Removal of ocular artifact from the EEG: a review

Cross-frequency modulation of postural fluctuations and scalp EEG in older adults: error amplification feedback for rapid balance adjustments

Virtual error amplification (VEA) in visual feedback enhances attentive control over postural stability, although the neural mechanisms are still debated. This study investigated the distinct cortical control of unsteady stance in older adults using VEA through cross-frequency modulation of postural fluctuations and scalp EEG. Thirty-seven community-dwelling older adults (68.1 ± 3.6 years) maintained an upright stance on a stabilometer while receiving either VEA or real error feedback. Along with postural fluctuation dynamics, phase-amplitude coupling (PAC) and amplitude-amplitude coupling (AAC) were analyzed for postural fluctuations under 2 Hz and EEG sub-bands (theta, alpha, and beta). The results revealed a higher mean frequency of the postural fluctuation phase (p = .005) and a greater root mean square of the postural fluctuation amplitude (p = .003) with VEA compared to the control condition. VEA also reduced PAC between the postural fluctuation phase and beta-band EEG in the left frontal (p = .009), sensorimotor (p = .002), and occipital (p = .018) areas. Conversely, VEA increased the AAC of posture fluctuation amplitude and beta-band EEG in FP2 (p = .027). Neither theta nor alpha band PAC or AAC were affected by VEA. VEA optimizes postural strategies in older adults during stabilometer stance by enhancing visuospatial attentive control of postural responses and facilitating the transition of motor states against postural perturbations through a disinhibitory process. Incorporating VEA into virtual reality technology is advocated as a valuable strategy for optimizing therapeutic interventions in postural therapy, particularly to mitigate the risk of falls among older adults.

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.

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

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

The impact of culture on emotion suppression: Insights from an electrophysiological study of emotion regulation in Japan

Prior theory and evidence suggest that native East Asians tend to down-regulate their emotional arousal to negatively valenced experiences through expressive suppression, an emotion regulation technique focused on suppressing one's emotional experience. One proposed explanation for this choice of regulation strategy and its efficacy is rooted in their commitment to the cultural value of interdependence with others. However, prior work has not yet thoroughly supported this hypothesis using in vivo neural correlates of emotion regulation. Here, we utilized an established electroencephalogram (EEG) correlate of emotional arousal, the late positive potential (LPP), to examine whether down-regulation of the LPP in native East Asians might be particularly pronounced for those relatively high in interdependent self-construal. In this study, native Japanese participants attempted to suppress their emotional reaction to unpleasant images during EEG recording. In support of the hypothesis that emotion suppression among native East Asians is influenced by the cultural value of interdependence, there was a significant effect of interdependent self-construal on the LPP. Specifically, those relatively high in interdependent (versus independent) self-construal exhibited a smaller LPP in response to unpleasant pictures when instructed to suppress their emotions versus a passive viewing condition. However, this effect was negligible for those relatively low in interdependent self-construal, suggesting that cultural values impact the in vivo efficacy of different emotion regulation techniques. These results demonstrate the importance of identifying correspondence between self-report measures and in vivo correlates of emotion regulation in cross-cultural research.

Coregistration of EEG and eye-tracking in infants and developing populations

Infants cannot be instructed where to look; therefore, infant researchers rely on observation of their participant's gaze to make inferences about their cognitive processes. They therefore started studying infant attention in the real world from early on. Developmental researchers were early adopters of methods combining observations of gaze and behaviour with electroencephalography (EEG) to study attention and other cognitive functions. However, the direct combination of eye-tracking methods and EEG to test infants is still rare, as it includes specific challenges. The current article reviews the development of co-registration research in infancy. It points out specific challenges of co-registration in infant research and suggests ways to overcome them. It ends with recommendations for implementing the co-registration of EEG and eye-tracking in infant research to maximise the benefits of the two measures and their combination and to orient on Open Science principles while doing so. In summary, this work shows that the co-registration of EEG and eye-tracking in infant research can be beneficial to studying natural and real-world behaviour despite its challenges.

Transcutaneous auricular vagus nerve stimulation modulates the processing of interoceptive prediction error signals and their role in allostatic regulation

It has recently been suggested that predictive processing principles may apply to interoception, defined as the processing of hormonal, autonomic, visceral, and immunological signals. In the current study, we aimed at providing empirical evidence for the role of cardiac interoceptive prediction errors signals on allostatic adjustments, using transcutaneous auricular vagus nerve stimulation (taVNS) as a tool to modulate the processing of interoceptive afferents. In a within-subject design, participants performed a cardiac-related interoceptive task (heartbeat counting task) under taVNS and sham stimulation, spaced 1-week apart. We observed that taVNS, in contrast to sham stimulation, facilitated the maintenance of interoceptive accuracy levels over time (from the initial, stimulation-free, baseline block to subsequent stimulation blocks), suggesting that vagus nerve stimulation may have helped to maintain engagement to cardiac afferent signals. During the interoceptive task, taVNS compared to sham, produced higher heart-evoked potentials (HEP) amplitudes, a potential readout measure of cardiac-related prediction error processing. Further analyses revealed that the positive relation between interoceptive accuracy and allostatic adjustments-as measured by heart rate variability (HRV)-was mediated by HEP amplitudes. Providing initial support for predictive processing accounts of interoception, our results suggest that the stimulation of the vagus nerve may increase the precision with which interoceptive signals are processed, favoring their influence on allostatic adjustments.

A general dual-pathway network for EEG denoising

Introduction

Scalp electroencephalogram (EEG) analysis and interpretation are crucial for tracking and analyzing brain activity. The collected scalp EEG signals, however, are weak and frequently tainted with various sorts of artifacts. The models based on deep learning provide comparable performance with that of traditional techniques. However, current deep learning networks applied to scalp EEG noise reduction are large in scale and suffer from overfitting.

Methods

Here, we propose a dual-pathway autoencoder modeling framework named DPAE for scalp EEG signal denoising and demonstrate the superiority of the model on multi-layer perceptron (MLP), convolutional neural network (CNN) and recurrent neural network (RNN), respectively. We validate the denoising performance on benchmark scalp EEG artifact datasets.

Results

The experimental results show that our model architecture not only significantly reduces the computational effort but also outperforms existing deep learning denoising algorithms in root relative mean square error (RRMSE)metrics, both in the time and frequency domains.

