In addition, a more frequent shifting between two principal FC states indicated that brain network dynamically maintained a “default mode” in the motor system during REST, whereas the presence of a single principal FC state and reduced FC variability implied a more temporally stable connectivity during HCO, validating the distinct temporal patterns between REST and HCO. Our results further demonstrated that dynamic FC analysis could offer unique insights in understanding how the brain reorganizes itself during rest and task states, and the ways in which the brain adaptively responds to the cognitive requirements of tasks.

The right touch: Stroking of CT-innervated skin promotes vocal emotion processing

Research has revealed a special mechanoreceptor, called C-tactile (CT) afferent, that is situated in hairy skin and that seems relevant for the processing of social touch. We pursued a possible role of this receptor in the perception of other social signals such as a person’s voice. Participants completed three sessions in which they heard surprised and neutral vocal and nonvocal sounds and detected rare sound repetitions. In a given session, participants received no touch or soft brushstrokes to the arm (CT innervated) or palm (CT free). Event-related potentials elicited to sounds revealed that stroking to the arm facilitated the integration of vocal and emotional information. The late positive potential was greater for surprised vocal relative to neutral vocal and nonvocal sounds, and this effect was greater for arm touch relative to both palm touch and no touch. Together, these results indicate that stroking to the arm facilitates the allocation of processing resources to emotional voices, thus supporting the possibility that CT stimulation benefits social perception cross-modally.

Electrophysiological signatures of atypical intrinsic brain connectivity networks in autism

OBJECTIVE

Abnormal local and long-range brain connectivity have been widely reported in autism spectrum disorder (ASD), yet the nature of these abnormalities and their functional relevance at distinct cortical rhythms remains unknown. Investigations of intrinsic connectivity networks (ICNs) and their coherence across whole brain networks hold promise for determining whether patterns of functional connectivity abnormalities vary across frequencies and networks in ASD. In the present study, we aimed to probe atypical intrinsic brain connectivity networks in ASD from resting-state electroencephalography (EEG) data via characterizing the whole brain network.

APPROACH

Connectivity within individual ICNs (measured by spectral power) and between ICNs (measured by coherence) were examined at four canonical frequency bands via a time-frequency independent component analysis on high-density EEG, which were recorded from 20 ASD and 20 typical developing (TD) subjects during an eyes-closed resting state.

MAIN RESULTS

Among twelve identified electrophysiological ICNs, individuals with ASD showed hyper-connectivity in individual ICNs and hypo-connectivity between ICNs. Functional connectivity alterations in ASD were more severe in the frontal lobe and the default mode network (DMN) and at low frequency bands. These functional connectivity measures also showed abnormal age-related associations in ICNs related to frontal, temporal and motor regions in ASD.

SIGNIFICANCE

Our findings suggest that ASD is characterized by the opposite directions of abnormalities (i.e. hypo- and hyper-connectivity) in the hierarchical structure of the whole brain network, with more impairments in the frontal lobe and the DMN at low frequency bands, which are critical for top-down control of sensory systems, as well as for both cognition and social skills.

Attentional ERPs distinguish aging and early Alzheimer's dementia

The early detection of Alzheimer's disease requires our distinguishing it from cognitive aging. Here, we test whether spatial attentional changes might support that distinction. We engaged young normal (YN), older normal (ON), and patients with early Alzheimer's dementia (EAD) in an attentionally cued, self-movement heading discrimination task while we recorded push-button response times and event related potentials. YNs and ONs show the behavioral effects of attentional shifts from the cue to the target, whereas EAD patients did not (p < 0.001). YNs and ONs also show the shifting lateralization of a newly described attentional event related potentials component, whereas EAD patients did not (p < 0.001). Our findings suggest that spatial inattention in EAD patients may contribute to heading direction processing impairments that distinguish them from ONs and undermine their navigational capacity and driving safety.

