Automatic Removal of Physiological Artifacts in EEG: The Optimized Fingerprint Method for Sports Science Applications

Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review

Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscle activity, fasciculation, cable swings, or magnetic induction pose significant challenges in real-world BCI applications. In this paper, we present a systematic review of methods for motion artifact reduction in online BCI experiments. Using the PRISMA filter method, we conducted a comprehensive literature search on PubMed, focusing on open access publications from 1966 to 2022. We evaluated 2,333 publications based on predefined filtering rules to identify existing methods and pipelines for motion artifact reduction in EEG data. We present a lookup table of all papers that passed the defined filters, all used methods, and pipelines and compare their overall performance and suitability for online BCI experiments. We summarize suitable methods, algorithms, and concepts for motion artifact reduction in online BCI applications, highlight potential research gaps, and discuss existing community consensus. This review aims to provide a comprehensive overview of the current state of the field and guide researchers in selecting appropriate methods for motion artifact reduction in online BCI experiments.

ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals

Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence, they must be removed to facilitate unbiased decision-making for a given investigation. Due to the complex and elusive manifestation of artefacts in neuronal signals, computational techniques serve as powerful tools for their detection and removal. Machine learning (ML) based methods have been successfully applied in this task. Due to ML’s popularity, many articles are published every year, making it challenging to find, compare and select the most appropriate method for a given experiment. To this end, this paper presents ABOT (Artefact removal Benchmarking Online Tool) as an online benchmarking tool which allows users to compare existing ML-driven artefact detection and removal methods from the literature. The characteristics and related information about the existing methods have been compiled as a knowledgebase (KB) and presented through a user-friendly interface with interactive plots and tables for users to search it using several criteria. Key characteristics extracted from over 120 articles from the literature have been used in the KB to help compare the specific ML models. To comply with the FAIR (Findable, Accessible, Interoperable and Reusable) principle, the source code and documentation of the toolbox have been made available via an open-access repository.

A high-density 256-channel cap for dry electroencephalography

High‐density electroencephalography (HD‐EEG) is currently limited to laboratory environments since state‐of‐the‐art electrode caps require skilled staff and extensive preparation. We propose and evaluate a 256‐channel cap with dry multipin electrodes for HD‐EEG. We describe the designs of the dry electrodes made from polyurethane and coated with Ag/AgCl. We compare in a study with 30 volunteers the novel dry HD‐EEG cap to a conventional gel‐based cap for electrode‐skin impedances, resting state EEG, and visual evoked potentials (VEP). We perform wearing tests with eight electrodes mimicking cap applications on real human and artificial skin. Average impedances below 900 kΩ for 252 out of 256 dry electrodes enables recording with state‐of‐the‐art EEG amplifiers. For the dry EEG cap, we obtained a channel reliability of 84% and a reduction of the preparation time of 69%. After exclusion of an average of 16% (dry) and 3% (gel‐based) bad channels, resting state EEG, alpha activity, and pattern reversal VEP can be recorded with less than 5% significant differences in all compared signal characteristics metrics. Volunteers reported wearing comfort of 3.6 ± 1.5 and 4.0 ± 1.8 for the dry and 2.5 ± 1.0 and 3.0 ± 1.1 for the gel‐based cap prior and after the EEG recordings, respectively (scale 1–10). Wearing tests indicated that up to 3,200 applications are possible for the dry electrodes. The 256‐channel HD‐EEG dry electrode cap overcomes the principal limitations of HD‐EEG regarding preparation complexity and allows rapid application by not medically trained persons, enabling new use cases for HD‐EEG.

Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation

In the assistive research area, human-computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the complexity of the necessary algorithms and the difficulty of hardware implementation, there are few general-purpose designs that consider practicality and stability in real life. Therefore, to solve these limitations and problems, an HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides eye-state detection, including the fixation, saccade, and blinking states. Moreover, this algorithm can distinguish among ten kinds of saccade movements (i.e., up, down, left, right, farther left, farther right, up-left, down-left, up-right, and down-right). In addition, we developed an HCI system based on an eye-movement classification algorithm. This system provides an eye-dialing interface that can be used to improve the lives of people with disabilities. The results illustrate the good performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye-movement features, can be utilized in real-life applications.