Discussion

The DPAE architecture does not require a priori knowledge of the noise distribution nor is it limited by the network layer structure, which is a general network model oriented toward blind source separation.

Neural Oscillations and Functional Significances for Prioritizing Dual-Task Walking in Parkinson's Disease

Background

Task prioritization involves allocating brain resources in a dual-task scenario, but the mechanistic details of how prioritization strategies affect dual-task walking performance for Parkinson's disease (PD) are little understood.

Objective

We investigated the performance benefits and corresponding neural signatures for people with PD during dual-task walking, using gait-prioritization (GP) and manual-prioritization (MP) strategies.

Methods

Participants (N = 34) were asked to hold two inter-locking rings while walking and to prioritize either taking big steps (GP strategy) or separating the two rings (MP strategy). Gait parameters and ring-touch time were measured, and scalp electroencephalograph was performed.

Results

Compared with the MP strategy, the GP strategy yielded faster walking speed and longer step length, whereas ring-touch time did not significantly differ between the two strategies. The MP strategy led to higher alpha (8-12 Hz) power in the posterior cortex and beta (13-35 Hz) power in the left frontal-temporal area, but the GP strategy was associated with stronger network connectivity in the beta band. Changes in walking speed and step length because of prioritization negatively correlated with changes in alpha power. Prioritization-related changes in ring-touch time correlated negatively with changes in beta power but positively with changes in beta network connectivity.

Conclusions

A GP strategy in dual-task walking for PD can enhance walking speed and step length without compromising performance in a secondary manual task. This strategy augments attentional focus and facilitates compensatory reinforcement of inter-regional information exchange.

Analyzing neural activity under prolonged mask usage through EEG

In recent COVID times, mask has been a compulsion at workplaces and institutes as a preventive measure against multiple viral diseases including coronavirus (COVID-19) disease. However, the effects of prolonged mask-wearing on humans' neural activity are not well known. This paper is to investigate the effect of prolonged mask usage on the human brain through electroencephalogram (EEG), which acquires neural activity and translates it into comprehensible electrical signals. The performances of 10 human subjects with and without mask were assessed on a random patterned alphabet game. Besides EEG, physiological parameters of oxygen saturation, heart rate, blood pressure, and body temperature were recorded. Spectral and statistical analysis were performed on the recorded entities along with linear discriminant analysis (LDA) on extracted spectral features. The mean EEG spectral power in alpha, beta, and gamma sub-bands of the subjects with mask was smaller than the subjects without mask. The performances on the task and the oxygen saturation level between the two groups differed significantly (p < 0.05). Whereas, the blood pressure, body temperature, and heart rate of both groups were similar. Based on the LDA analysis, the occipital and frontal lobes exhibited the greatest variability in channel measurements, with O1 and O2 channels in the occipital lobe demonstrating significant variations within the alpha band due to visual focus, while the F3, AF3, and F7 channels were found to be differentiating within the beta and gamma frequency bands due to the cognitive stimulating tasks. All other channels were observed to be non-discriminatory.

iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG

The goal of this study was to test a novel approach (iCanClean) to remove non-brain sources from scalp EEG data recorded in mobile conditions. We created an electrically conductive phantom head with 10 brain sources, 10 contaminating sources, scalp, and hair. We tested the ability of iCanClean to remove artifacts while preserving brain activity under six conditions: Brain, Brain + Eyes, Brain + Neck Muscles, Brain + Facial Muscles, Brain + Walking Motion, and Brain + All Artifacts. We compared iCanClean to three other methods: Artifact Subspace Reconstruction (ASR), Auto-CCA, and Adaptive Filtering. Before and after cleaning, we calculated a Data Quality Score (0-100%), based on the average correlation between brain sources and EEG channels. iCanClean consistently outperformed the other three methods, regardless of the type or number of artifacts present. The most striking result was for the condition with all artifacts simultaneously present. Starting from a Data Quality Score of 15.7% (before cleaning), the Brain + All Artifacts condition improved to 55.9% after iCanClean. Meanwhile, it only improved to 27.6%, 27.2%, and 32.9% after ASR, Auto-CCA, and Adaptive Filtering. For context, the Brain condition scored 57.2% without cleaning (reasonable target). We conclude that iCanClean offers the ability to clear multiple artifact sources in real time and could facilitate human mobile brain-imaging studies with EEG.

Sonic Influence on Initially Neutral Brands: Using EEG to Unveil the Secrets of Audio Evaluative Conditioning

The present study addresses the question of whether explicit, survey-type measures of attitude differ in sensitivity when compared to implicit, non-conscious measures of attitude in the context of attitude changes in response to evaluative conditioning (EC). In the frame of a pre-test, participants rated 300 brand names on a Likert-type scale, the results of which were then used to create personalised lists of neutral brands. After this initial online component, the participants were exposed to one, five, and ten rounds of EC (during three separate sessions), during which half of the brands were paired with pleasant audio excerpts (positive EC) and the remainder were paired with unpleasant audio excerpts (negative EC). Following each conditioning round, the participants rated the brand names again, whilst changes in the brain's electrical activity in response to the brands were recorded via electroencephalography (EEG). After having rated the brand names, the participants also completed two implicit association tests (IAT; one for each of the neutral conditions). The results revealed that self-reported, explicit responses of brand names remained unchanged despite having been conditioned. Similarly, the IAT did not reveal any declines in reaction time. In contrast, the EEG data appeared to not only be sensitive to initial brand ratings, but also the conditioning effects of initially neutral brands. Respective neurophysiological effects were found at frontal electrode locations AF3 and AF4 for a 1 s-long time window starting at 400 ms after stimulus onset. Furthermore, the EEG revealed that changes in brand attitude are more susceptible to the effects of negative conditioning than positive conditioning. Given the rather small sample size, any generalizability seems vague, but the present results provide scientific evidence that EEG could indeed be a valuable additional method to investigate EC effects. The results of this study support the notion of utilising a multidimensional approach, inclusive of neuroscience, to understanding consumer attitudes instead of solely relying on self-report measures. In the end, the brain knows more than it admits to consciousness and language, which is why objective methods should always be included in any study.

