EMG contamination of EEG: spectral and topographical characteristics

Differential analgesic effects of high-frequency or accelerated intermittent theta burst stimulation of M1 on experimental tonic pain: Correlations with cortical activity changes assessed by TMS-EEG

Accelerated intermittent theta burst stimulation (AiTBS) has attracted much attention in the past few years as a new form of brain stimulation paradigm. However, it is unclear the relative efficacy of AiTBS on cortical excitability compared to conventional high-frequency rTMS. Using concurrent TMS and electroencephalogram (TMS-EEG), this study systematically compared the efficacy on cortical excitability and a typical clinical application (i.e. pain), between AiTBS with different intersession interval (ISIs) and 10-Hz rTMS. Participants received 10-Hz rTMS, AiTBS-15 (3 iTBS sessions with a 15-min ISI), AiTBS-50 (3 iTBS sessions with a 50-min ISI), or Sham stimulation over the primary motor cortex on four separate days. All four protocols included a total of 1800 pulses but with different session durations (10-Hz rTMS ​= ​18, AiTBS-15 ​= ​40, and AiTBS-50 ​= ​110 ​min). AiTBS-50 and 10-Hz rTMS were more effective in pain reduction compared to AiTBS-15. Using single-pulse TMS-induced oscillation, our data revealed low gamma oscillation as a shared cortical excitability change across all three active rTMS protocols but demonstrated completely opposite directions. Changes in low gamma oscillation were further associated with changes in pain perception across the three active conditions. In contrast, a distinct pattern of TMS-evoked potentials (TEPs) was revealed, with 10-Hz rTMS decreasing inhibitory N100 amplitude and AiTBS-15 reducing excitatory P60 amplitude. These changes in TEPs were also covarying with low gamma power changes. Sham stimulation indicated no significant effect on either cortical excitability or pain perception. These results are relevant only for provoked experimental pain, without being predictive for chronic pain, and revealed a change in low gamma oscillation, particularly around the very particular frequency of 40 ​Hz, shared between AiTBS and high-frequency rTMS. Conversely, cortical excitability (balance between excitation and inhibition) assessed by TEP recording was modulated differently by AiTBS and high-frequency rTMS paradigms.

Markers of too little effort or too much alertness during neuropsychological assessment: Demonstration with perioperative changes

OBJECTIVE

Cognitive assessment is based on performance in different tests. However, this performance might be hindered by lack of effective effort on the one hand, and by too much stress on the other hand. Despite their known impact, there are currently no effective tools for measuring cognitive effort or stress effect during cognitive assessment. We developed real-time electrophysiological markers for cognitive effort and for stress effect, which could be used during cognitive assessment.

METHODS

We assessed these markers during the use of the Montreal Cognitive Assessment (MoCA) before and after cardiac surgery, which is known to involve cognitive decline in up to 30%-50% of elderly patients.

RESULTS

The major findings of the study, for the largest group of patients, with preoperative MoCA in the intermediate range, were that the decline is significantly associated (1) with higher preoperative cognitive effort and (2) with higher postoperative stress effect during the test.

CONCLUSIONS

These findings, as well as preliminary additional ones, suggest a potential importance for monitoring cognitive effort and stress effect during assessment in general, and specifically during perioperative assessment.

SIGNIFICANCE

Easy-to-use markers could improve the efficacy of cognitive assessment and direct treatment generally, and specifically for perioperative decline.

Electroencephalographic Correlates of Clinical Severity in the Natural history study of RTT and Related Disorders

OBJECTIVE

This study was undertaken to characterize quantitative electroencephalographic (EEG) features in participants from the Natural history study of RTT and Related Disorders and to assess the potential for these features to act as objective measures of cortical function for Rett syndrome (RTT).

METHODS

EEG amplitude and power features were derived from the resting EEG of 60 females with RTT (median age = 10.7 years) and 26 neurotypical females (median age = 10.6 years). Analyses focus on group differences and within the RTT group, associations between the EEG parameters and clinical severity. For a subset of participants (n = 20), follow-up data were available for assessing the reproducibility of the results and the stability in the parameters over 1 year.

RESULTS

Compared to neurotypical participants, participants with RTT had greater amplitude variability and greater low-frequency activity as reflected by greater delta power, more negative 1/f slope, and lower theta/delta, alpha/delta, beta/delta, alpha/theta, and beta/theta ratios. Greater delta power, more negative 1/f slope, and lower power ratios were associated with greater severity. Analyses of year 1 data replicated the associations between 1/f slope and power ratios and clinical severity and demonstrated good within-subject consistency in these measures.

INTERPRETATION

Overall, group comparisons reflected a greater predominance of lower versus higher frequency activity in participants with RTT, which is consistent with prior clinical interpretations of resting EEG in this population. The observed associations between the EEG power measures and clinical assessments and the repeatability of these measures underscore the potential for EEG to provide an objective measure of cortical function and clinical severity for RTT. ANN NEUROL 2024;96:175-186.

Probing a neural unreliability account of auditory sensory processing atypicalities in Rett Syndrome

BACKGROUND

In the search for objective tools to quantify neural function in Rett Syndrome (RTT), which are crucial in the evaluation of therapeutic efficacy in clinical trials, recordings of sensory-perceptual functioning using event-related potential (ERP) approaches have emerged as potentially powerful tools. Considerable work points to highly anomalous auditory evoked potentials (AEPs) in RTT. However, an assumption of the typical signal-averaging method used to derive these measures is "stationarity" of the underlying responses - i.e. neural responses to each input are highly stereotyped. An alternate possibility is that responses to repeated stimuli are highly variable in RTT. If so, this will significantly impact the validity of assumptions about underlying neural dysfunction, and likely lead to overestimation of underlying neuropathology. To assess this possibility, analyses at the single-trial level assessing signal-to-noise ratios (SNR), inter-trial variability (ITV) and inter-trial phase coherence (ITPC) are necessary.

METHODS

AEPs were recorded to simple 100 Hz tones from 18 RTT and 27 age-matched controls (Ages: 6-22 years). We applied standard AEP averaging, as well as measures of neuronal reliability at the single-trial level (i.e. SNR, ITV, ITPC). To separate signal-carrying components from non-neural noise sources, we also applied a denoising source separation (DSS) algorithm and then repeated the reliability measures.

RESULTS

Substantially increased ITV, lower SNRs, and reduced ITPC were observed in auditory responses of RTT participants, supporting a "neural unreliability" account. Application of the DSS technique made it clear that non-neural noise sources contribute to overestimation of the extent of processing deficits in RTT. Post-DSS, ITV measures were substantially reduced, so much so that pre-DSS ITV differences between RTT and TD populations were no longer detected. In the case of SNR and ITPC, DSS substantially improved these estimates in the RTT population, but robust differences between RTT and TD were still fully evident.

CONCLUSIONS

To accurately represent the degree of neural dysfunction in RTT using the ERP technique, a consideration of response reliability at the single-trial level is highly advised. Non-neural sources of noise lead to overestimation of the degree of pathological processing in RTT, and denoising source separation techniques during signal processing substantially ameliorate this issue.

Substance-dependent EEG during recovery from anesthesia and optimization of monitoring

The electroencephalographic (EEG) activity during anesthesia emergence contains information about the risk for a patient to experience postoperative delirium, but the EEG dynamics during emergence challenge monitoring approaches. Substance-specific emergence characteristics may additionally limit the reliability of commonly used processed EEG indices during emergence. This study aims to analyze the dynamics of different EEG indices during anesthesia emergence that was maintained with different anesthetic regimens. We used the EEG of 45 patients under general anesthesia from the emergence period. Fifteen patients per group received sevoflurane, isoflurane (+ sufentanil) or propofol (+ remifentanil) anesthesia. One channel EEG and the bispectral index (BIS A-1000) were recorded during the study. We replayed the EEG back to the Conox, Entropy Module, and the BIS Vista to evaluate and compare the index behavior. The volatile anesthetics induced significantly higher EEG frequencies, causing higher indices (AUC > 0.7) over most parts of emergence compared to propofol. The median duration of "awake" indices (i.e., > 80) before the return of responsiveness (RoR) was significantly longer for the volatile anesthetics (p < 0.001). The different indices correlated well under volatile anesthesia (rs > 0.6), with SE having the weakest correlation. For propofol, the correlation was lower (rs < 0.6). SE was significantly higher than BIS and, under propofol anesthesia, qCON. Systematic differences of EEG-based indices depend on the drugs and devices used. Thus, to avoid early awareness or anesthesia overdose using an EEG-based index during emergence, the anesthetic regimen, the monitor used, and the raw EEG trace should be considered for interpretation before making clinical decisions.