Magnitude and Temporal Variability of Inter-stimulus EEG Modulate the Linear Relationship Between Laser-Evoked Potentials and Fast-Pain Perception

The level of pain perception is correlated with the magnitude of pain-evoked brain responses, such as laser-evoked potentials (LEP), across trials. The positive LEP-pain relationship lays the foundation for pain prediction based on single-trial LEP, but cross-individual pain prediction does not have a good performance because the LEP-pain relationship exhibits substantial cross-individual difference. In this study, we aim to explain the cross-individual difference in the LEP-pain relationship using inter-stimulus EEG (isEEG) features. The isEEG features (root mean square as magnitude and mean square successive difference as temporal variability) were estimated from isEEG data (at full band and five frequency bands) recorded between painful stimuli. A linear model was fitted to investigate the relationship between pain ratings and LEP response for fast-pain trials on a trial-by-trial basis. Then the correlation between isEEG features and the parameters of LEP-pain model (slope and intercept) was evaluated. We found that the magnitude and temporal variability of isEEG could modulate the parameters of an individual's linear LEP-pain model for fast-pain trials. Based on this, we further developed a new individualized fast-pain prediction scheme, which only used training individuals with similar isEEG features as the test individual to train the fast-pain prediction model, and obtained improved accuracy in cross-individual fast-pain prediction. The findings could help elucidate the neural mechanism of cross-individual difference in pain experience and the proposed fast-pain prediction scheme could be potentially used as a practical and feasible pain prediction method in clinical practice.

Common Interferences Removal from Dense Multichannel EEG Using Independent Component Decomposition

To improve the spatial resolution, dense multichannel electroencephalogram with more than 32 leads has gained more and more applications. However, strong common interference will not only conceal the weak components generated from the specific isolated neural source, but also lead to severe spurious correlation between different brain regions, which results in great distortion on brain connectivity or brain network analysis. Starting from the fast independent component analysis algorithm, we first derive the mixing matrix of independent source components based on the baseline signals prior to tasks. Then, we identify the common interferences as those components whose mixing vectors span the minimum angles with respect to the unitary vector. By assuming that both the common interferences and their corresponding mixing vectors stay consistent during the entire experiment, we apply the demixing and mixing matrix to the task signals and remove the inferred common interferences. Subsequently, we validate the method using simulation. Finally, the index of global coherence is calculated for validation. It turns out that the proposed method can successfully remove the common interferences so that the prominent coherence of mu rhythms in motor imagery tasks is unmasked. The proposed method can gain wide applications because it reveals the true correlation between the local sources in spite of the low signal-to-noise ratio.

Trait related sensorimotor deficits in people who stutter: An EEG investigation of μ rhythm dynamics during spontaneous fluency

Stuttering is associated with compromised sensorimotor control (i.e., internal modeling) across the dorsal stream and oscillations of EEG mu (μ) rhythms have been proposed as reliable indices of anterior dorsal stream processing. The purpose of this study was to compare μ rhythm oscillatory activity between (PWS) and matched typically fluent speakers (TFS) during spontaneously fluent overt and covert speech production tasks. Independent component analysis identified bilateral μ components from 24/27 PWS and matched TFS that localized over premotor cortex. Time-frequency analysis of the left hemisphere μ clusters demonstrated significantly reduced μ-α and μ-β ERD (pCLUSTER < 0.05) in PWS across the time course of overt and covert speech production, while no group differences were found in the right hemisphere in any condition. Results were interpreted through the framework of State Feedback Control. They suggest that weak forward modeling and evaluation of sensory feedback across the time course of speech production characterizes the trait related sensorimotor impairment in PWS. This weakness is proposed to represent an underlying sensorimotor instability that may predispose the speech of PWS to breakdown.

Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior

Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or “components” derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.

Specificity of spontaneous EEG associated with different levels of cognitive and communicative dysfunctions in children

This study aimed to reveal electrophysiological markers of communicative and cognitive dysfunctions of different severity in children with autism spectrum disorder (ASD). Eyes-opened electroencephalograms (EEGs) of 42 children with ASD, divided into two groups according to the severity of their communicative and cognitive dysfunctions (24 with severe and 18 children with less severe ASD), and 70 age-matched controls aged 4-9 years were examined by means of spectral and group independent component (gIC) analyses. A predominance of theta and beta EEG activity in both groups of children with ASD compared to the activity in the control group was found in the global gIC together with a predominance of beta EEG activity in the right occipital region. The quantity of local gICs with enhanced slow and high-frequency EEG activity (within the frontal, temporal, and parietal cortex areas) in children 4-9 years of age might be considered a marker of cognitive and communicative dysfunction severity.

