Recorded EEG Signals Research Articles

Effects of transcranial magnetic stimulation on cortical structures during motor imagination performance in the brain-computer interface

The aftermath of a stroke can frequently result in impaired motor functions causing problems performing habitual limb movements. These movement disorders stem from damage to the cerebral cortex and disruptions to neuronal connections in the central pyramidal pathways [1]. Restoring motor skills following a stroke is a time-consuming and challenging process, requiring resources from both external medical sources and the patient. Despite these challenges, it is still feasible for patients to regain control over their limb movements. The common approach for stroke neurorehabilitation consists of therapeutic physical activities and kinesiotherapy. These methods rely on afferent information during motor tasks to repair connections between intact brain areas [2]. Through exercising the impacted limbs, synaptic rearrangement happens in the cortex, awakening dormant neurons and increasing cortical areas adjacent to inactive regions. Although these techniques can partially restore movement, a significant number of stroke patients still face impairments. It is crucial to acknowledge the limitations of the current interventions and explore new approaches for better outcomes. Traditional rehabilitation methods often do not fully restore movement control, prompting researchers to explore alternative approaches. Motor-imagery-based brain-computer interfaces (BCIs) have gained attention recently [3, 4]. They allow for integrating various feedback mechanisms, which can be combined with upper and lower limb exoskeletons. The level of control a subject has over a BCI system directly affects the recovery process [5]. Introducing transcranial magnetic stimulation (TMS) into motor-imagery BCIs shows promise for developing a unified and highly efficient approach to post-stroke rehabilitation. Twenty-nine healthy adult participants (21 females) with a mean age of 20.93±2.14 years were recruited for this experiment. All participants were right-handed and had no prior experience with brain-computer interfaces. Ethical approval was obtained from the local ethics committee of Lobachevsky State University of Nizhny Novgorod (ethical approval No. 2, dated 03/19/2021), and written informed consent was obtained from all participants. Subjects were randomly assigned to receive either a sham intervention (n=15) or active TMS (n=14). The experiment displayed tasks on a 24-inch LCD screen placed 2 meters away. Participants sat in a reclining chair with hands on adjustable armrests for EEG signal recording. Motor-imagery-based BCI training took place over two days with four daily tasks: motor performance, quasi-motor, and two motor imaginations of the dominant hand. EEG activity was recorded prior to and after completing the tasks. Each task comprised of 20 trials, each lasting 10 seconds. TMS was administered in between two motor imagination tasks, and a rest period of 2 minutes followed the stimulation. The researcher objectively monitored electromyography (EMG) in real-time during motor tasks. Certified NVX 52 amplifiers with 32 Cl/Ag electrodes positioned according to the international 10-10 system recorded electroencephalography (EEG) signals. The EEG was digitized at a sampling rate of 1000 Hz and filtered with a 50 Hz Notch filter. Disposable electrodes were used to record EMG data from musculus flexor digitorum superficialis on the right hand. EMG signals were digitized at 1000 Hz and filtered using a 50 Hz Notch filter. TMS was applied to the dorsolateral prefrontal cortex (dlPFC) using a figure-8 coil connected to a Neuro MS/D magnetic stimulator. Sham stimulation was performed with the same parameters, except the coil was tilted 90 degrees to simulate the sound of real stimulation. A nonparametric permutation test was conducted to assess the statistically significant dissimilarities between rest periods and periods subsequent to conducting motor imagery after TMS. Significant clusters of the highest event-related desynchronization (ERD) were observed. The analysis demonstrated a significant negative cluster in the theta rhythm (0–6 Hz) that was distinct from the magnetic stimulation site. Similar significant ERD clusters were identified during the first and second series of imaginary movements. The study examined alterations in motor cortex function following targeted rTMS. The results demonstrated that TMS using particular parameters resulted in the pre-activation of brain regions comparable to those stimulated during motor imagery. The implementation of this stimulation protocol increased activity in cortical regions related to motor imagery. The outcomes propose the probable effectiveness of TMS in elevating motor cortex activation during the rehabilitation process.

An ecological study protocol for the multimodal investigation of the neurophysiological underpinnings of dyadic joint action.