The Influence of Electromyographic on Electroencephalogram-Based Monitoring: Putting the Forearm on the Forehead

BACKGROUND

Monitoring the electroencephalogram (EEG) during general anesthesia can help to safely navigate the patient through the procedure by avoiding too deep or light anesthetic levels. In daily clinical practice, the EEG is recorded from the forehead and available neuromonitoring systems translate the EEG information into an index inversely correlating with the anesthetic level. Electrode placement on the forehead can lead to an influence of electromyographic (EMG) activity on the recorded signal in patients without neuromuscular blockade (NMB). A separation of EEG and EMG in the clinical setting is difficult because both signals share an overlapping frequency range. Previous research showed that indices decreased when EMG was absent in awake volunteers with NMB. Here, we investigated to what extent the indices changed, when EEG recorded during surgery with NMB agents was superimposed with EMG.

METHODS

We recorded EMG from the flexor muscles of the forearm of 18 healthy volunteers with a CONOX monitor during different activity settings, that is, during contraction using a grip strengthener and during active diversion (relaxed arm). Both the forehead and forearm muscles are striated muscles. The recorded EMG was normalized by z -scoring and added to the EEG in different amplification steps. The EEG was recorded during anesthesia with NMB. We replayed these combined EEG and EMG signals to different neuromonitoring systems, that is, bispectral index (BIS), CONOX with qCON and qNOX, and entropy module with state entropy (SE) and response entropy (RE). We used the Friedman test and a Tukey-Kramer post hoc correction for statistical analysis.

RESULTS

The indices of all neuromonitoring systems significantly increased when the EEG was superimposed with the contraction EMG and with high EMG amplitudes, the monitors returned invalid values, representative of artifact contamination. When replaying the EEG being superimposed with "relaxed" EMG, the qCON and BIS showed significant increases, but not SE and RE. For SE and RE, we observed an increased number of invalid values.

CONCLUSIONS

With our approach, we could show that EMG activity during contraction and resting state can influence the neuromonitoring systems. This knowledge may help to improve EEG-based patient monitoring in the future and help the anesthesiologist to use the neuromonitoring systems with more knowledge regarding their function.

Consistent movement of viewers' facial keypoints while watching emotionally evocative videos

Neuropsychological research aims to unravel how diverse individuals' brains exhibit similar functionality when exposed to the same stimuli. The evocation of consistent responses when different subjects watch the same emotionally evocative stimulus has been observed through modalities like fMRI, EEG, physiological signals and facial expressions. We refer to the quantification of these shared consistent signals across subjects at each time instant across the temporal dimension as Consistent Response Measurement (CRM). CRM is widely explored through fMRI, occasionally with EEG, physiological signals and facial expressions using metrics like Inter-Subject Correlation (ISC). However, fMRI tools are expensive and constrained, while EEG and physiological signals are prone to facial artifacts and environmental conditions (such as temperature, humidity, and health condition of subjects). In this research, facial expression videos are used as a cost-effective and flexible alternative for CRM, minimally affected by external conditions. By employing computer vision-based automated facial keypoint tracking, a new metric similar to ISC, called the Average t-statistic, is introduced. Unlike existing facial expression-based methodologies that measure CRM of secondary indicators like inferred emotions, keypoint, and ICA-based features, the Average t-statistic is closely associated with the direct measurement of consistent facial muscle movement using the Facial Action Coding System (FACS). This is evidenced in DISFA dataset where the time-series of Average t-statistic has a high correlation (R2 = 0.78) with a metric called AU consistency, which directly measures facial muscle movement through FACS coding of video frames. The simplicity of recording facial expressions with the automated Average t-statistic expands the applications of CRM such as measuring engagement in online learning, customer interactions, etc., and diagnosing outliers in healthcare conditions like stroke, autism, depression, etc. To promote further research, we have made the code repository publicly available.

Real-Time Classification of Anxiety in Virtual Reality Therapy Using Biosensors and a Convolutional Neural Network

Virtual Reality Exposure Therapy is a method of cognitive behavioural therapy that aids in the treatment of anxiety disorders by making therapy practical and cost-efficient. It also allows for the seamless tailoring of the therapy by using objective, continuous feedback. This feedback can be obtained using biosensors to collect physiological information such as heart rate, electrodermal activity and frontal brain activity. As part of developing our objective feedback framework, we developed a Virtual Reality adaptation of the well-established emotional Stroop Colour-Word Task. We used this adaptation to differentiate three distinct levels of anxiety: no anxiety, mild anxiety and severe anxiety. We tested our environment on twenty-nine participants between the ages of eighteen and sixty-five. After analysing and validating this environment, we used it to create a dataset for further machine-learning classification of the assigned anxiety levels. To apply this information in real-time, all of our information was processed within Virtual Reality. Our Convolutional Neural Network was able to differentiate the anxiety levels with a 75% accuracy using leave-one-out cross-validation. This shows that our system can accurately differentiate between different anxiety levels.

Investigation of electro-vascular phase-amplitude coupling during an auditory task

Multimodal neuroimaging using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) provides complementary views of cortical processes, including those related to auditory processing. However, current multimodal approaches often overlook potential insights that can be gained from nonlinear interactions between electrical and hemodynamic signals. Here, we explore electro-vascular phase-amplitude coupling (PAC) between low-frequency hemodynamic and high-frequency electrical oscillations during an auditory task. We further apply a temporally embedded canonical correlation analysis (tCCA)-general linear model (GLM)-based correction approach to reduce the possible effect of systemic physiology on fNIRS recordings. Before correction, we observed significant PAC between fNIRS and broadband EEG in the frontal region (p ≪ 0.05), β (p ≪ 0.05) and γ (p = 0.010) in the left temporal/temporoparietal (left auditory; LA) region, and γ (p = 0.032) in the right temporal/temporoparietal (right auditory; RA) region across the entire dataset. Significant differences in PAC across conditions (task versus silence) were observed in LA (p = 0.023) and RA (p = 0.049) γ sub-bands and in lower frequency (5-20 Hz) frontal activity (p = 0.005). After correction, significant fNIRS-γ-band PAC was observed in the frontal (p = 0.021) and LA (p = 0.025) regions, while fNIRS-α (p = 0.003) and fNIRS-β (p = 0.041) PAC were observed in RA. Decreased frontal γ-band (p = 0.008) and increased β-band (p ≪ 0.05) PAC were observed during the task. These outcomes represent the first characterization of electro-vascular PAC between fNIRS and EEG signals during an auditory task, providing insights into electro-vascular coupling in auditory processing.

Probing a neural unreliability account of auditory sensory processing atypicalities in Rett Syndrome

Background In the search for objective tools to quantify neural function in Rett Syndrome (RTT), which are crucial in the evaluation of therapeutic efficacy in clinical trials, recordings of sensory-perceptual functioning using event-related potential (ERP) approaches have emerged as potentially powerful tools. Considerable work points to highly anomalous auditory evoked potentials (AEPs) in RTT. However, an assumption of the typical signal-averaging method used to derive these measures is "stationarity" of the underlying responses - i.e. neural responses to each input are highly stereotyped. An alternate possibility is that responses to repeated stimuli are highly variable in RTT. If so, this will significantly impact the validity of assumptions about underlying neural dysfunction, and likely lead to overestimation of underlying neuropathology. To assess this possibility, analyses at the single-trial level assessing signal-to-noise ratios (SNR), inter-trial variability (ITV) and inter-trial phase coherence (ITPC) are necessary. Methods AEPs were recorded to simple 100Hz tones from 18 RTT and 27 age-matched controls (Ages: 6-22 years). We applied standard AEP averaging, as well as measures of neuronal reliability at the single-trial level (i.e. SNR, ITV, ITPC). To separate signal-carrying components from non-neural noise sources, we also applied a denoising source separation (DSS) algorithm and then repeated the reliability measures. Results Substantially increased ITV, lower SNRs, and reduced ITPC were observed in auditory responses of RTT participants, supporting a "neural unreliability" account. Application of the DSS technique made it clear that non-neural noise sources contribute to overestimation of the extent of processing deficits in RTT. Post-DSS, ITV measures were substantially reduced, so much so that pre-DSS ITV differences between RTT and TD populations were no longer detected. In the case of SNR and ITPC, DSS substantially improved these estimates in the RTT population, but robust differences between RTT and TD were still fully evident. Conclusions To accurately represent the degree of neural dysfunction in RTT using the ERP technique, a consideration of response reliability at the single-trial level is highly advised. Non-neural sources of noise lead to overestimation of the degree of pathological processing in RTT, and denoising source separation techniques during signal processing substantially ameliorate this issue.