Artifact Rejection Methodology Enables Continuous, Noninvasive Measurement of Gastric Myoelectric Activity in Ambulatory Subjects

The increasing prevalence of functional and motility gastrointestinal (GI) disorders is at odds with bottlenecks in their diagnosis, treatment, and follow-up. Lack of noninvasive approaches means that only specialized centers can perform objective assessment procedures. Abnormal GI muscular activity, which is coordinated by electrical slow-waves, may play a key role in symptoms. As such, the electrogastrogram (EGG), a noninvasive means to continuously monitor gastric electrical activity, can be used to inform diagnoses over broader populations. However, it is seldom used due to technical issues: inconsistent results from single-channel measurements and signal artifacts that make interpretation difficult and limit prolonged monitoring. Here, we overcome these limitations with a wearable multi-channel system and artifact removal signal processing methods. Our approach yields an increase of 0.56 in the mean correlation coefficient between EGG and the clinical "gold standard", gastric manometry, across 11 subjects (p < 0.001). We also demonstrate this system's usage for ambulatory monitoring, which reveals myoelectric dynamics in response to meals akin to gastric emptying patterns and circadian-related oscillations. Our approach is noninvasive, easy to administer, and has promise to widen the scope of populations with GI disorders for which clinicians can screen patients, diagnose disorders, and refine treatments objectively.

Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition

Independent Component Analysis (ICA) has proven to be an effective data driven method for analyzing EEG data, separating signals from temporally and functionally independent brain and non-brain source processes and thereby increasing their definition. Dimension reduction by Principal Component Analysis (PCA) has often been recommended before ICA decomposition of EEG data, both to minimize the amount of required data and computation time. Here we compared ICA decompositions of fourteen 72-channel single subject EEG data sets obtained (i) after applying preliminary dimension reduction by PCA, (ii) after applying no such dimension reduction, or else (iii) applying PCA only. Reducing the data rank by PCA (even to remove only 1% of data variance) adversely affected both the numbers of dipolar independent components (ICs) and their stability under repeated decomposition. For example, decomposing a principal subspace retaining 95% of original data variance reduced the mean number of recovered 'dipolar' ICs from 30 to 10 per data set and reduced median IC stability from 90% to 76%. PCA rank reduction also decreased the numbers of near-equivalent ICs across subjects. For instance, decomposing a principal subspace retaining 95% of data variance reduced the number of subjects represented in an IC cluster accounting for frontal midline theta activity from 11 to 5. PCA rank reduction also increased uncertainty in the equivalent dipole positions and spectra of the IC brain effective sources. These results suggest that when applying ICA decomposition to EEG data, PCA rank reduction should best be avoided.

Assessing Brain-Muscle Connectivity in Human Locomotion through Mobile Brain/Body Imaging: Opportunities, Pitfalls, and Future Directions

Assessment of the cortical role during bipedalism has been a methodological challenge. While surface electroencephalography (EEG) is capable of non-invasively measuring cortical activity during human locomotion, it is associated with movement artifacts obscuring cerebral sources of activity. Recently, statistical methods based on blind source separation revealed potential for resolving this issue, by segregating non-cerebral/artifactual from cerebral sources of activity. This step marked a new opportunity for the investigation of the brains' role while moving and was tagged mobile brain/body imaging (MoBI). This methodology involves simultaneous mobile recording of brain activity with several other body behavioral variables (e.g., muscle activity and kinematics), through wireless recording wearable devices/sensors. Notably, several MoBI studies using EEG-EMG approaches recently showed that the brain is functionally connected to the muscles and active throughout the whole gait cycle and, thus, rejecting the long-lasting idea of a solely spinal-driven bipedalism. However, MoBI and brain/muscle connectivity assessments during human locomotion are still in their fledgling state of investigation. Mobile brain/body imaging approaches hint toward promising opportunities; however, there are some remaining pitfalls that need to be resolved before considering their routine clinical use. This article discusses several of these pitfalls and proposes research to address them. Examples relate to the validity, reliability, and reproducibility of this method in ecologically valid scenarios and in different populations. Furthermore, whether brain/muscle connectivity within the MoBI framework represents a potential biomarker in neuromuscular syndromes where gait disturbances are evident (e.g., age-related sarcopenia) remains to be determined.