Psychophysiology of Monotonous Driving, Fatigue and Sleepiness in Train and Non-Professional Drivers: Driver Safety Implications

Fatigue and sleepiness are complex bodily states associated with monotony as well as physical and cognitive impairment, accidents, injury, and illness. Moreover, these states are often characteristic of professional driving. However, most existing work has focused on motor vehicle drivers, and research examining train drivers remains limited. As such, the present study psychophysiologically examined monotonous driving, fatigue, and sleepiness in a group of passenger train drivers and a group of non-professional drivers. Sixty-three train drivers and thirty non-professional drivers participated in the present study, which captured 32-lead electroencephalogram (EEG) data during a monotonous driving task. Fatigue and sleepiness were self-evaluated using the Pittsburgh Sleep Quality Index, the Epworth Sleepiness Scale, the Karolinksa Sleepiness Scale, and the Checklist of Individual Strength. Unexpectedly, fatigue and sleepiness scores did not significantly differ between the groups; however, train drivers generally scored lower than non-professional drivers, which may be indicative of individual and/or industry attempts to reduce fatigue. Across both groups, fatigue and sleepiness scores were negatively correlated with theta, alpha, and beta EEG variables clustered towards the fronto-central and temporal regions. Broadly, these associations may reflect a monotony-associated blunting of neural activity that is associated with a self-reported fatigue state.

Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques

The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain-computer interface. Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this study, we analyze a comprehensive review of numerous articles related to EEG signal processing. We searched the major scientific and engineering databases and summarized the results of our findings. Our survey encompassed the entire process of EEG signal processing, from acquisition and pretreatment (denoising) to feature extraction, classification, and application. We present a detailed discussion and comparison of various methods and techniques used for EEG signal processing. Additionally, we identify the current limitations of these techniques and analyze their future development trends. We conclude by offering some suggestions for future research in the field of EEG signal processing.

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.

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.

Depression screening using hybrid neural network

Depression is a common cause of increased suicides worldwide, and studies have shown that the number of patients suffering from major depressive disorder (MDD) increased several-fold during the COVID-19 pandemic, highlighting the importance of disease detection and depression management, while increasing the need for effective diagnostic tools. In recent years, machine learning and deep learning methods based on electroencephalography (EEG) have achieved significant results in the field of automatic depression detection. However, most current studies have focused on a small number of EEG signal channels, and experimental data require special processing by professionals. In this study, 128 channels of EEG signals were simply filtered and 24-fold leave-one-out cross-validation experiments were performed using 2DCNN-LSTM classifier, support vector machine, K-nearest neighbor and decision tree. The current results show that the proposed 2DCNN-LSTM model has an average classification accuracy of 95.1% with an AUC of 0.98 for depression detection of 6-second participant EEG signals, and the model is much better than 72.05%, 79.7% and 79.49% for support vector machine, K nearest neighbor and decision tree. In addition, we found that the model achieved a 100% probability of correctly classifying the EEG signals of 300-second participants.

Deep-EEG: An Optimized and Robust Framework and Method for EEG-Based Diagnosis of Epileptic Seizure

Detecting brain disorders using deep learning methods has received much hype during the last few years. Increased depth leads to more computational efficiency, accuracy, and optimization and less loss. Epilepsy is one of the most common chronic neurological disorders characterized by repeated seizures. We have developed a deep learning model using Deep convolutional Autoencoder-Bidirectional Long Short Memory for Epileptic Seizure Detection (DCAE-ESD-Bi-LSTM) for automatic detection of seizures using EEG data. The significant feature of our model is that it has contributed to the accurate and optimized diagnosis of epilepsy in ideal and real-life situations. The results on the benchmark (CHB-MIT) dataset and the dataset collected by the authors show the relevance of the proposed approach over the baseline deep learning techniques by achieving an accuracy of 99.8%, classification accuracy of 99.7%, sensitivity of 99.8%, specificity and precision of 99.9% and F1 score of 99.6%. Our approach can contribute to the accurate and optimized detection of seizures while scaling the design rules and increasing performance without changing the network's depth.

Neurophysiological Evaluation of Students' Experience during Remote and Face-to-Face Lessons: A Case Study at Driving School

Nowadays, fostered by technological progress and contextual circumstances such as the economic crisis and pandemic restrictions, remote education is experiencing growing deployment. However, this growth has generated widespread doubts about the actual effectiveness of remote/online learning compared to face-to-face education. The present study was aimed at comparing face-to-face and remote education through a multimodal neurophysiological approach. It involved forty students at a driving school, in a real classroom, experiencing both modalities. Wearable devices to measure brain, ocular, heart and sweating activities were employed in order to analyse the students' neurophysiological signals to obtain insights into the cognitive dimension. In particular, four parameters were considered: the Eye Blink Rate, the Heart Rate and its Variability and the Skin Conductance Level. In addition, the students filled out a questionnaire at the end to obtain an explicit measure of their learning performance. Data analysis showed higher cognitive activity, in terms of attention and mental engagement, in the in-presence setting compared to the remote modality. On the other hand, students in the remote class felt more stressed, particularly during the first part of the lesson. The analysis of questionnaires demonstrated worse performance for the remote group, thus suggesting a common "disengaging" behaviour when attending remote courses, thus undermining their effectiveness. In conclusion, neuroscientific tools could help to obtain insights into mental concerns, often "blind", such as decreasing attention and increasing stress, as well as their dynamics during the lesson itself, thus allowing the definition of proper countermeasures to emerging issues when introducing new practices into daily life.

Using electroencephalography to analyse drivers' different cognitive workload characteristics based on on-road experiment

Driver's cognitive workload has an important impact on driving safety. This paper carries out an on-road experiment to analyse the impact from three innovative aspects: significance analysis of electroencephalogram (EEG) under different cognitive workloads, distribution of EEG maps with different frequency signals and influence of different cognitive workloads on driving safety based on EEG. First, the EEG signals are processed and four frequencies of delta, theta, alpha and beta are obtained. Then, the time-frequency transform and power spectral density calculation are carried out by short-time Fourier to study the correlation of each frequency signal of different workload states, as well as the distribution pattern of the EEG topographic map. Finally, the time and space energy and phase changes in each cognitive task event are studied through event-related spectral perturbation and inter-trial coherence. Results show the difference between left and right brains, as well as the resource occupancy trends of the monitor, perception, visual and auditory channels in different driving conditions. Results also demonstrate that the increase in cognitive workloads will directly affect driving safety. Changes in cognitive workload have different effects on brain signals, and this paper can provide a theoretical basis for improving driving safety under different cognitive workloads. Mastering the EEG characteristics of signals can provide more targeted supervision and safety warnings for the driver.