Probing a neural unreliability account of auditory sensory processing atypicalities in Rett Syndrome

Background

In the search for objective tools to quantify neural function in Rett Syndrome (RTT), which are crucial in the evaluation of therapeutic efficacy in clinical trials, recordings of sensory-perceptual functioning using event-related potential (ERP) approaches have emerged as potentially powerful tools. Considerable work points to highly anomalous auditory evoked potentials (AEPs) in RTT. However, an assumption of the typical signal-averaging method used to derive these measures is "stationarity" of the underlying responses - i.e. neural responses to each input are highly stereotyped. An alternate possibility is that responses to repeated stimuli are highly variable in RTT. If so, this will significantly impact the validity of assumptions about underlying neural dysfunction, and likely lead to overestimation of underlying neuropathology. To assess this possibility, analyses at the single-trial level assessing signal-to-noise ratios (SNR), inter-trial variability (ITV) and inter-trial phase coherence (ITPC) are necessary.

Methods

AEPs were recorded to simple 100Hz tones from 18 RTT and 27 age-matched controls (Ages: 6-22 years). We applied standard AEP averaging, as well as measures of neuronal reliability at the single-trial level (i.e. SNR, ITV, ITPC). To separate signal-carrying components from non-neural noise sources, we also applied a denoising source separation (DSS) algorithm and then repeated the reliability measures.

Results

Substantially increased ITV, lower SNRs, and reduced ITPC were observed in auditory responses of RTT participants, supporting a "neural unreliability" account. Application of the DSS technique made it clear that non-neural noise sources contribute to overestimation of the extent of processing deficits in RTT. Post-DSS, ITV measures were substantially reduced, so much so that pre-DSS ITV differences between RTT and TD populations were no longer detected. In the case of SNR and ITPC, DSS substantially improved these estimates in the RTT population, but robust differences between RTT and TD were still fully evident.

Conclusions

To accurately represent the degree of neural dysfunction in RTT using the ERP technique, a consideration of response reliability at the single-trial level is highly advised. Non-neural sources of noise lead to overestimation of the degree of pathological processing in RTT, and denoising source separation techniques during signal processing substantially ameliorate this issue.

fNIRS-EEG BCIs for Motor Rehabilitation: A Review

Motor impairment has a profound impact on a significant number of individuals, leading to a substantial demand for rehabilitation services. Through brain-computer interfaces (BCIs), people with severe motor disabilities could have improved communication with others and control appropriately designed robotic prosthetics, so as to (at least partially) restore their motor abilities. BCI plays a pivotal role in promoting smoother communication and interactions between individuals with motor impairments and others. Moreover, they enable the direct control of assistive devices through brain signals. In particular, their most significant potential lies in the realm of motor rehabilitation, where BCIs can offer real-time feedback to assist users in their training and continuously monitor the brain's state throughout the entire rehabilitation process. Hybridization of different brain-sensing modalities, especially functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), has shown great potential in the creation of BCIs for rehabilitating the motor-impaired populations. EEG, as a well-established methodology, can be combined with fNIRS to compensate for the inherent disadvantages and achieve higher temporal and spatial resolution. This paper reviews the recent works in hybrid fNIRS-EEG BCIs for motor rehabilitation, emphasizing the methodologies that utilized motor imagery. An overview of the BCI system and its key components was introduced, followed by an introduction to various devices, strengths and weaknesses of different signal processing techniques, and applications in neuroscience and clinical contexts. The review concludes by discussing the possible challenges and opportunities for future development.

Predictors of Low Risk for Delirium during Anesthesia Emergence

BACKGROUND

Processed electroencephalography (EEG) is used to monitor the level of anesthesia, and it has shown the potential to predict the occurrence of delirium. While emergence trajectories of relative EEG band power identified post hoc show promising results in predicting a risk for a delirium, they are not easily transferable into an online predictive application. This article describes a low-resource and easily applicable method to differentiate between patients at high risk and low risk for delirium, with patients at low risk expected to show decreasing EEG power during emergence.

METHODS

This study includes data from 169 patients (median age, 61 yr [49, 73]) who underwent surgery with general anesthesia maintained with propofol, sevoflurane, or desflurane. The data were derived from a previously published study. The investigators chose a single frontal channel, calculated the total and spectral band power from the EEG and calculated a linear regression model to observe the parameters' change during anesthesia emergence, described as slope. The slope of total power and single band power was correlated with the occurrence of delirium.

RESULTS

Of 169 patients, 32 (19%) showed delirium. Patients whose total EEG power diminished the most during emergence were less likely to screen positive for delirium in the postanesthesia care unit. A positive slope in total power and band power evaluated by using a regression model was associated with a higher risk ratio (total, 2.83 [95% CI, 1.46 to 5.51]; alpha/beta band, 7.79 [95% CI, 2.24 to 27.09]) for delirium. Furthermore, a negative slope in multiple bands during emergence was specific for patients without delirium and allowed definition of a test for patients at low risk.

CONCLUSIONS

This study developed an easily applicable exploratory method to analyze a single frontal EEG channel and to identify patterns specific for patients at low risk for delirium.

EDITOR’S PERSPECTIVE

Response of the GE Entropy™ monitor to neuromuscular block in awake volunteers

BACKGROUND

The GE Entropy™ monitor analyses the frontal electroencephalogram (EEG) and generates two indices intended to represent the degree of anaesthetic drug effect on the brain. It is frequently used in the context of neuromuscular block. We have shown that a similar device, the Bispectral Index monitor (BIS), does not generate correct values in awake volunteers when neuromuscular blocking drugs are administered.

METHODS

We replayed the EEGs recorded during awake paralysis from the original study to an Entropy monitor via a calibrated electronic playback system. Each EEG was replayed 30 times to evaluate the consistency of the Entropy output.

RESULTS

Both State Entropy and Response Entropy decreased during periods of neuromuscular block to values consistent with anaesthesia, despite there being no change in conscious state (State Entropy <60 in eight of nine rocuronium trials and nine of 10 suxamethonium trials). Entropy values did not return to pre-test levels until after the return of movement. Entropy did not generate exactly the same results when the same EEG was replayed multiple times, which is primarily because of a cyclical state within the Entropy system itself.

CONCLUSIONS

The GE Entropy™ monitor requires muscle activity to generate correct values in an awake subject. It could therefore be unreliable at detecting awareness in patients who have been given neuromuscular blocking drugs. In addition, Entropy does not generate the same result each time it is presented with the same EEG.

Technical and clinical considerations for electroencephalography-based biomarkers for major depressive disorder

Major depressive disorder (MDD) is a prevalent and debilitating psychiatric disease that leads to substantial loss of quality of life. There has been little progress in developing new MDD therapeutics due to a poor understanding of disease heterogeneity and individuals' responses to treatments. Electroencephalography (EEG) is poised to improve this, owing to the ease of large-scale data collection and the advancement of computational methods to address artifacts. This review summarizes the viability of EEG for developing brain-based biomarkers in MDD. We examine the properties of well-established EEG preprocessing pipelines and consider factors leading to the discovery of sensitive and reliable biomarkers.

Prediction of Gait Kinematics and Kinetics: A Systematic Review of EMG and EEG Signal Use and Their Contribution to Prediction Accuracy

Human-machine interfaces hold promise in enhancing rehabilitation by predicting and responding to subjects' movement intent. In gait rehabilitation, neural network architectures utilize lower-limb muscle and brain activity to predict continuous kinematics and kinetics during stepping and walking. This systematic review, spanning five databases, assessed 16 papers meeting inclusion criteria. Studies predicted lower-limb kinematics and kinetics using electroencephalograms (EEGs), electromyograms (EMGs), or a combination with kinematic data and anthropological parameters. Long short-term memory (LSTM) and convolutional neural network (CNN) tools demonstrated highest accuracies. EEG focused on joint angles, while EMG predicted moments and torque joints. Useful EEG electrode locations included C3, C4, Cz, P3, F4, and F8. Vastus Lateralis, Rectus Femoris, and Gastrocnemius were the most commonly accessed muscles for kinematic and kinetic prediction using EMGs. No studies combining EEGs and EMGs to predict lower-limb kinematics and kinetics during stepping or walking were found, suggesting a potential avenue for future development in this technology.