A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings

Background

EEG may be affected by artefacts hindering the analysis of brain signals. Data-driven methods like independent component analysis (ICA) are successful approaches to remove artefacts from the EEG. However, the ICA-based methods developed so far are often affected by limitations, such as: the need for visual inspection of the separated independent components (subjectivity problem) and, in some cases, for the independent and simultaneous recording of the inspected artefacts to identify the artefactual independent components; a potentially heavy manipulation of the EEG signals; the use of linear classification methods; the use of simulated artefacts to validate the methods; no testing in dry electrode or high-density EEG datasets; applications limited to specific conditions and electrode layouts.

Methods

Our fingerprint method automatically identifies EEG ICs containing eyeblinks, eye movements, myogenic artefacts and cardiac interference by evaluating 14 temporal, spatial, spectral, and statistical features composing the IC fingerprint. Sixty-two real EEG datasets containing cued artefacts are recorded with wet and dry electrodes (128 wet and 97 dry channels). For each artefact, 10 nonlinear SVM classifiers are trained on fingerprints of expert-classified ICs. Training groups include randomly chosen wet and dry datasets decomposed in 80 ICs. The classifiers are tested on the IC-fingerprints of different datasets decomposed into 20, 50, or 80 ICs. The SVM performance is assessed in terms of accuracy, False Omission Rate (FOR), Hit Rate (HR), False Alarm Rate (FAR), and sensitivity (p). For each artefact, the quality of the artefact-free EEG reconstructed using the classification of the best SVM is assessed by visual inspection and SNR.

Results

The best SVM classifier for each artefact type achieved average accuracy of 1 (eyeblink), 0.98 (cardiac interference), and 0.97 (eye movement and myogenic artefact). Average classification sensitivity (p) was 1 (eyeblink), 0.997 (myogenic artefact), 0.98 (eye movement), and 0.48 (cardiac interference). Average artefact reduction ranged from a maximum of 82% for eyeblinks to a minimum of 33% for cardiac interference, depending on the effectiveness of the proposed method and the amplitude of the removed artefact. The performance of the SVM classifiers did not depend on the electrode type, whereas it was better for lower decomposition levels (50 and 20 ICs).

Discussion

Apart from cardiac interference, SVM performance and average artefact reduction indicate that the fingerprint method has an excellent overall performance in the automatic detection of eyeblinks, eye movements and myogenic artefacts, which is comparable to that of existing methods. Being also independent from simultaneous artefact recording, electrode number, type and layout, and decomposition level, the proposed fingerprint method can have useful applications in clinical and experimental EEG settings.

Perceptual Surprise Improves Action Stopping by Nonselectively Suppressing Motor Activity via a Neural Mechanism for Motor Inhibition