Hardware Acceleration of EEG-Based Emotion Classification Systems: A Comprehensive Survey

Recent years have witnessed a growing interest in EEG-based wearable classifiers of emotions, which could enable the real-time monitoring of patients suffering from neurological disorders such as Amyotrophic Lateral Sclerosis (ALS), Autism Spectrum Disorder (ASD), or Alzheimer's. The hope is that such wearable emotion classifiers would facilitate the patients' social integration and lead to improved healthcare outcomes for them and their loved ones. Yet in spite of their direct relevance to neuro-medicine, the hardware platforms for emotion classification have yet to fill up some important gaps in their various approaches to emotion classification in a healthcare context. In this paper, we present the first hardware-focused critical review of EEG-based wearable classifiers of emotions and survey their implementation perspectives, their algorithmic foundations, and their feature extraction methodologies. We further provide a neuroscience-based analysis of current hardware accelerators of emotion classifiers and use it to map out several research opportunities, including multi-modal hardware platforms, accelerators with tightly-coupled cores operating robustly in the near/supra-threshold region, and pre-processing libraries for universal EEG-based datasets.

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.

Special Report on the Impact of the COVID-19 Pandemic on Clinical EEG and Research and Consensus Recommendations for the Safe Use of EEG

INTRODUCTION

The global COVID-19 pandemic has affected the economy, daily life, and mental/physical health. The latter includes the use of electroencephalography (EEG) in clinical practice and research. We report a survey of the impact of COVID-19 on the use of clinical EEG in practice and research in several countries, and the recommendations of an international panel of experts for the safe application of EEG during and after this pandemic.

METHODS

Fifteen clinicians from 8 different countries and 25 researchers from 13 different countries reported the impact of COVID-19 on their EEG activities, the procedures implemented in response to the COVID-19 pandemic, and precautions planned or already implemented during the reopening of EEG activities.

RESULTS

Of the 15 clinical centers responding, 11 reported a total stoppage of all EEG activities, while 4 reduced the number of tests per day. In research settings, all 25 laboratories reported a complete stoppage of activity, with 7 laboratories reopening to some extent since initial closure. In both settings, recommended precautions for restarting or continuing EEG recording included strict hygienic rules, social distance, and assessment for infection symptoms among staff and patients/participants.

CONCLUSIONS

The COVID-19 pandemic interfered with the use of EEG recordings in clinical practice and even more in clinical research. We suggest updated best practices to allow safe EEG recordings in both research and clinical settings. The continued use of EEG is important in those with psychiatric diseases, particularly in times of social alarm such as the COVID-19 pandemic.

Exploring the Limitations of Event-Related Potential Measures in Moving Subjects: Pilot Studies of Four Different Technical Modifications in Ergometer Rowing

Measuring brain activity in moving subjects is of great importance for investigating human behavior in ecological settings. For this purpose, EEG measures are applicable; however, technical modifications are required to reduce the typical massive movement artefacts. Four different approaches to measure EEG/ERPs during rowing were tested: (i) a purpose-built head-mounted preamplifier, (ii) a laboratory system with active electrodes, and a wireless headset combined with (iii) passive or (iv) active electrodes. A standard visual oddball task revealed very similar (within subjects) visual evoked potentials for rowing and rest (without movement). The small intraindividual differences between rowing and rest, in comparison to the typically larger interindividual differences in the ERP waveforms, revealed that ERPs can be measured reliably even in an athletic movement such as rowing. On the other hand, the expected modulation of the motor-related activity by force output was largely affected by movement artefacts. Therefore, for a successful application of ERP measures in movement research, further developments to differentiate between movement-related neuronal activity and movement-related artefacts are required. However, activities with small magnitudes related to motor learning and motor control may be difficult to detect because they are superimposed by the very large motor potential, which increases with force output.