HArtMuT-modeling eye and muscle contributors in neuroelectric imaging

Objective.Magneto- and electroencephalography (M/EEG) measurements record a mix of signals from the brain, eyes, and muscles. These signals can be disentangled for artifact cleaning e.g. using spatial filtering techniques. However, correctly localizing and identifying these components relies on head models that so far only take brain sources into account.Approach.We thus developed the Head Artifact Model using Tripoles (HArtMuT). This volume conduction head model extends to the neck and includes brain sources as well as sources representing eyes and muscles that can be modeled as single dipoles, symmetrical dipoles, and tripoles. We compared a HArtMuT four-layer boundary element model (BEM) with the EEGLAB standard head model on their localization accuracy and residual variance (RV) using a HArtMuT finite element model (FEM) as ground truth. We also evaluated the RV on real-world data of mobile participants, comparing different HArtMuT BEM types with the EEGLAB standard head model.Main results.We found that HArtMuT improves localization for all sources, especially non-brain, and localization error and RV of non-brain sources were in the same range as those of brain sources. The best results were achieved by using cortical dipoles, muscular tripoles, and ocular symmetric dipoles, but dipolar sources alone can already lead to convincing results.Significance.We conclude that HArtMuT is well suited for modeling eye and muscle contributions to the M/EEG signal. It can be used to localize sources and to identify brain, eye, and muscle components. HArtMuT is freely available and can be integrated into standard software.

EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms

Blind source separation (BSS) methods have received a great deal of attention in electroencephalogram (EEG) artifact elimination as they are routine and standard signal processing tools to remove artifacts and reserve desired neural information. On the other hand, a classifier should follow BSS methods to automatically identify artifactual sources and remove them in the following steps. In addition, removing all detected artifactual components leads to loss of information since some desired information related to neural activity leaks to these sources. So, an approach should be employed to detect and suppress the artifacts and reserve neural activity. This study introduces a novel method based on EEG and Poincare planes in the phase space to detect artifactual components estimated by second-order blind identification (SOBI). Artifacts are detected using a mixture of well-known conventional classifiers and were removed employing stationary wavelet transform (SWT) to reserve neural information. The proposed method is a combination of signal processing techniques and machine learning algorithms, including multi-layer perceptron (MLP), K-nearest neighbor (KNN), naïve Bayes, and support vector machine (SVM) which have significant results while applying our proposed method to different scenarios. Simulated, semi-simulated, and real EEG signals are employed to evaluate the proposed method, and several evaluation criteria are calculated. We achieved acceptable results, for example, 98% average accuracy and 97% average sensitivity in artifactual EEG component detection or about 2% as mean square error in EEG reconstruction after artifact removal. Results showed that the proposed method is effective and can be used in future studies as we have considered different real-world scenarios to evaluate it.

A Survey on Denoising Techniques of Electroencephalogram Signals Using Wavelet Transform

Electroencephalogram (EEG) artifacts such as eyeblink, eye movement, and muscle movements widely contaminate the EEG signals. Those unwanted artifacts corrupt the information contained in the EEG signals and degrade the performance of qualitative analysis of clinical applications and as well as EEG-based brain–computer interfaces (BCIs). The applications of wavelet transform in denoising EEG signals are increasing day by day due to its capability of handling non-stationary signals. All the reported wavelet denoising techniques for EEG signals are surveyed in this paper in terms of the quality of noise removal and retrieving important information. In order to evaluate the performance of wavelet denoising techniques for EEG signals and to express the quality of reconstruction, the techniques were evaluated based on the results shown in the respective literature. We also compare certain features in the evaluation of the wavelet denoising techniques, such as the requirement of reference channel, automation, online, and performance on a single channel.

Wireless interfaces for brain neurotechnologies

Wireless interfaces enable brain-implanted devices to remotely interact with the external world. They are critical components in modern research and clinical neurotechnologies and play a central role in determining their overall size, lifetime and functionality. Wireless interfaces use a wide range of modalities-including radio-frequency fields, acoustic waves and light-to transfer energy and data to and from an implanted device. These forms of energy interact with living tissue through distinct mechanisms and therefore lead to systems with vastly different form factors, operating characteristics, and safety considerations. This paper reviews recent advances in the development of wireless interfaces for brain neurotechnologies. We summarize the requirements that state-of-the-art brain-implanted devices impose on the wireless interface, and discuss the working principles and applications of wireless interfaces based on each modality. We also investigate challenges associated with wireless brain neurotechnologies and discuss emerging solutions permitted by recent developments in electrical engineering and materials science. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.

Neural correlates of user learning during long-term BCI training for the Cybathlon competition

Background

Brain-computer interfaces (BCIs) are systems capable of translating human brain patterns, measured through electroencephalography (EEG), into commands for an external device. Despite the great advances in machine learning solutions to enhance the performance of BCI decoders, the translational impact of this technology remains elusive. The reliability of BCIs is often unsatisfactory for end-users, limiting their application outside a laboratory environment.

Methods

We present the analysis on the data acquired from an end-user during the preparation for two Cybathlon competitions, where our pilot won the gold medal twice in a row. These data are of particular interest given the mutual learning approach adopted during the longitudinal training phase (8 months), the long training break in between the two events (1 year) and the demanding evaluation scenario. A multifaceted perspective on long-term user learning is proposed: we enriched the information gathered through conventional metrics (e.g., accuracy, application performances) by investigating novel neural correlates of learning in different neural domains.

Results

First, we showed that by focusing the training on user learning, the pilot was capable of significantly improving his performance over time even with infrequent decoder re-calibrations. Second, we revealed that the analysis of the within-class modifications of the pilot’s neural patterns in the Riemannian domain is more effective in tracking the acquisition and the stabilization of BCI skills, especially after the 1-year break. These results further confirmed the key role of mutual learning in the acquisition of BCI skills, and particularly highlighted the importance of user learning as a key to enhance BCI reliability.