Motor inhibition is a cognitive control ability that allows humans to stop actions rapidly even after initiation. Understanding and improving motor inhibition could benefit adaptive behavior in both health and disease. We recently found that presenting surprising, task-unrelated sounds when stopping is necessary improves the likelihood of successful stopping. In the current study, we investigated the neural underpinnings of this effect. Specifically, we tested whether surprise-related stopping improvements are due to a genuine increase in motor inhibition. In Experiment 1, we measured motor inhibition in primary motor cortex of male and female humans by quantifying corticospinal excitability (CSE) via transcranial magnetic stimulation and electromyography during a hybrid surprise-Go/NoGo task. Consistent with prior studies of motor inhibition, successful stopping was accompanied by nonselective suppression of CSE; that is, CSE was suppressed even in task-unrelated motor effectors. Importantly, unexpected sounds significantly increased this motor-system inhibition to a degree that was directly related to behavioral improvements in stopping. In Experiment 2, we then used scalp encephalography to investigate whether unexpected sounds increase motor-inhibition-related activity in the CNS. We used an independent stop-signal localizer task to identify a well characterized frontocentral low-frequency EEG component that indexes motor inhibition. We then investigated the activity of this component in the surprise-Go/NoGo task. Consistent with Experiment 1, this signature of motor inhibition was indeed increased when NoGo signals were followed by unexpected sounds. Together, these experiments provide converging evidence suggesting that unexpected events improve motor inhibition by automatically triggering inhibitory control.SIGNIFICANCE STATEMENT The ability to stop ongoing actions rapidly allows humans to adapt their behavior flexibly and rapidly. Action stopping is important in daily life (e.g., stopping to cross the street when a car approaches) and is severely impaired in many neuropsychiatric disorders. Therefore, finding ways to improve action stopping could aid adaptive behaviors in health and disease. Our current study shows that presenting unexpected sounds in stopping situations facilitates successful stopping. This improvement is specifically due to a surprise-related increase in a neural mechanism for motor inhibition, which rapidly suppresses the excitability of the motor system after unexpected events. These findings suggest a tight interaction between the neural systems for surprise processing and motor inhibition and yield a promising avenue for future research.

Neural Oscillation Correlates Chemistry Decision-Making

This study explored the electroencephalography (EEG) dynamics during a chemistry-related decision-making task and further examined whether the correctness of the decision-making performance could be reflected by EEG activity. A total of 66 undergraduate students’ EEG were collected while they participated in a chemistry-related decision-making task in which they had to retrieve the relevant chemistry concepts in order to make correct decisions for each task item. The results showed that it was only in the anterior cingulate cortex (ACC) cluster that distinct patterns in EEG dynamics were displayed for the correct and incorrect responses. The logistic regression results indicated that ACC theta power from 300[Formula: see text]ms to 250[Formula: see text]ms before stimulus onset was the most informative factor for estimating the likelihood of making correct decisions in the chemistry-related decision-making task, while it was the ACC low beta power from 150[Formula: see text]ms to 250[Formula: see text]ms after stimulus onset. The results suggested that the ACC theta augmentation before the stimulus onset serves to actively maintain the relevant information for retrieval from long-term memory, while the ACC low beta augmentation after the stimulus onset may serve the function of mapping the encoded stimulus onto the relevant criteria that the given participant has held within his or her mind to guide the decision-making responses.

ARTIST: A fully automated artifact rejection algorithm for single-pulse TMS-EEG data

Concurrent single-pulse TMS-EEG (spTMS-EEG) is an emerging noninvasive tool for probing causal brain dynamics in humans. However, in addition to the common artifacts in standard EEG data, spTMS-EEG data suffer from enormous stimulation-induced artifacts, posing significant challenges to the extraction of neural information. Typically, neural signals are analyzed after a manual time-intensive and often subjective process of artifact rejection. Here we describe a fully automated algorithm for spTMS-EEG artifact rejection. A key step of this algorithm is to decompose the spTMS-EEG data into statistically independent components (ICs), and then train a pattern classifier to automatically identify artifact components based on knowledge of the spatio-temporal profile of both neural and artefactual activities. The autocleaned and hand-cleaned data yield qualitatively similar group evoked potential waveforms. The algorithm achieves a 95% IC classification accuracy referenced to expert artifact rejection performance, and does so across a large number of spTMS-EEG data sets (n = 90 stimulation sites), retains high accuracy across stimulation sites/subjects/populations/montages, and outperforms current automated algorithms. Moreover, the algorithm was superior to the artifact rejection performance of relatively novice individuals, who would be the likely users of spTMS-EEG as the technique becomes more broadly disseminated. In summary, our algorithm provides an automated, fast, objective, and accurate method for cleaning spTMS-EEG data, which can increase the utility of TMS-EEG in both clinical and basic neuroscience settings.

Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis

OBJECTIVE

This study proposes and evaluates a novel data-driven spatial filtering approach for enhancing steady-state visual evoked potentials (SSVEPs) detection toward a high-speed brain-computer interface (BCI) speller.