Alteration in brain connectivity in patients with Dravet syndrome after vagus nerve stimulation (VNS): exploration of its effectiveness using graph theory analysis with electroencephalography

OBJECTIVE

Vagus nerve stimulation (VNS) is a nonpharmacologic therapeutic option for patients who have pharmaco-resistant Dravet syndrome (DS). Plentiful efforts have been made for delivering VNS to DS patients, but its effectiveness still requires further verification. We investigated the effectiveness of the VNS treatment of DS patients using brain connectivity analysis with electroencephalography (EEG).

APPROACH

Twenty pharmaco-resistant DS patients were selected to undergo VNS implantation and classified into responder and non-responder groups after 24 months post-VNS. The effect of VNS between 6 months pre- and 6, 12, and 24 months post-VNS in all patients, responders, and non-responders on four different frequency categories of four brain parameters were compared using resting-state EEG.

MAIN RESULTS

In alpha and beta bands, all patients showed positive results for characteristic path length (CPL), global efficiency (GE), and transitivity after VNS treatment, and changes in betweenness centrality (BC) were not significant. The difference in transitivity between responders and non-responders is more pronounced than those in CPL and GE are, in both the alpha (p < 0.015) and beta (p < 0.001) bands. There was an obvious change in BC, especially in the alpha band, as the hubs tended to move from frontal lobe to parietal lobe for responders; however, there was no change for the non-responders.

SIGNIFICANCE

We investigated the alteration in brain connectivity of DS patients in alpha and beta bands during a long-term follow-up and found the responders have a decreased transitivity after the VNS treatment. Moreover, the hubs with high values in the alpha band tended to move from frontal lobe to parietal lobe for responders after VNS treatment.

Developing and validating equations to predict V˙O2 peak from the 6MWT in Childhood ALL Survivors

INTRODUCTION

The 6-Minute Walking Test (6MWT) is a safe, standardized and well utilized method to assess the functional capacity. Recently, it was reported that the published prediction equations cannot accurately predict a valid maximal oxygen consumption (V̇O₂ peak) value in cancer survivors. Thus, the aim of this study was to establish and to validate a new equation based on the 6MWT to predict V̇O₂ peak in childhood acute lymphoblastic leukemia (ALL) survivors.

METHODS

A total of 200 childhood ALL survivors were enrolled in this study, among which 168 participants underwent a cardiopulmonary exercise test and a 6MWT to assess their functional capacity and their cardiorespiratory fitness. In addition, participants completed a physical activity questionnaire. Participants were randomly divided in two groups to establish the equations (n = 118 (70%)) and to validate it (n = 50 (30%)). Multiple linear regression analyses were used to determine a new prediction equation for V̇O₂ peak from 6MWT using clinical and specific variables related to the disease. The accuracy in between V̇O₂ peak measured and V̇O₂ peak predicted were assessed using the Bland and Altman method.

RESULTS

The new establish clinical V̇O₂ peak equation is: V̇O₂ peak (mL.kg^-1.min^-1) = (-0.283*age(years)) - (0.099*weight(kg)) + (0.071*6MWD(meters)) -(0.135*HR end(bpm)) + 22.789 with a mean bias of 2.67 mL.kg^-1.min^-1 (95% CI (-9.64 to 14.98)). The new establish disease-specific V̇O₂ peak equation is: V̇O₂ peak (mL.kg^-1.min^-1) = (-0.236*age(years)) - (0.094*weight(kg)) -(0.120*HR end(bpm)) + (0.067*6MWD(meters)) + (0.065*MVLPA(min/day)) - (0.204*DT(years)) + 25.145 with a mean bias of 2.51 mL.kg^-1.min^-1 (95% CI (-9.98 to 15.01)).