Conclusion

We firmly believe that our work may open new perspectives and fuel discussions in the BCI field to shift the focus of future research: not only to the machine learning of the decoder, but also in investigating novel training procedures to boost the user learning and the stability of the BCI skills in the long-term. To this end, the analyses and the metrics proposed could be used to monitor the user learning during training and provide a marker guiding the decoder re-calibration to maximize the mutual adaptation of the user to the BCI system.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-022-01047-x.

A meta-learning BCI for estimating decision confidence

OBJECTIVE

We investigated whether a recently introduced transfer-learning technique based on meta-learning could improve the performance of Brain-Computer Interfaces (BCIs) for decision-confidence prediction with respect to more traditional machine learning methods.

APPROACH

We adapted the meta-learning by biased regularisation algorithm to the problem of predicting decision confidence from EEG and EOG data on a decision-by-decision basis in a difficult target discrimination task based on video feeds. The method exploits previous participants' data to produce a prediction algorithm that is then quickly tuned to new participants. We compared it with with the traditional single-subject training almost universally adopted in BCIs, a state-of-the-art transfer learning technique called Domain Adversarial Neural Networks (DANN), a transfer-learning adaptation of a zero-training method we used recently for a similar task, and with a simple baseline algorithm.

MAIN RESULTS

The meta-learning approach was significantly better than other approaches in most conditions, and much better in situations where limited data from a new participant are available for training/tuning. Meta-learning by biased regularisation allowed our BCI to seamlessly integrate information from past participants with data from a specific user to produce high-performance predictors. Its robustness in the presence of small training sets is a real-plus in BCI applications, as new users need to train the BCI for a much shorter period.

SIGNIFICANCE

Due to the variability and noise of EEG/EOG data, BCIs need to be normally trained with data from a specific participant. This work shows that even better performance can be obtained using our version of meta-learning by biased regularisation.

A Brief Summary of EEG Artifact Handling

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

Intelligent Method for Real-Time Portable EEG Artifact Annotation in Semiconstrained Environment Based on Computer Vision

As a convenient device for observing neural activity in the natural environment, portable EEG technology (PEEGT) has an extensive prospect in expanding neuroscience research into natural applications. However, unlike in the laboratory environment, PEEGT is usually applied in a semiconstrained environment, including management and engineering, generating much more artifacts caused by the subjects' activities. Due to the limitations of existing artifacts annotation, the problem limits PEEGT to take advantage of portability and low-test cost, which is a crucial obstacle for the potential application of PEEGT in the natural environment. This paper proposes an intelligent method to identify two leading antecedent causes of EEG artifacts, participant's blinks and head movements, and annotate the time segments of artifacts in real time based on computer vision (CV). Furthermore, it changes the original postprocessing mode based on artifact signal recognition to the preprocessing mode based on artifact behavior recognition by the CV method. Through a comparative experiment with three artifacts mark operators and the CV method, we verify the effectiveness of the method, which lays a foundation for accurate artifact removal in real time in the next step. It enlightens us on how to adopt computer technology to conduct large-scale neurotesting in a natural semiconstrained environment outside the laboratory without expensive laboratory equipment or high manual costs.

A Two-Person Neuroscience Approach for Social Anxiety: A Paradigm With Interbrain Synchrony and Neurofeedback

Social anxiety disorder has been widely recognised as one of the most commonly diagnosed mental disorders. Individuals with social anxiety disorder experience difficulties during social interactions that are essential in the regular functioning of daily routines; perpetually motivating research into the aetiology, maintenance and treatment methods. Traditionally, social and clinical neuroscience studies incorporated protocols testing one participant at a time. However, it has been recently suggested that such protocols are unable to directly assess social interaction performance, which can be revealed by testing multiple individuals simultaneously. The principle of two-person neuroscience highlights the interpersonal aspect of social interactions that observes behaviour and brain activity from both (or all) constituents of the interaction, rather than analysing on an individual level or an individual observation of a social situation. Therefore, two-person neuroscience could be a promising direction for assessment and intervention of the social anxiety disorder. In this paper, we propose a novel paradigm which integrates two-person neuroscience in a neurofeedback protocol. Neurofeedback and interbrain synchrony, a branch of two-person neuroscience, are discussed in their own capacities for their relationship with social anxiety disorder and relevance to the paradigm. The newly proposed paradigm sets out to assess the social interaction performance using interbrain synchrony between interacting individuals, and to employ a multi-user neurofeedback protocol for intervention of the social anxiety.

Removing EOG Artifacts from the EEG signal of Methamphetamine Addicts

EEG can be used to characterize the electrical activity of the cerebral cortex, but it is also susceptible to interference. Compared with the other artifacts, Electrooculogram (EOG) artifacts have a greater impact on EEG processing and are more difficult to remove. Here, we mainly compared the regression and ICA algorithms both based on the EOG channels for the effect of removing EOG artifacts in the Stroop experiment of methamphetamine addicts. From the perspective of time domain and power spectral density, the ICA algorithm based on the EOG channels is more effective. However, the regression algorithm based on the EOG channels is less complex, more time-saving, and more suitable for real-time tasks.Clinical Relevance- For clinical purposes, this research has a certain reference value for selecting appropriate methods of removing EOG artifacts when processing the EEG of methamphetamine addicts.

Causal roles of prefrontal cortex during spontaneous perceptual switching are determined by brain state dynamics

The prefrontal cortex (PFC) is thought to orchestrate cognitive dynamics. However, in tests of bistable visual perception, no direct evidence supporting such presumable causal roles of the PFC has been reported except for a recent work. Here, using a novel brain-state-dependent neural stimulation system, we identified causal effects on percept dynamics in three PFC activities—right frontal eye fields, dorsolateral PFC (DLPFC), and inferior frontal cortex (IFC). The causality is behaviourally detectable only when we track brain state dynamics and modulate the PFC activity in brain-state-/state-history-dependent manners. The behavioural effects are underpinned by transient neural changes in the brain state dynamics, and such neural effects are quantitatively explainable by structural transformations of the hypothetical energy landscapes. Moreover, these findings indicate distinct functions of the three PFC areas: in particular, the DLPFC enhances the integration of two PFC-active brain states, whereas IFC promotes the functional segregation between them. This work resolves the controversy over the PFC roles in spontaneous perceptual switching and underlines brain state dynamics in fine investigations of brain-behaviour causality.