METHODS

Task-related component analysis (TRCA), which can enhance reproducibility of SSVEPs across multiple trials, was employed to improve the signal-to-noise ratio (SNR) of SSVEP signals by removing background electroencephalographic (EEG) activities. An ensemble method was further developed to integrate TRCA filters corresponding to multiple stimulation frequencies. This study conducted a comparison of BCI performance between the proposed TRCA-based method and an extended canonical correlation analysis (CCA)-based method using a 40-class SSVEP dataset recorded from 12 subjects. An online BCI speller was further implemented using a cue-guided target selection task with 20 subjects and a free-spelling task with 10 of the subjects.

RESULTS

The offline comparison results indicate that the proposed TRCA-based approach can significantly improve the classification accuracy compared with the extended CCA-based method. Furthermore, the online BCI speller achieved averaged information transfer rates (ITRs) of 325.33 ± 38.17 bits/min with the cue-guided task and 198.67 ± 50.48 bits/min with the free-spelling task.

CONCLUSION

This study validated the efficiency of the proposed TRCA-based method in implementing a high-speed SSVEP-based BCI.

SIGNIFICANCE

The high-speed SSVEP-based BCIs using the TRCA method have great potential for various applications in communication and control.

An adaptive orienting theory of error processing

The ability to detect and correct action errors is paramount to safe and efficient goal-directed behaviors. Existing work on the neural underpinnings of error processing and post-error behavioral adaptations has led to the development of several mechanistic theories of error processing. These theories can be roughly grouped into adaptive and maladaptive theories. While adaptive theories propose that errors trigger a cascade of processes that will result in improved behavior after error commission, maladaptive theories hold that error commission momentarily impairs behavior. Neither group of theories can account for all available data, as different empirical studies find both impaired and improved post-error behavior. This article attempts a synthesis between the predictions made by prominent adaptive and maladaptive theories. Specifically, it is proposed that errors invoke a nonspecific cascade of processing that will rapidly interrupt and inhibit ongoing behavior and cognition, as well as orient attention toward the source of the error. It is proposed that this cascade follows all unexpected action outcomes, not just errors. In the case of errors, this cascade is followed by error-specific, controlled processing, which is specifically aimed at (re)tuning the existing task set. This theory combines existing predictions from maladaptive orienting and bottleneck theories with specific neural mechanisms from the wider field of cognitive control, including from error-specific theories of adaptive post-error processing. The article aims to describe the proposed framework and its implications for post-error slowing and post-error accuracy, propose mechanistic neural circuitry for post-error processing, and derive specific hypotheses for future empirical investigations.

Higher similarity in beta topography between tasks than subjects

We have recently provided evidence for highly idiosyncratic topographic distributions of beta oscillations (as well as slow potentials) across individuals. More recently, by emphasizing the analysis of similarity instead of differences across tasks, we concluded that differences between an attention task and quiet resting may be negligible or at least unsystematic across subjects. Due to the possibility that individual differences could be due to noise in a wide sense or some inherent instability of beta activity, we designed a replication study to explicitly test whether pairs of individuals matched for head size and shape would still present less similar beta topography than each individual between sessions or tasks. We used independent component analysis (ICA) for an exhaustive decomposition of beta activity in a visual attention task and in quiet resting, recorded by 256-channel EEG in 20 subjects, on two separate days. We evaluated whether each ICA component obtained in one task and in one given individual could be explained by a linear regression model based on the topographic patterns of the complementary task (correlation between one component with a linear combination of components from complementary conditions), of the same task in a second session and of a matched individual. Results again showed a high topographic similarity between conditions, as previously seen between reasoning and simple visual attention beta correlates. From an overall number of 16 components representing brain activity obtained for the tasks (out of 60 originally computed where the remaining were considered noise), over 92% could satisfactorily be explained by the complementary task. Although the similarity between sessions was significantly smaller than between tasks on each day, the similarity between sessions was statistically higher than that between subjects in a highly significant way. We discuss the possible biases of group spatial averaging and the emphasis on differences as opposed to similarities, and noise in a wide sense, as the main causes of hardly replicable findings on task-related forms of activity and the inconclusive state of a universal functional mapping of cortical association areas.