CONCLUSION

This is the first study that predicted V̇O₂ peak from a 6MWT using clinical and specific variables related to the disease of childhood ALL survivors. The availability of these newly established V̇O₂ peak equations makes them an accurate tool to provide a better follow-up and better adapted physical training for survivors. We invite researchers to use our assessment procedures for their further studies.IMPLICATIONS FOR REHABILITATIONIt is critical to understand the cardiorespiratory fitness of the childhood ALL survivorsThe maximal oxygen consumption (i.e., V̇O₂ peak) is recognized as the gold standard to measure the patient's cardiorespiratory fitness in the field of exercise physiologyThis study is novel and reports the validation of two new VO₂ peak equations, from 6MWT, by using clinical and disease-specific variables of childhood ALL survivorsThe availability of such validated equations can better facilitate the follow-up of survivors' cardiorespiratory fitness, by relevant health care professionals and exercise physiologists.

The Relevance of a Conductor Competition for the Study of Emotional Synchronization Within and Between Groups in a Natural Musical Setting

Group emotional dynamics are a central concern in the study of human interaction and communication. To study group emotions, the social context of a musical event in natural conditions may overcome several limits of laboratory experiments and could provide a suitable framework. This study aimed to evaluate if cultural events such as a conductor competition could welcome scientific research for the study of group emotional sharing. We led an observational study, which suggests that in this particular context, public, musicians and jury would agree to participate and to wear neurophysiological and physiological devices to monitor their emotional state during the competition. Self-administrated scales showed that, in the context of a musical competition, members of the public felt strong musical emotions such as music chills. Our results suggest that such a specific competition design is a suitable experimental model to lead an experiment under ecological conditions to effectively investigate collective emotional synchronization. In the future, with the implementation of an acquisition system recording synchronous neurophysiological data for a large group of participants, we may be able to highlight mechanisms involved in emotional synchronization in a natural musical setting.

Imaging the human hippocampus with optically-pumped magnetoencephalography

Optically-pumped (OP) magnetometers allow magnetoencephalography (MEG) to be performed while a participant's head is unconstrained. To fully leverage this new technology, and in particular its capacity for mobility, the activity of deep brain structures which facilitate explorative behaviours such as navigation, must be detectable using OP-MEG. One such crucial brain region is the hippocampus. Here we had three healthy adult participants perform a hippocampal-dependent task - the imagination of novel scene imagery - while being scanned using OP-MEG. A conjunction analysis across these three participants revealed a significant change in theta power in the medial temporal lobe. The peak of this activated cluster was located in the anterior hippocampus. We repeated the experiment with the same participants in a conventional SQUID-MEG scanner and found similar engagement of the medial temporal lobe, also with a peak in the anterior hippocampus. These OP-MEG findings indicate exciting new opportunities for investigating the neural correlates of a range of crucial cognitive functions in naturalistic contexts including spatial navigation, episodic memory and social interactions.

Automatic Removal of Cardiac Interference (ARCI): A New Approach for EEG Data

EEG recordings are generally affected by interference from physiological and non-physiological sources which may obscure underlying brain activity and hinder effective EEG analysis. In particular, cardiac interference can be caused by the electrical activity of the heart and/or cardiovascular activity related to blood flow. Successful EEG application in sports science settings requires a method for artifact removal that is automatic and flexible enough to be applied in a variety of acquisition conditions without requiring simultaneous ECG recordings that could restrict movement. We developed an automatic method for classifying and removing both electrical cardiac and cardiovascular artifacts (ARCI) that does not require additional ECG recording. Our method employs independent component analysis (ICA) to isolate data independent components (ICs) and identifies the artifactual ICs by evaluating specific IC features in the time and frequency domains. We applied ARCI to EEG datasets with cued artifacts and acquired during an eyes-closed condition. Data were recorded using a standard EEG wet cap with either 128 or 64 electrodes and using a novel dry electrode cap with either 97 or 64 dry electrodes. All data were decomposed into different numbers of components to evaluate the effect of ICA decomposition level on effective cardiac artifact detection. ARCI performance was evaluated by comparing automatic ICs classifications with classifications performed by experienced investigators. Automatic and investigator classifications were highly consistent resulting in an overall accuracy greater than 99% in all datasets and decomposition levels, and an average sensitivity greater than 90%. Best results were attained when data were decomposed into a fewer number of components where the method achieved perfect sensitivity (100%). Performance was also evaluated by comparing automatic component classification with externally recorded ECG. Results showed that ICs automatically classified as artifactual were significantly correlated with ECG activity whereas the other ICs were not. We also assessed that the interference affecting EEG signals was reduced by more than 82% after automatic artifact removal. Overall, ARCI represents a significant step in the detection and removal of cardiac-related EEG artifacts and can be applied in a variety of acquisition settings making it ideal for sports science applications.