A cube that seems to shift its spatial arrangement as you keep looking; the elegant silhouette of a pirouetting dancer, which starts to spin in the opposite direction the more you stare at it; an illustration that shows two profiles – or is it a vase? These optical illusions are examples of bistable visual perception. Beyond their entertaining aspect, they provide a way for scientists to explore the dynamics of human consciousness, and the neural regions involved in this process.

Some studies show that bistable visual perception is associated with the activation of the prefrontal cortex, a brain area involved in complex cognitive processes. However, it is unclear whether this region is required for the illusions to emerge. Some research has showed that even if sections of the prefrontal cortex are temporally deactivated, participants can still experience the illusions.

Instead, Takamitsu Watanabe proposes that bistable visual perception is a process tied to dynamic brain states – that is, that distinct regions of the prefontal cortex are required for this fluctuating visual awareness, depending on the state of the whole brain. Such causal link cannot be observed if brain activity is not tracked closely.

To investigate this, the brain states of 65 participants were recorded as individuals were experiencing the optical illusions; the activity of their various brain regions could therefore be mapped, and then areas of the prefrontal cortex could precisely be inhibited at the right time using transcranial magnetic stimulation. This revealed that, indeed, prefrontal cortex regions were necessary for bistable visual perception, but not in a simple way. Instead, which ones were required and when depended on activity dynamics taking place in the whole brain. Overall, these results indicate that monitoring brain states is necessary to better understand – and ultimately, control – the neural pathways underlying perception and behaviour.

EEGdenoiseNet: a benchmark dataset for deep learning solutions of EEG denoising

Objective.Deep learning (DL) networks are increasingly attracting attention across various fields, including electroencephalography (EEG) signal processing. These models provide comparable performance to that of traditional techniques. At present, however, there is a lack of well-structured and standardized datasets with specific benchmark limit the development of DL solutions for EEG denoising.Approach.Here, we present EEGdenoiseNet, a benchmark EEG dataset that is suited for training and testing DL-based denoising models, as well as for performance comparisons across models. EEGdenoiseNet contains 4514 clean EEG segments, 3400 ocular artifact segments and 5598 muscular artifact segments, allowing users to synthesize contaminated EEG segments with the ground-truth clean EEG.Main results.We used EEGdenoiseNet to evaluate denoising performance of four classical networks (a fully-connected network, a simple and a complex convolution network, and a recurrent neural network). Our results suggested that DL methods have great potential for EEG denoising even under high noise contamination.Significance.Through EEGdenoiseNet, we hope to accelerate the development of the emerging field of DL-based EEG denoising. The dataset and code are available athttps://github.com/ncclabsustech/EEGdenoiseNet.

A deep neural network approach for P300 detection-based BCI using single-channel EEG scalogram images

Brain-computer interfaces (BCIs) acquire electroencephalogram (EEG) signals and interpret them into a command that helps people with severe motor disabilities using single channel. The goal of BCI is to achieve a prototype that supports disabled people to develop the relevant function. Various studies have been implemented in the literature to achieve a superior design using multi-channel EEG signals. This paper proposed a novel framework for the automatic P300 detection-based BCI model using a single EEG electrode. In the present study, we introduced a denoising approach using the bandpass filter technique followed by the transformation of scalogram images using continuous wavelet transform. The derived images were trained and validated using a deep neural network based on the transfer learning approach. This paper presents a BCI model based on the deep network that delivers higher performance in terms of classification accuracy and bitrate for disabled subjects using a single-channel EEG signal. The proposed P300 based BCI model has the highest average information transfer rates of 13.23 to 26.48 bits/min for disabled subjects. The classification performance has shown that the deep network based on the transfer learning approach can offer comparable performance with other state-of-the-art-method.

A non-invasive olfactory bulb measure dissociates Parkinson's patients from healthy controls and discloses disease duration

Olfactory dysfunction is a prevalent non-motor symptom of Parkinson's disease (PD). This dysfunction is a result of neurodegeneration within the olfactory bulb (OB), the first processing area of the central olfactory system, and commonly precedes the characteristic motor symptoms in PD by several years. Functional measurements of the OB could therefore potentially be used as an early biomarker for PD. Here, we used a non-invasive method, so-called electrobulbogram (EBG), to measure OB function in PD and age-matched healthy controls to assess whether EBG measures can dissociate PDs from controls. We estimated the spectrogram of the EBG signal during exposure to odor in PD (n = 20) and age-matched controls (n = 18) as well as identified differentiating patterns of odor-related synchronization in the gamma, beta, and theta frequency bands. Moreover, we assessed if these PD-EBG components could dissociate PD from control as well as their relationship with PD characteristics. We identified six EBG components during the initial and later stages of odor processing which dissociated PD from controls with 90% sensitivity and 100% specificity with links to PD characteristics. These PD-EBG components were related to medication, disease duration, and severity, as well as clinical odor identification performance. These findings support using EBG as a tool to experimentally assess PD interventions, potentially aid diagnosis, and the potential development of EBG into an early biomarker for PD.

High-speed ocular Artifacts Removal of multichannel EEG Based on improved moment matching

OBJECTIVE

The excellent Signal-to-Noise Ratio (SNR) is the premise of Electroencephalogram (EEG) research and applications. This study aims to use innovative method to swiftly remove the Ocular Artifacts (OAs) from multichannel EEG to enhance the SNR.

METHODS

The moment matching method which is prevalently used to removing stripe noise from hyperspectral images is adapted and improved to deduct OAs from EEG. This modified approach regards sampling points of multichannel EEG as pixels in images. It utilizes the propagation characteristics of EEG to correct the potential shift caused by OAs.

RESULTS

By using mathematical derivation and empirical corroboration, the results suggest that the improved moment matching (IMM) is capable of reducing OAs effectively and reserving the EEG waveform information on the greatest extent compared to existing methods, such as independent component analysis (ICA) and second-order blind identification (SOBI). In the frontal region heavily affected by OAs, the SNR increased by 138.1% compared with ICA, the whole SNR increased by an average of 58.7%. Moreover, low latency superiority is provided for real-time and offline processing.

CONCLUSION

IMM is effective for OAs removal and it is helpful to improve SNR of multichannel EEG.