Automated EEG Artifact Handling With Application in Driver Monitoring

Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic environments are becoming increasingly important in areas such as brain-computer interfaces and behavior science. However, the recorded EEG in such environments is often heavily contaminated by motion artifacts and eye movements. This poses new requirements on artifact handling. The objective of this paper is to present an automated EEG artifacts handling algorithm, which will be used as a preprocessing step in a driver monitoring application. The algorithm, named Automated aRTifacts handling in EEG (ARTE), is based on wavelets, independent component analysis, and hierarchical clustering. The algorithm is tested on a dataset obtained from a driver sleepiness study including 30 drivers and 540 30-min 30-channel EEG recordings. The algorithm is evaluated by a clinical neurophysiologist, by quantitative criteria (signal quality index, mean square error, relative error, and mean absolute error), and by demonstrating its usefulness as a preprocessing step in driver monitoring, here exemplified with driver sleepiness classification. All results are compared with a state-of-the-art algorithm called FORCe. The quantitative and expert evaluation results show that the two algorithms are comparable, and that both algorithms significantly reduce the impact of artifacts in recorded EEG signals. When artifact handling is used as a preprocessing step in driver sleepiness classification, the classification accuracy increased by 5% when using ARTE and by 2% when using FORCe. The advantage with ARTE is that it is data driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered.

Fusing Partial Camera Signals for Noncontact Pulse Rate Variability Measurement

Remote camera-based measurement of physiology has great potential for healthcare and affective computing. Recent advances in computer vision and signal processing have enabled photoplethysmography (PPG) measurement using commercially available cameras. However, there remain challenges in recovering accurate noncontact PPG measurements in the presence of rigid head motion. When a subject is moving, their face may be turned away from one camera, be obscured by an object, or move out of the frame resulting in missing observations. As the calculation of pulse rate variability (PRV) requires analysis over a time window of several minutes, the effect of missing observations on such features is deleterious. We present an approach for fusing partial color-channel signals from an array of cameras that enable physiology measurements to be made from moving subjects, even if they leave the frame of one or more cameras, which would not otherwise be possible with only a single camera. We systematically test our method on subjects ( N=25) using a set of six, 5-min tasks (each repeated twice) involving different levels of head motion. This results in validation across 25 h of measurement. We evaluate pulse rate and PRV parameter estimation including statistical, geometric, and frequency-based measures. The median absolute error in pulse rate measurements was 0.57 beats-per-minute (BPM). In all but two tasks with the greatest motion, the median error was within 0.4 BPM of that from a contact PPG device. PRV estimates were significantly improved using our proposed approach compared to an alternative not designed to handle missing values and multiple camera signals; the error was reduced by over 50%. Without our proposed method, errors in pulse rate would be very high, and estimation of PRV parameters would not be feasible due to significant data loss.

Automatic and robust noise suppression in EEG and MEG: The SOUND algorithm

Electroencephalography (EEG) and magnetoencephalography (MEG) often suffer from noise- and artifact-contaminated channels and trials. Conventionally, EEG and MEG data are inspected visually and cleaned accordingly, e.g., by identifying and rejecting the so-called "bad" channels. This approach has several shortcomings: data inspection is laborious, the rejection criteria are subjective, and the process does not fully utilize all the information in the collected data. Here, we present noise-cleaning methods based on modeling the multi-sensor and multi-trial data. These approaches offer objective, automatic, and robust removal of noise and disturbances by taking into account the sensor- or trial-specific signal-to-noise ratios. We introduce a method called the source-estimate-utilizing noise-discarding algorithm (the SOUND algorithm). SOUND employs anatomical information of the head to cross-validate the data between the sensors. As a result, we are able to identify and suppress noise and artifacts in EEG and MEG. Furthermore, we discuss the theoretical background of SOUND and show that it is a special case of the well-known Wiener estimators. We explain how a completely data-driven Wiener estimator (DDWiener) can be used when no anatomical information is available. DDWiener is easily applicable to any linear multivariate problem; as a demonstrative example, we show how DDWiener can be utilized when estimating event-related EEG/MEG responses. We validated the performance of SOUND with simulations and by applying SOUND to multiple EEG and MEG datasets. SOUND considerably improved the data quality, exceeding the performance of the widely used channel-rejection and interpolation scheme. SOUND also helped in localizing the underlying neural activity by preventing noise from contaminating the source estimates. SOUND can be used to detect and reject noise in functional brain data, enabling improved identification of active brain areas.