EEG-Based Cross-Subject Driver Drowsiness Recognition With an Interpretable Convolutional Neural Network

In the context of electroencephalogram (EEG)-based driver drowsiness recognition, it is still challenging to design a calibration-free system, since EEG signals vary significantly among different subjects and recording sessions. Many efforts have been made to use deep learning methods for mental state recognition from EEG signals. However, existing work mostly treats deep learning models as black-box classifiers, while what have been learned by the models and to which extent they are affected by the noise in EEG data are still underexplored. In this article, we develop a novel convolutional neural network combined with an interpretation technique that allows sample-wise analysis of important features for classification. The network has a compact structure and takes advantage of separable convolutions to process the EEG signals in a spatial-temporal sequence. Results show that the model achieves an average accuracy of 78.35% on 11 subjects for leave-one-out cross-subject drowsiness recognition, which is higher than the conventional baseline methods of 53.40%-72.68% and state-of-the-art deep learning methods of 71.75%-75.19%. Interpretation results indicate the model has learned to recognize biologically meaningful features from EEG signals, e.g., alpha spindles, as strong indicators of drowsiness across different subjects. In addition, we also explore reasons behind some wrongly classified samples with the interpretation technique and discuss potential ways to improve the recognition accuracy. Our work illustrates a promising direction on using interpretable deep learning models to discover meaningful patterns related to different mental states from complex EEG signals.

BCI-Walls: A robust methodology to predict if conscious EEG changes can be detected in the presence of artefacts

Brain computer interfaces (BCI) depend on reliable realtime detection of conscious EEG changes for example to control a video game. However, scalp recordings are contaminated with non-stationary noise, such as facial muscle activity and eye movements. This interferes with the detection process making it potentially unreliable or even impossible. We have developed a new methodology which provides a hard and measurable criterion if conscious EEG changes can be detected in the presence of non-stationary noise by requiring the signal-to-noise ratio of a scalp recording to be greater than the SNR-wall which in turn is based on the highest and lowest noise variances of the recording. As an instructional example, we have recorded signals from the central electrode Cz during eight different activities causing non-stationary noise such as playing a video game or reading out loud. The results show that facial muscle activity and eye-movements have a strong impact on the detectability of EEG and that minimising both eye-movement artefacts and muscle noise is essential to be able to detect conscious EEG changes.

Sedline® Miscalculation of Depth of Anaesthesia Variables in Two Pigs Due to Electrocardiographic Signal Contamination

Two young (11-week-old) pigs underwent sole propofol anaesthesia as part of an experimental study. The depth of anaesthesia was evaluated both clinically and using the electroencephalography(EEG)-based monitor Sedline; in particular, the patient state index, suppression ratio, raw EEG traces, and its spectrogram were assessed. Physiological parameters and electrocardiographic activity were continuously monitored. In one pig (Case 1), during the administration of high doses of propofol, the Sedline-generated variables suddenly indicated an increased EEG activity while this was not confirmed by observation of either the raw EEG or its spectrogram. In the second pig (Case 2), a similar event was recorded during euthanasia with systemic pentobarbital. Both events happened while the EEG activity was isoelectric except for signal interferences and synchronous in rhythm and shape with the electrocardiographic activity. The suggestion of increased brain activity based on the interpretation of the Sedline variables was suspected wrong; most probably due to electrocardiographic interferences. In pigs, the patient state index and suppression ratio, as calculated by the Sedline monitor, could be influenced by the electrocardiographic activity contaminating the EEG trace, especially during otherwise isoelectric periods (strong EEG depression). Visual interpretation of the raw EEG and of the spectrogram remains necessary to identify such artefacts.

Design and Validation of a Low-Cost Mobile EEG-Based Brain-Computer Interface

Objective: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain-computer (BCI) interface and internet-of-things (IoT) applications. Approach: The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation. Main Results: The adjustable headset was designed to accommodate 90% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user's hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An inertial measurement unit (IMU) provides monitoring of head movements. The EEG amplifier operates with 24-bit resolution up to 500 Hz sampling frequency and can communicate with other devices using 802.11 b/g/n WiFi. It has high signal-to-noise ratio (SNR) and common-mode rejection ratio (CMRR) (121 dB and 110 dB, respectively) and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as a backend coding language and JavaScript (JS), Cascading Style Sheets (CSS), and HyperText Markup Language (HTML) as front-end coding languages and includes training and optimization of support vector machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human-subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation and provides proof-of-concept validation for the device's use at both the clinic and at home. Significance: The usability, interoperability, portability, reliability, and programmability of the proposed wireless closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications.

Spontaneous dyadic behaviour predicts the emergence of interpersonal neural synchrony

Synchronization of neural activity across brains - interpersonal neural synchrony (INS) - is emerging as a powerful marker of social interaction that predicts success of multi-person coordination, communication, and cooperation. As the origins of INS are poorly understood, we tested whether and how INS might emerge from spontaneous dyadic behavior. We recorded neural activity (EEG) and human behavior (full-body kinematics, eye movements and facial expressions) while dyads of participants were instructed to look at each other without speaking or making co-verbal gestures. We made four fundamental observations. First, despite the absence of a structured social task, INS emerged spontaneously only when participants were able to see each other. Second, we show that such spontaneous INS, comprising specific spectral and topographic profiles, did not merely reflect intra-personal modulations of neural activity, but it rather reflected real-time and dyad-specific coupling of neural activities. Third, using state-of-art video-image processing and deep learning, we extracted the temporal unfolding of three notable social behavioral cues - body movement, eye contact, and smiling - and demonstrated that these behaviors also spontaneously synchronized within dyads. Fourth, we probed the correlates of INS in such synchronized social behaviors. Using cross-correlation and Granger causality analyses, we show that synchronized social behaviors anticipate and in fact Granger cause INS. These results provide proof-of-concept evidence for studying interpersonal neural and behavioral synchrony under natural and unconstrained conditions. Most importantly, the results suggest that INS could be conceptualized as an emergent property of two coupled neural systems: an entrainment phenomenon, promoted by real-time dyadic behavior.

From MEG to clinical EEG: evaluating a promising non-invasive estimator of defense-related muscle sympathetic nerve inhibition

Sudden, unexpected stimuli can induce a transient inhibition of sympathetic vasoconstriction to skeletal muscle, indicating a link to defense reactions. This phenomenon is relatively stable within, but differs between, individuals. It correlates with blood pressure reactivity which is associated with cardiovascular risk. Inhibition of muscle sympathetic nerve activity (MSNA) is currently characterized through invasive microneurography in peripheral nerves. We recently reported that brain neural oscillatory power in the beta spectrum (beta rebound) recorded with magnetoencephalography (MEG) correlated closely with stimulus-induced MSNA inhibition. Aiming for a clinically more available surrogate variable reflecting MSNA inhibition, we investigated whether a similar approach with electroencephalography (EEG) can accurately gauge stimulus-induced beta rebound. We found that beta rebound shows similar tendencies to correlate with MSNA inhibition, but these EEG data lack the robustness of previous MEG results, although a correlation in the low beta band (13-20 Hz) to MSNA inhibition was found (p = 0.021). The predictive power is summarized in a receiver-operating-characteristics curve. The optimum threshold yielded sensitivity and false-positive rate of 0.74 and 0.33 respectively. A plausible confounder is myogenic noise. A more complicated experimental and/or analysis approach is required for differentiating MSNA-inhibitors from non-inhibitors based on EEG, as compared to MEG.

The development of oscillatory and aperiodic resting state activity is linked to a sensitive period in humans

Congenital blindness leads to profound changes in electroencephalographic (EEG) resting state activity. A well-known consequence of congenital blindness in humans is the reduction of alpha activity which seems to go together with increased gamma activity during rest. These results have been interpreted as indicating a higher excitatory/inhibitory (E/I) ratio in visual cortex compared to normally sighted controls. Yet it is unknown whether the spectral profile of EEG during rest would recover if sight were restored. To test this question, the present study evaluated periodic and aperiodic components of the EEG resting state power spectrum. Previous research has linked the aperiodic components, which exhibit a power-law distribution and are operationalized as a linear fit of the spectrum in log-log space, to cortical E/I ratio. Moreover, by correcting for the aperiodic components from the power spectrum, a more valid estimate of the periodic activity is possible. Here we analyzed resting state EEG activity from two studies involving (1) 27 permanently congenitally blind adults (CB) and 27 age-matched normally sighted controls (MCB); (2) 38 individuals with reversed blindness due to bilateral, dense, congenital cataracts (CC) and 77 age-matched sighted controls (MCC). Based on a data driven approach, aperiodic components of the spectra were extracted for the low frequency (Lf-Slope 1.5 to 19.5 Hz) and high frequency (Hf-Slope 20 to 45 Hz) range. The Lf-Slope of the aperiodic component was significantly steeper (more negative slope), and the Hf-Slope of the aperiodic component was significantly flatter (less negative slope) in CB and CC participants compared to the typically sighted controls. Alpha power was significantly reduced, and gamma power was higher in the CB and the CC groups. These results suggest a sensitive period for the typical development of the spectral profile during rest and thus likely an irreversible change in the E/I ratio in visual cortex due to congenital blindness. We speculate that these changes are a consequence of impaired inhibitory circuits and imbalanced feedforward and feedback processing in early visual areas of individuals with a history of congenital blindness.