SIGNIFICANCE

IMM affords option offering preponderance for enhancement of SNR of multichannel EEG.

Education 4.0: Teaching the Basis of Motor Imagery Classification Algorithms for Brain-Computer Interfaces

Education 4.0 is looking to prepare future scientists and engineers not only by granting them with knowledge and skills but also by giving them the ability to apply them to solve real life problems through the implementation of disruptive technologies. As a consequence, there is a growing demand for educational material that introduces science and engineering students to technologies, such as Artificial Intelligence (AI) and Brain–Computer Interfaces (BCI). Thus, our contribution towards the development of this material is to create a test bench for BCI given the basis and analysis on how they can be discriminated against. This is shown using different AI methods: Fisher Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Artificial Neural Networks (ANN), Restricted Boltzmann Machines (RBM) and Self-Organizing Maps (SOM), allowing students to see how input changes alter their performance. These tests were done against a two-class Motor Image database. First, using a large frequency band and no filtering eye movement. Secondly, the band was reduced and the eye movement was filtered. The accuracy was analyzed obtaining values around 70∼80% for all methods, excluding SVM and SOM mapping. Accuracy and mapping differentiability increased for some subjects for the second scenario 70∼85%, meaning either their band with the most significant information is on that limited space or the contamination because of eye movement was better mitigated by the regression method. This can be translated to saying that these methods work better under limited spaces. The outcome of this work is useful to show future scientists and engineers how BCI experiments are conducted while teaching them the basics of some AI techniques that can be used in this and other several experiments that can be carried on the framework of Education 4.0.

Online detection of movement during natural and self-initiated reach-and-grasp actions from EEG signals

Movement intention detection using electroencephalography (EEG) is a challenging but essential component of brain-computer interfaces (BCIs) for people with motor disabilities.Objective.The goal of this study is to develop a new experimental paradigm to perform asynchronous online detection of movement based on low-frequency time-domain EEG features, concretely on movement-related cortical potentials. The paradigm must be easily transferable to people without any residual upper-limb movement function and the BCI must be independent of upper-limb movement onset measurements and external cues.Approach. In a study with non-disabled participants, we evaluated a novel BCI paradigm to detect self-initiated reach-and-grasp movements. Two experimental conditions were involved. In one condition, participants performed reach-and-grasp movements to a target and simultaneously shifted their gaze towards it. In a control condition, participants solely shifted their gaze towards the target (oculomotor task). The participants freely decided when to initiate the tasks. After eye artefact correction, the EEG signals were time-locked to the saccade onset and the resulting amplitude features were exploited on a hierarchical classification approach to detect movement asynchronously.Main results. With regards to BCI performance, 54.1% (14.4% SD) of the movements were correctly identified, and all participants achieved a performance above chance-level (around 12%). An average of 21.5% (14.1% SD) of the oculomotor tasks were falsely detected as upper-limb movement. In an additional rest condition, 1.7 (1.6 SD) false positives per minute were measured. Through source imaging, movement information was mapped to sensorimotor, posterior parietal and occipital areas.Significance. We present a novel approach for movement detection using EEG signals which does not rely on upper-limb movement onset measurements or on the presentation of external cues. The participants' behaviour closely matches the natural behaviour during goal-directed reach-and-grasp movements, which also constitutes an advantage with respect to current BCI protocols.

The effect of a psychomotor intervention on electroencephalography and neuropsychological performances in older adults with and without mild cognitive impairment

AIM

The aim of this pilot study was to examine the acute effect of a psychomotor intervention (PMI) on auditory-verbal memory, emotional state, and electrocortical activity recorded by electroencephalography on subjectively healthy older adults (sHE) and older adults diagnosed with mild cognitive impairment (MCIs).

METHODS

Eleven MCIs and 11 sHE underwent a single 45-min PMI. Resting state electroencephalography, the Rey Auditory-Verbal Learning Test, MoodMeter®, and the Positive and Negative Affect Schedule were compared between groups and pre- and post-PMI.

RESULTS

Electroencephalography current source density and activity within the theta frequency band were higher in MCIs than in sHE at baseline, and brain frequency had a tendency to decrease in MCIs after training. Both groups showed improvement on the auditory-verbal memory test. Only among MCIs were there increases in perceived physical state and psychological strain and an improvement in negative affect.

CONCLUSIONS

Our findings suggest that acute psychomotor activity may be more effective for MCIs than for sHE. It supports the notion that PMI does have functional influences on the central nervous level and therefore might prevent and treat cognitive, psychological, and psychiatric symptoms of people with mild cognitive impairment.

Oscillatory alpha power at rest reveals an independent self: A cross-cultural investigation

In the current cultural psychology literature, it is commonly assumed that the personal self is cognitively more salient for those with an independent (vs. interdependent) self-construal (SC). So far, however, this assumption remains largely untested. Here, we drew on evidence that resting state alpha power (RSAP) reflects mental processes constituting the personal self, and tested whether RSAP is positively correlated with independent (vs. interdependent) SC. Study 1 tested European Americans and Taiwanese, whereas Study 2 tested European Americans and Japanese (total N = 164). A meta-analysis performed on the combined data confirmed a reliable association between independent (vs. interdependent) SC and RSAP. However, this association was only reliable when participants had their eyes closed. Even though European Americans were consistently more independent than East Asians, RSAP was no greater for European Americans than for East Asians. Our data helps explore a missing link in the theorizing of contemporary cultural psychology.

AOAR: an automatic ocular artifact removal approach for multi-channel electroencephalogram data based on non-negative matrix factorization and empirical mode decomposition

OBJECTIVE

Electroencephalogram (EEG) signals suffer inevitable interference from artifacts during the acquisition process. These artifacts make the analysis and interpretation of EEG data difficult. A major source of artifacts in EEGs is ocular activity. Therefore, it is important to remove ocular artifacts before further processing the EEG data.

APPROACH

In this study, an automatic ocular artifact removal (AOAR) method for EEG signals is proposed based on non-negative matrix factorization (NMF) and empirical mode decomposition (EMD). First, the amplitude of EEG data was normalized in order to ensure its non-negativity. Then, the normalized EEG data were decomposed into a set of components using NMF. The components containing ocular artifacts were extracted automatically through the fractal dimension. Subsequently, the temporal activities of these components were adaptively decomposed into some intrinsic mode functions (IMFs) by EMD. The IMFs corresponding to ocular artifacts were removed. Finally, the de-noised EEG data were reconstructed.