Heightened early-attentional stimulus orienting and impulsive action in men with antisocial personality disorder

We tested whether enhanced stimulus orienting operationalized as N1 and P2 auditory evoked potentials to increasing loudness (50-90 dB clicks) could be associated with trait impulsivity (Barratt Impulsiveness Scale, BIS-11), impulsive action (commission error on the Immediate Memory Task), or impulsive choice (immediate responses on temporal discounting tasks). We measured N1 and P2 loudness sensitivity in a passive listening task as linear intensity-sensitivity slopes in 36 men with antisocial personality disorder with a history of conviction for criminal conduct and 16 healthy control men. Across all subjects, regression analyses revealed that a steeper P2 slope predicted higher IMT commission error/correct detection ratio, and lower stimulus discriminability (A-prime). These associations were also found within both groups. These relationships suggest an association between enhanced early stimulus orienting (P2), impulsive action (response inhibition), and impaired signal-noise discriminability (A-prime).

Acute ethanol and taurine intake affect absolute alpha power in frontal cortex before and after exercise

Taurine and alcohol has been popularly ingested through energy drinks. Reports from both compounds shows they are active on nervous system but little is known about the acute effect of these substances on the frontal cortex in an exercise approach. The aim of this study was to determine the effects of 0,6mldL^-1 of ethanol (ET), 6g of taurine (TA), and taurine with ethanol (TA+ET) intake on absolute alpha power (AAP) in the frontal region, before and after exercise. Nine participants were recruited, five women (22±3years) and four men (26±5years), for a counterbalanced experimental design. For each treatment, the tests were performed considering three moments: "baseline", "peak" and "post-exercise". In the placebo treatment (PL), the frontal areas showed AAP decrease at the post-exercise. However, in the TA, AAP decreased at peak and increased at post-exercise. In the ET treatment, AAP increased at the peak moment for the left frontal electrodes. In the TA+ET treatment, an AAP increase was observed at peak, and it continued after exercise ended. These substances were able to produce electrocortical activity changes in the frontal regions after a short duration and low intensity exercise. Left and right regions showed different AAP dynamics during peak and post-exercise moments when treatments were compared.

Establishing a Right Frontal Beta Signature for Stopping Action in Scalp EEG: Implications for Testing Inhibitory Control in Other Task Contexts

Many studies have examined the rapid stopping of action as a proxy of human self-control. Several methods have shown that a critical focus for stopping is the right inferior frontal cortex. Moreover, electrocorticography studies have shown beta band power increases in the right inferior frontal cortex and in the BG for successful versus failed stop trials, before the time of stopping elapses, perhaps underpinning a prefrontal-BG network for inhibitory control. Here, we tested whether the same signature might be visible in scalp electroencephalography (EEG)-which would open important avenues for using this signature in studies of the recruitment and timing of prefrontal inhibitory control. We used independent component analysis and time-frequency approaches to analyze EEG from three different cohorts of healthy young volunteers (48 participants in total) performing versions of the standard stop signal task. We identified a spectral power increase in the band 13-20 Hz that occurs after the stop signal, but before the time of stopping elapses, with a right frontal topography in the EEG. This right frontal beta band increase was significantly larger for successful compared with failed stops in two of the three studies. We also tested the hypothesis that unexpected events recruit the same frontal system for stopping. Indeed, we show that the stopping-related right-lateralized frontal beta signature was also active after unexpected events (and we accordingly provide data and scripts for the method). These results validate a right frontal beta signature in the EEG as a temporally precise and functionally significant neural marker of the response inhibition process.