Functional-oriented, portable brain-computer interface training for hand motor recovery after stroke: a randomized controlled study

Background

Brain-computer interfaces (BCIs) have been proven to be effective for hand motor recovery after stroke. Facing kinds of dysfunction of the paretic hand, the motor task of BCIs for hand rehabilitation is relatively single, and the operation of many BCI devices is complex for clinical use. Therefore, we proposed a functional-oriented, portable BCI equipment and explored the efficiency of hand motor recovery after a stroke.

Materials and methods

Stroke patients were randomly assigned to the BCI group and the control group. The BCI group received BCI-based grasp/open motor training, while the control group received task-oriented guidance training. Both groups received 20 sessions of motor training in 4 weeks, and each session lasted for 30 min. The Fugl-Meyer assessment of the upper limb (FMA-UE) was applied for the assessment of rehabilitation outcomes, and the EEG signals were obtained for processing.

Results

The progress of FMA-UE between the BCI group [10.50 (5.75, 16.50)] and the control group [5.00 (4.00, 8.00)] was significantly different (Z = -2.834, P = 0.005). Meanwhile, the FMA-UE of both groups improved significantly (P < 0.001). A total of 24 patients in the BCI group achieved the minimal clinically important difference (MCID) of FMA-UE with an effective rate of 80%, and 16 in the control group achieved the MCID, with an effective rate of 51.6%. The lateral index of the open task in the BCI group was significantly decreased (Z = -2.704, P = 0.007). The average BCI accuracy for 24 stroke patients in 20 sessions was 70.7%, which was improved by 5.0% in the final session compared with the first session.

Conclusion

Targeted hand movement and two motor task modes, namely grasp and open, to be applied in a BCI design may be suitable in stroke patients with hand dysfunction. The functional-oriented, portable BCI training can promote hand recovery after a stroke, and it is expected to be widely used in clinical practice. The lateral index change of inter-hemispheric balance may be the mechanism of motor recovery.

Trial registration number

ChiCTR2100044492.

Child and adolescent development of the brain oscillatory activity during a working memory task

The developmental trajectories of brain oscillations during the encoding and maintenance phases of a Working Memory (WM) task were calculated. The Delayed-Match-to-Sample Test (DMTS) was applied to 239 subjects of 6-29 years, while EEG was recorded. The Event-Related Spectral Perturbation (ERSP) was obtained in the range between 1 and 25 Hz during the encoding and maintenance phases. Behavioral parameters of reaction times (RTs) and response accuracy were simultaneously recorded. The results indicate a myriad of transient and sustained bursts of oscillatory activity from low frequencies (1 Hz) to the beta range (up to 19 Hz). Beta and Low-frequency ERSP increases were prominent in the encoding phase in all age groups, while low-frequency ERSP indexed the maintenance phase only in children and adolescents, but not in late adolescents and young adults, suggesting an age-dependent neural mechanism of stimulus trace maintenance. While the latter group showed Beta and Alpha indices of anticipatory attention for the retrieval phase. Mediation analysis showed an important role of early Delta-Theta and late Alpha oscillations for mediation between age and behavioral responses performance. In conclusion, the results show a complex pattern of oscillatory bursts during the encoding and maintenance phases with a consistent pattern of developmental changes.

Quantitative signal quality assessment for large-scale continuous scalp electroencephalography from a big data perspective

Objective. Despite electroencephalography (EEG) being a widely used neuroimaging technique with an excellent temporal resolution, in practice, the signals are heavily contaminated by artifacts masking responses of interest in an experiment. It is thus essential to guarantee a prompt and effective detection of artifacts that provides quantitative quality assessment (QA) on raw EEG data. This type of pipeline is crucial for large-scale EEG studies. However, current EEG QA studies are still limited.Approach. In this study, combined from a big data perspective, we therefore describe a quantitative signal quality assessment pipeline, a stable and general threshold-based QA pipeline that automatically integrates artifact detection and new QA measures to assess continuous resting-state raw EEG data. One simulation dataset and two resting-state EEG datasets from 42 healthy subjects and 983 clinical patients were utilized to calibrate the QA pipeline.Main Results. The results demonstrate that (1) the QA indices selected are sensitive: they almost strictly and linearly decrease as the noise level increases; (2) stable, replicable QA thresholds are valid for other experimental and clinical EEG datasets; and (3) use of the QA pipeline on these datasets reveals that high-frequency noises are the most common noises in EEG practice. The QA pipeline is also deployed in the WeBrain cloud platform (https://webrain.uestc.edu.cn/, the Chinese EEG Brain Consortium portal).Significance. These findings suggest that the proposed QA pipeline may be a stable and promising approach for quantitative EEG signal quality assessment in large-scale EEG studies.

Modulation of theta and gamma oscillations during familiarization with previously unknown music

Repeated listening to unknown music leads to gradual familiarization with musical sequences. Passively listening to musical sequences could involve an array of dynamic neural responses in reaching familiarization with the musical excerpts. This study elucidates the dynamic brain response and its variation over time by investigating the electrophysiological changes during the familiarization with initially unknown music. Twenty subjects were asked to familiarize themselves with previously unknown 10 s classical music excerpts over three repetitions while their electroencephalogram was recorded. Dynamic spectral changes in neural oscillations are monitored by time-frequency analyses for all frequency bands (theta: 5-9 Hz, alpha: 9-13 Hz, low-beta: 13-21 Hz, high beta: 21-32 Hz, and gamma: 32-50 Hz). Time-frequency analyses reveal sustained theta event-related desynchronization (ERD) in the frontal-midline and the left pre-frontal electrodes which decreased gradually from 1st to 3rd time repetition of the same excerpts (frontal-midline: 57.90 %, left-prefrontal: 75.93 %). Similarly, sustained gamma ERD decreased in the frontal-midline and bilaterally frontal/temporal areas (frontal-midline: 61.47 %, left-frontal: 90.88 %, right-frontal: 87.74 %). During familiarization, the decrease of theta ERD is superior in the first part (1-5 s) whereas the decrease of gamma ERD is superior in the second part (5-9 s) of music excerpts. The results suggest that decreased theta ERD is associated with successfully identifying familiar sequences, whereas decreased gamma ERD is related to forming unfamiliar sequences.

The restorative effects of short-term exposure to nature in immersive virtual environments (IVEs) as evidenced by participants' brain activities

Short-term exposure to nature has excellent potential to be used as a public health intervention measure. Nevertheless, the physiological and psychological mechanisms of this health benefit are still unclear. In this study, we intend to verify the effects of short-term exposure to nature on psychological functioning and to explore the underlying mechanism through experiments conducted in immersive virtual environments (IVEs). Participants were randomly exposed to videos of an urban forest and an indoor environment in IVEs. Before and after the exposure, a participant's self-perceived stress and cognitive performance were measured using the PSS-14 form and the Stroop task, respectively. Their brain activities during the exposure were measured using the electroencephalogram (EEG). The PSS-14 and the Stroop task results confirmed the benefits of stress reduction and cognitive performance improvements from short-term nature exposure. At the same time, rhythmic brain activities during nature exposure indicated better attentional states. The electrodes around the parietal region detected significantly stronger power spectral density of the theta band than other bands. Also, participants showed high functional connectivity among different brain parts during nature exposure, which revealed better cognitive flexibility. The topographic pattern of the differences in functional connectivity overlapped well with the default mode network (DMN)-a "task-negative" network active during the resting state. The overlap indicated a lower cognitive processing load when exposing to nature. Our results support the hypothesis that nature's restorative effects mainly come from effortless processing in natural environments.

iCanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG

Motion artifacts hinder source-level analysis of mobile electroencephalography (EEG) data using independent component analysis (ICA). iCanClean is a novel cleaning algorithm that uses reference noise recordings to remove noisy EEG subspaces, but it has not been formally tested in a parameter sweep. The goal of this study was to test iCanClean’s ability to improve the ICA decomposition of EEG data corrupted by walking motion artifacts. Our primary objective was to determine optimal settings and performance in a parameter sweep (varying the window length and r2 cleaning aggressiveness). High-density EEG was recorded with 120 + 120 (dual-layer) EEG electrodes in young adults, high-functioning older adults, and low-functioning older adults. EEG data were decomposed by ICA after basic preprocessing and iCanClean. Components well-localized as dipoles (residual variance < 15%) and with high brain probability (ICLabel > 50%) were marked as ‘good’. We determined iCanClean’s optimal window length and cleaning aggressiveness to be 4-s and r2 = 0.65 for our data. At these settings, iCanClean improved the average number of good components from 8.4 to 13.2 (+57%). Good performance could be maintained with reduced sets of noise channels (12.7, 12.2, and 12.0 good components for 64, 32, and 16 noise channels, respectively). Overall, iCanClean shows promise as an effective method to clean mobile EEG data.