MAIN RESULTS

The proposed method was tested against seven other methods. In order to assess the effectiveness and reliability of the AOAR method in processing EEG data, experiments on ocular artifact removal were performed using simulated EEG data. Experimental results indicated that the proposed method was superior to the other methods in terms of root mean square error, signal-to-noise ratio (SNR) and correlation coefficient, especially in cases with a lower SNR. To further evaluate the potential applications of the proposed method in real life, the proposed method and others were applied to preprocess real EEG data recorded from children with and without attention-deficit/hyperactivity disorder (ADHD). After artifact rejection, the event-related potential feature was extracted for classification. The AOAR method was best at distinguishing the children with ADHD from the others.

SIGNIFICANCE

These results indicate that the proposed AOAR method has excellent prospects for removing ocular artifacts from EEG data.

Automatic Eyeblink Artifact Removal From EEG Signal Using Wavelet Transform With Heuristically Optimized Threshold

This paper proposes an automatic eyeblink artifacts removal method from corrupted-EEG signals using discrete wavelet transform (DWT) and meta-heuristically optimized threshold. The novel idea of thresholding approximation-coefficients (ACs) instead of detail-coefficients (DCs) of DWT of EEG in a backward manner is proposed for the first time for the removal of eyeblink artifacts. EEG is very sensitive and easily gets affected by eyeblink artifacts. First, the eyeblink corrupted EEG signals are identified using support vector machine (SVM) as a classifier. Then the corrupted EEG signal is decomposed using DWT up to the sixth level. Both the mother wavelet and the level of decomposition are selected using appropriate techniques. Then the ACs are thresholded in backward manner using the optimum threshold values followed by inverse DWT operation to reconstruct the original EEG signal. The AC at level 6 is thresholded and is used in IDWT with DC to get back the AC at level 5. Likewise, the backward thresholding of the ACs followed by IDWT is continued till the artifact free EEG signal is reconstructed at level 1. The optimum values of the thresholds of the ACs at different levels are optimized using two meta-heuristic algorithms, particle swarm optimization (PSO) and grey wolf optimization (GWO) for comparison. The results reveal that the proposed methodology is superior to the recently reported methods in terms of average correlation coefficient (CC) which states that the proposed method is better in terms of the quality of reconstruction in addition to being fully automatic.

Is Brain Dynamics Preserved in the EEG After Automated Artifact Removal? A Validation of the Fingerprint Method and the Automatic Removal of Cardiac Interference Approach Based on Microstate Analysis

The assessment of a method for removing artifacts from electroencephalography (EEG) datasets often disregard verifying that global brain dynamics is preserved. In this study, we verified that the recently introduced optimized fingerprint method and the automatic removal of cardiac interference (ARCI) approach not only remove physiological artifacts from EEG recordings but also preserve global brain dynamics, as assessed with a new approach based on microstate analysis. We recorded EEG activity with a high-resolution EEG system during two resting-state conditions (eyes open, 25 volunteers, and eyes closed, 26 volunteers) known to exhibit different brain dynamics. After signal decomposition by independent component analysis (ICA), the independent components (ICs) related to eyeblinks, eye movements, myogenic interference, and cardiac electromechanical activity were identified with the optimized fingerprint method and ARCI approach and statistically compared with the outcome of the expert classification of the ICs by visual inspection. Brain dynamics in two different groups of denoised EEG signals, reconstructed after having removed the artifactual ICs identified by either visual inspection or the automated methods, was assessed by calculating microstate topographies, microstate metrics (duration, occurrence, and coverage), and directional predominance (based on transition probabilities). No statistically significant differences between the expert and the automated classification of the artifactual ICs were found (p > 0.05). Cronbach’s α values assessed the high test–retest reliability of microstate parameters for EEG datasets denoised by the automated procedure. The total EEG signal variance explained by the sets of global microstate templates was about 80% for all denoised EEG datasets, with no significant differences between groups. For the differently denoised EEG datasets in the two recording conditions, we found that the global microstate templates and the sequences of global microstates were very similar (p < 0.01). Descriptive statistics and Cronbach’s α of microstate metrics highlighted no significant differences and excellent consistency between groups (p > 0.5). These results confirm the ability of the optimized fingerprint method and the ARCI approach to effectively remove physiological artifacts from EEG recordings while preserving global brain dynamics. They also suggest that microstate analysis could represent a novel approach for assessing the ability of an EEG denoising method to remove artifacts without altering brain dynamics.

The effects of roflumilast, a phosphodiesterase type-4 inhibitor, on EEG biomarkers in schizophrenia: A randomised controlled trial

BACKGROUND

Patients with schizophrenia have significant cognitive deficits, which may profoundly impair quality of life. These deficits are also evident at the neurophysiological level with patients demonstrating altered event-related potential in several stages of cognitive processing compared to healthy controls; within the auditory domain, for example, there are replicated alterations in Mismatch Negativity, P300 and Auditory Steady State Response. However, there are no approved pharmacological treatments for cognitive deficits in schizophrenia.

AIMS

Here we examine whether the phosphodiesterase-4 inhibitor, roflumilast, can improve neurophysiological deficits in schizophrenia.

METHODS

Using a randomised, double-blind, placebo-controlled, crossover design study in 18 patients with schizophrenia, the effect of the phosphodiesterase-4 inhibitor, roflumilast (100 µg and 250 µg) on auditory steady state response (early stage), mismatch negativity and theta (intermediate stage) and P300 (late stage) was examined using electroencephalogram. A total of 18 subjects were randomised and included in the analysis.

RESULTS

Roflumilast 250 µg significantly enhanced the amplitude of both the mismatch negativity (p=0.04) and working memory-related theta oscillations (p=0.02) compared to placebo but not in the other (early- or late-stage) cognitive markers.

CONCLUSIONS

The results suggest that phosphodiesterase-4 inhibition, with roflumilast, can improve electroencephalogram cognitive markers, which are impaired in schizophrenia, and that phosphodiesterase-4 inhibition acts at an intermediate rather than early or late cognitive processing stage. This study also underlines the use of neurophysiological measures as cognitive biomarkers in experimental medicine.