Detection Analysis of Epileptic EEG Using a Novel Random Forest Model Combined With Grid Search Optimization

In the automatic detection of epileptic seizures, the monitoring of critically ill patients with time varying EEG signals is an essential procedure in intensive care units. There is an increasing interest in using EEG analysis to detect seizure, and in this study we aim to get a better understanding of how to visualize the information in the EEG time-frequency feature, and design and train a novel random forest algorithm for EEG decoding, especially for multiple-levels of illness. Here, we propose an automatic detection framework for epileptic seizure based on multiple time-frequency analysis approaches; it involves a novel random forest model combined with grid search optimization. The short-time Fourier transformation visualizes seizure features after normalization. The dimensionality of features is reduced through principal component analysis before feeding them into the classification model. The training parameters are optimized using grid search optimization to improve detection performance and diagnostic accuracy by in the recognition of three different levels epileptic of conditions (healthy subjects, seizure-free intervals, seizure activity). Our proposed model was used to classify 500 samples of raw EEG data, and multiple cross-validations were adopted to boost the modeling accuracy. Experimental results were evaluated by an accuracy, a confusion matrix, a receiver operating characteristic curve, and an area under the curve. The evaluations indicated that our model achieved the more effective classification than some previous typical methods. Such a scheme for computer-assisted clinical diagnosis of seizures has a potential guiding significance, which not only relieves the suffering of patient with epilepsy to improve quality of life, but also helps neurologists reduce their workload.

Focusing Attention on Muscle Exertion Increases EEG Coherence in an Endurance Cycling Task

The aim of this study was to examine EEG coherence before, during, and after time to exhaustion (TTE) trials in an endurance cycling task, as well as the effect of effort level and attentional focus (i.e., functional external, functional internal, and dysfunctional internal associative strategies-leading to Type 1, Type 2, and Type 3 performances) on brain functional connectivity. Eleven college-aged participants performed the TTE test on a cycle-ergometer with simultaneous EEG and rate of perceived exertion (RPE) monitoring. EEG data from 32 electrodes were divided into five effort level periods based on RPE values (Baseline, RPE 0-4, RPE 5-8, RPE 9-MAX, and Recovery). Within subjects RM-ANOVA was conducted to examine time to task completion across Type 1, Type 2, and Type 3 performance trials. RM-ANOVA (3 performance types × 5 effort levels) was also performed to compare the EEG coherence matrices in the alpha and beta bands for 13 pairs of electrodes (F3-F4, F3-P3, F4-P4, T7-T8, T7-P3, C3-C4, C3-P3, C4-P4, T8-P4, P3-P4, P3-O1, P4-O2, O2-O1). Significant differences were observed on TTE performance outcomes between Type 1 and Type 3, and between Type 2 and Type 3 performance states (p < 0.05), whereas Type 1 and Type 2 performance states did not differ. No significant main effects were observed on performance type (p > 0.05) for all frequency bands in any pair of electrodes of the coherence matrices. Higher EEG coherence values were observed at rest (Baseline) than during cycling (RPE 0-4, 5-8, 9-MAX) for all pairs of electrodes and EEG frequency bands irrespective of the type of performance (main effect of effort, p < 0.05). Interestingly, we observed a performance × effort interaction in C3-C4 in beta 3 band [F_{(4, 77)} = 2.62, p = 0.038] during RPE 9-MAX for Type 3 performance as compared to Type 1 and Type 2 performances. These findings may have practical implications in the development of performance optimization strategies in cycling, as we found that focusing attention on a core component of the action could stimulate functional connectivity among specific brain areas and lead to enhanced performance.