External validation of binary machine learning models for pain intensity perception classification from EEG in healthy individuals

Discrimination of pain intensity using machine learning (ML) and electroencephalography (EEG) has significant potential for clinical applications, especially in scenarios where self-report is unsuitable. However, existing research is limited due to a lack of external validation (assessing performance using novel data). We aimed for the first external validation study for pain intensity classification with EEG. Pneumatic pressure stimuli were delivered to the fingernail bed at high and low pain intensities during two independent EEG experiments with healthy participants. Study one (n = 25) was utilised for training and cross-validation. Study two (n = 15) was used for external validation one (identical stimulation parameters to study one) and external validation two (new stimulation parameters). Time-frequency features of peri-stimulus EEG were computed on a single-trial basis for all electrodes. ML training and analysis were performed on a subset of features, identified through feature selection, which were distributed across scalp electrodes and included frontal, central, and parietal regions. Results demonstrated that ML models outperformed chance. The Random Forest (RF) achieved the greatest accuracies of 73.18, 68.32 and 60.42% for cross-validation, external validation one and two, respectively. Importantly, this research is the first to externally validate ML and EEG for the classification of intensity during experimental pain, demonstrating promising performance which generalises to novel samples and paradigms. These findings offer the most rigorous estimates of ML's clinical potential for pain classification.

The effect of ketamine and D-cycloserine on the high frequency resting EEG spectrum in humans

RATIONALE

Preclinical studies indicate that high-frequency oscillations, above 100 Hz (HFO:100-170 Hz), are a potential translatable biomarker for pharmacological studies, with the rapid acting antidepressant ketamine increasing both gamma (40-100 Hz) and HFO.

OBJECTIVES

To assess the effect of the uncompetitive NMDA antagonist ketamine, and of D-cycloserine (DCS), which acts at the glycine site on NMDA receptors on HFO in humans.

METHODS

We carried out a partially double-blind, 4-way crossover study in 24 healthy male volunteers. Each participant received an oral tablet and an intravenous infusion on each of four study days. The oral treatment was either DCS (250 mg or 1000 mg) or placebo. The infusion contained 0.5 mg/kg ketamine or saline placebo. The four study conditions were therefore placebo-placebo, 250 mg DCS-placebo, 1000 mg DCS-placebo, or placebo-ketamine.

RESULTS

Compared with placebo, frontal midline HFO magnitude was increased by ketamine (p = 0.00014) and 1000 mg DCS (p = 0.013). Frontal gamma magnitude was also increased by both these treatments. However, at a midline parietal location, only HFO were increased by DCS, and not gamma, whilst ketamine increased both gamma and HFO at this location. Ketamine induced psychomimetic effects, as measured by the PSI scale, whereas DCS did not increase the total PSI score. The perceptual distortion subscale scores correlated with the posterior low gamma to frontal high beta ratio.

CONCLUSIONS

Our results suggest that, at high doses, a partial NMDA agonist (DCS) has similar effects on fast neural oscillations as an NMDA antagonist (ketamine). As HFO were induced without psychomimetic effects, they may prove a useful drug development target.

Alpha oscillations related to self-other integration and distinction during live orchestral performance: A naturalistic case study

Ensemble music performance requires musicians to achieve precise interpersonal coordination while maintaining autonomous control over their own actions. To do so, musicians dynamically shift between integrating other performers' actions into their own action plans and maintaining a distinction between their own and others' actions. Research in laboratory settings has shown that this dynamic process of self-other integration and distinction is indexed by sensorimotor alpha oscillations. The purpose of the current descriptive case study was to examine oscillations related to self-other integration and distinction in a naturalistic performance context. We measured alpha activity from four violinists during a concert hall performance of a 60-musician orchestra. We selected a musical piece from the orchestra's repertoire and, before analyzing alpha activity, performed a score analysis to divide the piece into sections that were expected to strongly promote self-other integration and distinction. In line with previous laboratory findings, performers showed suppressed and enhanced alpha activity during musical sections that promoted self-other integration and distinction, respectively. The current study thus provides preliminary evidence that findings from carefully controlled laboratory experiments generalize to complex real-world performance. Its findings also suggest directions for future research and potential applications of interest to musicians, music educators, and music therapists.

Modelling the relationships between EEG signals, movement kinematics and outcome in soccer kicking

The contribution of cortical activity (e.g. EEG recordings) in various brain regions to motor control during goal-directed manipulative tasks using lower limbs remains unexplored. Therefore, the aim of the current study was to determine the magnitude of associations between EEG-derived brain activity and soccer kicking parameters. Twenty-four under-17 players performed an instep kicking task (18 m from the goal) aiming to hit 1 × 1 m targets allocated in the goalpost upper corners in the presence of a goalkeeper. Using a portable 64-channel EEG system, brain oscillations in delta, theta, alpha, beta and gamma frequency bands were determined at the frontal, motor, parietal and occipital regions separately for three phases of the kicks: preparatory, approach and immediately prior to ball contact. Movement kinematic measures included segmental linear and relative velocities, angular joint displacement and velocities. Mean radial error and ball velocity were assumed as outcome indicators. A significant influence of frontal theta power immediately prior to ball contact was observed in the variance of ball velocity (R 2 = 35%, P = 0.01) while the expression of occipital alpha component recorded during the preparatory phase contributed to the mean radial error (R 2 = 20%, P = 0.049). Ankle eversion angle at impact moment likely mediated the association between frontal theta power and subsequent ball velocity (β = 0.151, P = 0.06). The present analysis showed that the brain signalling at cortical level may be determinant in movement control, ball velocity and accuracy when performing kick attempts from the edge of penalty area. Trial registration number #RBR-8prx2m-Brazilian Registry of Clinical Trials ReBec.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09786-2.

Affection of facial artifacts caused by micro-expressions on electroencephalography signals

Macro-expressions are widely used in emotion recognition based on electroencephalography (EEG) because of their use as an intuitive external expression. Similarly, micro-expressions, as suppressed and brief emotional expressions, can also reflect a person's genuine emotional state. Therefore, researchers have started to focus on emotion recognition studies based on micro-expressions and EEG. However, compared to the effect of artifacts generated by macro-expressions on the EEG signal, it is not clear how artifacts generated by micro-expressions affect EEG signals. In this study, we investigated the effects of facial muscle activity caused by micro-expressions in positive emotions on EEG signals. We recorded the participants' facial expression images and EEG signals while they watched positive emotion-inducing videos. We then divided the 13 facial regions and extracted the main directional mean optical flow features as facial micro-expression image features, and the power spectral densities of theta, alpha, beta, and gamma frequency bands as EEG features. Multiple linear regression and Granger causality test analyses were used to determine the extent of the effect of facial muscle activity artifacts on EEG signals. The results showed that the average percentage of EEG signals affected by muscle artifacts caused by micro-expressions was 11.5%, with the frontal and temporal regions being significantly affected. After removing the artifacts from the EEG signal, the average percentage of the affected EEG signal dropped to 3.7%. To the best of our knowledge, this is the first study to investigate the affection of facial artifacts caused by micro-expressions on EEG signals.

Transcranial ultrasound neuromodulation of the thalamic visual pathway in a large animal model and the dose-response relationship with MR-ARFI

Neuromodulation of deep brain structures via transcranial ultrasound stimulation (TUS) is a promising, but still elusive approach to non-invasive treatment of brain disorders. The purpose of this study was to confirm that MR-guided TUS of the lateral geniculate nucleus (LGN) can modulate visual evoked potentials (VEPs) in the intact large animal; and to study the impact on cortical brain oscillations. The LGN on one side was identified with T2-weighted MRI in sheep (all male, n = 9). MR acoustic radiation force imaging (MR-ARFI) was used to confirm localization of the targeted area in the brain. Electroencephalographic (EEG) signals were recorded, and the visual evoked potential (VEP) peak-to-peak amplitude (N70 and P100) was calculated for each trial. Time-frequency spectral analysis was performed to elucidate the effect of TUS on cortical brain dynamics. The VEP peak-to-peak amplitude was reversibly suppressed relative to baseline during TUS. Dynamic spectral analysis demonstrated a change in cortical oscillations when TUS is paired with visual sensory input. Sonication-associated microscopic displacements, as measured by MR-ARFI, correlated with the TUS-mediated suppression of visual evoked activity. TUS non-invasively delivered to LGN can neuromodulate visual activity and oscillatory dynamics in large mammalian brains.

Assessment of EEG-based functional connectivity in response to haptic delay

Haptic technologies enable users to physically interact with remote or virtual environments by applying force, vibration, or motion via haptic interfaces. However, the delivery of timely haptic feedback remains a challenge due to the stringent computation and communication requirements associated with haptic data transfer. Haptic delay disrupts the realism of the user experience and interferes with the quality of interaction. Research efforts have been devoted to studying the neural correlates of delayed sensory stimulation to better understand and thus mitigate the impact of delay. However, little is known about the functional neural networks that process haptic delay. This paper investigates the underlying neural networks associated with processing haptic delay in passive and active haptic interactions. Nineteen participants completed a visuo-haptic task using a computer screen and a haptic device while electroencephalography (EEG) data were being recorded. A combined approach based on phase locking value (PLV) functional connectivity and graph theory was used. To assay the effects of haptic delay on functional connectivity, we evaluate a global connectivity property through the small-worldness index and a local connectivity property through the nodal strength index. Results suggest that the brain exhibits significantly different network characteristics when a haptic delay is introduced. Haptic delay caused an increased manifestation of the small-worldness index in the delta and theta bands as well as an increased nodal strength index in the middle central region. Inter-regional connectivity analysis showed that the middle central region was significantly connected to the parietal and occipital regions as a result of haptic delay. These results are expected to indicate the detection of conflicting visuo-haptic information at the middle central region and their respective resolution and integration at the parietal and occipital regions.

Correlation of Sedline-generated variables and clinical signs with anaesthetic depth in experimental pigs receiving propofol

Most of currently available electroencephalographic (EEG)-based tools to assess depth of anaesthesia have not been studied or have been judged unreliable in pigs. Our primary aim was to investigate the dose-effect relationship between increasing propofol dose and variables generated by the EEG-based depth of anaesthesia monitor Sedline in pigs. A secondary aim was to compare the anaesthetic doses with clinical outcomes commonly used to assess depth of anaesthesia in this species. Sixteen juvenile pigs were included. Propofol infusion was administered at 10 mg kg-1 h-1, increased by 10 mg kg-1 h-1 every 15 minutes, and stopped when an EEG Suppression ratio >80% was reached. Patient state index, suppression ratio, left and right spectral edge frequency 95%, and outcomes from commonly used clinical methods to assess depth of anaesthesia in pigs were recorded. The best pharmacodynamic model was assessed for Patient state index, suppression ratio, left and right spectral edge frequency 95% in response to propofol administration. The decrease of Patient state index best fitted to an inhibitory double-sigmoid model (including a plateau phase). The increase of suppression ratio fitted a typical sigmoid Emax model. No relevant relationship could be identified between spectral edge frequency 95% values and propofol administration. A large variability in clinical outcomes was observed among pigs, such that they did not provide a reliable evaluation of propofol dose. The relationship between propofol dose and Patient state index/suppression ratio described in the present study can be used for prediction in future investigations. The evaluation of depth of anaesthesia based on common clinical outcomes was not reliable.

Resting-state electroencephalographic correlates of cognitive reserve: Moderating the age-related worsening in cognitive function

This exploratory study aimed to investigate the resting-state electroencephalographic (rsEEG) correlates of the cognitive reserve from a life span perspective. Current source density (CSD) and lagged-linear connectivity (LLC) measures were assessed to this aim. We firstly explored the relationship between rsEEG measures for the different frequency bands and a socio-behavioral proxy of cognitive reserve, the Cognitive Reserve Index (CRI). Secondly, we applied moderation analyses to assess whether any of the correlated rsEEG measures showed a moderating role in the relationship between age and cognitive function. Moderate negative correlations were found between the CRI and occipital CSD of delta and beta 2. Moreover, inter- and intrahemispheric LLC measures were correlated with the CRI, showing a negative association with delta and positive associations with alpha 1, beta 1, and beta 2. Among those correlated measures, just two rsEEG variables were significant moderators of the relationship between age and cognition: occipital delta CSD and right hemispheric beta 2 LLC between occipital and limbic regions. The effect of age on cognitive performance was stronger for higher values of both measures. Therefore, lower values of occipital delta CSD and lower beta 2 LLC between right occipital and limbic regions might protect or compensate for the effects of age on cognition. Results of this exploratory study might be helpful to allocate more preventive efforts to curb the progression of cognitive decline in adults with less CR, possibly characterized by these rsEEG parameters at a neural level. However, given the exploratory nature of this study, more conclusive work on these rsEEG measures is needed to firmly establish their role in the cognition-age relationship, for example, verifying if these measures moderate the relationship between brain structure and cognition.

An EEG-based asynchronous MI-BCI system to reduce false positives with a small number of channels for neurorehabilitation: A pilot study

Many studies have used motor imagery-based brain–computer interface (MI-BCI) systems for stroke rehabilitation to induce brain plasticity. However, they mainly focused on detecting motor imagery but did not consider the effect of false positive (FP) detection. The FP could be a threat to patients with stroke as it can induce wrong-directed brain plasticity that would result in adverse effects. In this study, we proposed a rehabilitative MI-BCI system that focuses on rejecting the FP. To this end, we first identified numerous electroencephalogram (EEG) signals as the causes of the FP, and based on the characteristics of the signals, we designed a novel two-phase classifier using a small number of EEG channels, including the source of the FP. Through experiments with eight healthy participants and nine patients with stroke, our proposed MI-BCI system showed 71.76% selectivity and 13.70% FP rate by using only four EEG channels in the patient group with stroke. Moreover, our system can compensate for day-to-day variations for prolonged session intervals by recalibration. The results suggest that our proposed system, a practical approach for the clinical setting, could improve the therapeutic effect of MI-BCI by reducing the adverse effect of the FP.

Sources of Variation in the Spectral Slope of the Sleep EEG

The 1/f spectral slope of the electroencephalogram (EEG) estimated in the γ frequency range has been proposed as an arousal marker that differentiates wake, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Here, we sought to replicate and extend these findings in a large sample, providing a comprehensive characterization of how slope changes with age, sex, and its test-retest reliability as well as potential confounds that could affect the slope estimation. We used 10,255 whole-night polysomnograms (PSGs) from the National Sleep Research Resource (NSRR). All preprocessing steps were performed using an open-source Luna package and the spectral slope was estimated by fitting log-log linear regression models on the absolute power from 30 to 45 Hz separately for wake, NREM, and REM stages. We confirmed that the mean spectral slope grows steeper going from wake to NREM to REM sleep. We found that the choice of mastoid referencing scheme modulated the extent to which electromyogenic, or electrocardiographic artifacts were likely to bias 30- to 45-Hz slope estimates, as well as other sources of technical, device-specific bias. Nonetheless, within individuals, slope estimates were relatively stable over time. Both cross-sectionally and longitudinal, slopes tended to become shallower with increasing age, particularly for REM sleep; males tended to show flatter slopes than females across all states. Our findings support that spectral slope can be a valuable arousal marker for both clinical and research endeavors but also underscore the importance of considering interindividual variation and multiple methodological aspects related to its estimation.

Deep-Learning-Based Automatic Selection of Fewest Channels for Brain-Machine Interfaces

Due to the development of convenient brain-machine interfaces (BMIs), the automatic selection of a minimum channel (electrode) set has attracted increasing interest because the decrease in the number of channels increases the efficiency of BMIs. This study proposes a deep-learning-based technique to automatically search for the minimum number of channels applicable to general BMI paradigms using a compact convolutional neural network for electroencephalography (EEG)-based BMIs. For verification, three types of BMI paradigms are assessed: 1) the typical P300 auditory oddball; 2) the new top-down steady-state visually evoked potential; and 3) the endogenous motor imagery. We observe that the optimized minimal EEG-channel sets are automatically selected in all three cases. Their decoding accuracies using the minimal channels are statistically equivalent to (or even higher than) those based on all channels. The brain areas of the selected channel set are neurophysiologically interpretable for all of these cognitive task paradigms. This study shows that the minimal EEG channel set can be automatically selected, irrespective of the types of BMI paradigms or EEG input features using a deep-learning approach, which also contributes to their portability.

Managing electromyogram contamination in scalp recordings: An approach identifying reliable beta and gamma EEG features of psychoses or other disorders

OBJECTIVE

In publications on the electroencephalographic (EEG) features of psychoses and other disorders, various methods are utilized to diminish electromyogram (EMG) contamination. The extent of residual EMG contamination using these methods has not been recognized. Here, we seek to emphasize the extent of residual EMG contamination of EEG.

METHODS

We compared scalp electrical recordings after applying different EMG-pruning methods with recordings of EMG-free data from 6 fully paralyzed healthy subjects. We calculated the ratio of the power of pruned, normal scalp electrical recordings in the six subjects, to the power of unpruned recordings in the same subjects when paralyzed. We produced "contamination graphs" for different pruning methods.

RESULTS

EMG contamination exceeds EEG signals progressively more as frequencies exceed 25 Hz and with distance from the vertex. In contrast, Laplacian signals are spared in central scalp areas, even to 100 Hz.

CONCLUSION

Given probable EMG contamination of EEG in psychiatric and other studies, few findings on beta- or gamma-frequency power can be relied upon. Based on the effectiveness of current methods of EEG de-contamination, investigators should be able to reanalyze recorded data, reevaluate conclusions from high-frequency EEG data, and be aware of limitations of the methods.

Deciphering the code: Identifying true gamma neural oscillations

Neural oscillatory activity occurring in the gamma frequency range (30-80 Hz) has been proposed to play essential roles in sensory and cognitive processing. Supporting this, abnormalities in gamma oscillations have been reported in patients with diverse neurological and neuropsychiatric disorders in which cognitive impairment is prominent. Understanding the mechanisms underpinning this relationship is the focus of extensive research. But while an increasing number of studies are investigating the intricate relationship between gamma oscillations and cognition, interpretation and generalisation of these studies is limited by the diverse, and at times questionable, methodologies used to analyse oscillatory activity. For example, a variety of different types of gamma oscillatory activity have been characterised, but all are generalised non-specifically as 'gamma oscillations'. This creates confusion, since distinct cellular and network mechanisms are likely responsible for generating these different types of rhythm. Moreover, in some instances, certain analytical measures of electrophysiological data are overinterpreted, with researchers pushing the boundaries of what would be considered rhythmic or oscillatory in nature. Here, we provide clarity on these issues, firstly presenting an overview of the different measures of gamma oscillatory activity, and describing common signal processing techniques used for analysis. Limitations of these techniques are discussed, and recommendations made on how future studies should optimise analyses, presentation and interpretation of gamma frequency oscillations. This is an essential progression in order to harmonise future studies, allowing us to gain a clearer understanding of the role of gamma oscillations in cognition, and in cognitive disorders.

Improved classification performance of EEG-fNIRS multimodal brain-computer interface based on multi-domain features and multi-level progressive learning

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have potentially complementary characteristics that reflect the electrical and hemodynamic characteristics of neural responses, so EEG-fNIRS-based hybrid brain-computer interface (BCI) is the research hotspots in recent years. However, current studies lack a comprehensive systematic approach to properly fuse EEG and fNIRS data and exploit their complementary potential, which is critical for improving BCI performance. To address this issue, this study proposes a novel multimodal fusion framework based on multi-level progressive learning with multi-domain features. The framework consists of a multi-domain feature extraction process for EEG and fNIRS, a feature selection process based on atomic search optimization, and a multi-domain feature fusion process based on multi-level progressive machine learning. The proposed method was validated on EEG-fNIRS-based motor imagery (MI) and mental arithmetic (MA) tasks involving 29 subjects, and the experimental results show that multi-domain features provide better classification performance than single-domain features, and multi-modality provides better classification performance than single-modality. Furthermore, the experimental results and comparison with other methods demonstrated the effectiveness and superiority of the proposed method in EEG and fNIRS information fusion, it can achieve an average classification accuracy of 96.74% in the MI task and 98.42% in the MA task. Our proposed method may provide a general framework for future fusion processing of multimodal brain signals based on EEG-fNIRS.

Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior

How cognitive task behavior is generated by brain network interactions is a central question in neuroscience. Answering this question calls for the development of novel analysis tools that can firstly capture neural signatures of task information with high spatial and temporal precision (the "where and when") and then allow for empirical testing of alternative network models of brain function that link information to behavior (the "how"). We outline a novel network modeling approach suited to this purpose that is applied to noninvasive functional neuroimaging data in humans. We first dynamically decoded the spatiotemporal signatures of task information in the human brain by combining MRI-individualized source electroencephalography (EEG) with multivariate pattern analysis (MVPA). A newly developed network modeling approach-dynamic activity flow modeling-then simulated the flow of task-evoked activity over more causally interpretable (relative to standard functional connectivity [FC] approaches) resting-state functional connections (dynamic, lagged, direct, and directional). We demonstrate the utility of this modeling approach by applying it to elucidate network processes underlying sensory-motor information flow in the brain, revealing accurate predictions of empirical response information dynamics underlying behavior. Extending the model toward simulating network lesions suggested a role for the cognitive control networks (CCNs) as primary drivers of response information flow, transitioning from early dorsal attention network-dominated sensory-to-response transformation to later collaborative CCN engagement during response selection. These results demonstrate the utility of the dynamic activity flow modeling approach in identifying the generative network processes underlying neurocognitive phenomena.

Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography

Eliminating facial electromyographic (EMG) signal from the electroencephalogram (EEG) is crucial for the accuracy of applications such as brain computer interfaces (BCIs) and brain functionality measurement. Facial electromyography typically corrupts the electroencephalogram. Although it is possible to find in the literature a number of multi-channel approaches for filtering corrupted EEG, studies employing single-channel approaches are scarce. In this context, this study proposed a single-channel method for attenuating facial EMG noise from contaminated EEG. The architecture of the method allows for the evaluation and incorporation of multiple decomposition and adaptive filtering techniques. The decomposition method was responsible for generating EEG or EMG reference signals for the adaptive filtering stage. In this study, the decomposition techniques CiSSA, EMD, EEMD, EMD-PCA, SSA, and Wavelet were evaluated. The adaptive filtering methods RLS, Wiener, LMS, and NLMS were investigated. A time and frequency domain set of features were estimated from experimental signals to evaluate the performance of the single channel method. This set of characteristics permitted the characterization of the contamination of distinct facial muscles, namely Masseter, Frontalis, Zygomatic, Orbicularis Oris, and Orbicularis Oculi. Data were collected from ten healthy subjects executing an experimental protocol that introduced the necessary variability to evaluate the filtering performance. The largest level of contamination was produced by the Masseter muscle, as determined by statistical analysis of the set of features and visualization of topological maps. Regarding the decomposition method, the SSA method allowed for the generation of more suitable reference signals, whereas the RLS and NLMS methods were more suitable when the reference signal was derived from the EEG. In addition, the LMS and RLS methods were more appropriate when the reference signal was the EMG. This study has a number of practical implications, including the use of filtering techniques to reduce EEG contamination caused by the activation of facial muscles required by distinct types of studies. All the developed code, including examples, is available to facilitate a more accurate reproduction and improvement of the results of this study.

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

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

Comparison of non-invasive, scalp-recorded auditory steady-state responses in humans, rhesus monkeys, and common marmosets

Auditory steady-state responses (ASSRs) are basic neural responses used to probe the ability of auditory circuits to produce synchronous activity to repetitive external stimulation. Reduced ASSR has been observed in patients with schizophrenia, especially at 40 Hz. Although ASSR is a translatable biomarker with a potential both in animal models and patients with schizophrenia, little is known about the features of ASSR in monkeys. Herein, we recorded the ASSR from humans, rhesus monkeys, and marmosets using the same method to directly compare the characteristics of ASSRs among the species. We used auditory trains on a wide range of frequencies to investigate the suitable frequency for ASSRs induction, because monkeys usually use stimulus frequency ranges different from humans for vocalization. We found that monkeys and marmosets also show auditory event-related potentials and phase-locking activity in gamma-frequency trains, although the optimal frequency with the best synchronization differed among these species. These results suggest that the ASSR could be a useful translational, cross-species biomarker to examine the generation of gamma-band synchronization in nonhuman primate models of schizophrenia.