A novel multimodal experimental setup and dyadic study protocol were designed to investigate the neurophysiological underpinnings of joint action through the synchronous acquisition of EEG, ECG, EMG, respiration and kinematic data from two individuals engaged in ecologic and naturalistic cooperative and competitive joint actions involving face-to-face real-time and real-space coordinated full body movements. Such studies are still missing because of difficulties encountered in recording reliable neurophysiological signals during gross body movements, in synchronizing multiple devices, and in defining suitable study protocols. The multimodal experimental setup includes the synchronous recording of EEG, ECG, EMG, respiration and kinematic signals of both individuals via two EEG amplifiers and a motion capture system that are synchronized via a single-board microcomputer and custom Python scripts. EEG is recorded using new dry sports electrode caps. The novel study protocol is designed to best exploit the multimodal data acquisitions. Table tennis is the dyadic motor task: it allows naturalistic and face-to-face interpersonal interactions, free in-time and in-space full body movement coordination, cooperative and competitive joint actions, and two task difficulty levels to mimic changing external conditions. Recording conditions-including minimum table tennis rally duration, sampling rate of kinematic data, total duration of neurophysiological recordings-were defined according to the requirements of a multilevel analytical approach including a neural level (hyperbrain functional connectivity, Graph Theoretical measures and Microstate analysis), a cognitive-behavioral level (integrated analysis of neural and kinematic data), and a social level (extending Network Physiology to neurophysiological data recorded from two interacting individuals). Four practical tests for table tennis skills were defined to select the study population, permitting to skill-match the dyad members and to form two groups of higher and lower skilled dyads to explore the influence of skill level on joint action performance. Psychometric instruments are included to assess personality traits and support interpretation of results. Studying joint action with our proposed protocol can advance the understanding of the neurophysiological mechanisms sustaining daily life joint actions and could help defining systems to predict cooperative or competitive behaviors before being overtly expressed, particularly useful in real-life contexts where social behavior is a main feature.

AI Oriented System to Identify Type of Epilepsy Seizure Using EEG Wave

For practical purposes, over the past three decades, several methods have been devised to mitigate various anomalies within corrupted EEG data. Nonetheless, there is still no universally acclaimed technique, rendering the field of study both intriguing and challenging. This research delves comprehensively into the identification and elimination of artifacts originating from ocular, muscular, and cardiac sources, offering a detailed analysis of their respective advantages and drawbacks. Furthermore, this investigation encompasses the methodologies employed for comparing real EEG data with simulated counterparts, serving to authenticate their efficacy. The primary focus of this work is twofold: firstly, to establish standardized criteria for the validation of recorded EEG signals in forthcoming studies, and secondly, to amalgamate diverse techniques through multiple processing stages, thereby facilitating the effective eradication of interference stemming from artifacts.

EOG Artifacts Suppression From single channel EEG Signals by VME-GMETV model

Nowadays the usage of portable electroencephalography (EEG) recorded systems are raised for recording of neuro activities of the brain, which are useful in clinical diagnosis such as brain–computer interface (BCI) applications. The eye movements or blinks are unavoidable activities, which reflects the electrooculogram (EOG) artifacts in the recorded EEG signals, which leads deceptive interpretation of the underlying brain state. In this paper a novel framework is proposed as Variational Mode Extraction (VME) method is integrated with Generalized Moreau Envelope Total Variation (GMETV) technique to filter the EOG artifacts. Initially, the contaminated EEG is applied to the VME method which extracts the EOG artifact segment. Then, this EOG artifact segment is given to the GMETV technique and it separates the wanted component of the EEG residue from the EOG artifact segment. The EEG residue is added back to the non EOG artifact segment signal which gives the final denoised EEG. Matlab simulations are done on both simulated and real time EEG databases to evolute the proposed VME-GMETV technique performance in terms of Relative Root Mean Square Error (RRMSE), Correlation Coefficient (CC) and Mean Absolute Error (MAE). From the experimental results, the proposed technique is outperformed the existing methods by averaged low RRMSE (0.1557), MAE (5.0714e−04) and averaged high CC (0.9695).

Quantitative Electroencephalography: Cortical Responses under Different Postural Conditions

In this study, the alpha and beta spectral frequency bands and amplitudes of EEG signals recorded from 10 healthy volunteers using an experimental cap with neoprene jacketed electrodes were analysed. Background: One of the main limitations in the analysis of EEG signals during movement is the presence of artefacts due to cranial muscle contraction; the objectives of this study therefore focused on two main aspects: (1) validating a tool capable of decreasing movement artefacts, while developing a reliable method for the quantitative analysis of EEG data; (2) using this method to analyse the EEG signal recorded during a particular motor activity (bi- and monopodalic postural control). Methods: The EEG sampling frequency was 512 Hz; the signal was acquired on 16 channels with monopolar montage and the reference on Cz. The recorded signals were processed using a specifically written Matlab script and also by exploiting open-source software (Eeglab). Results: The procedure used showed excellent reliability, allowing for a significant decrease in movement artefacts even during motor tasks performed both with eyes open and with eyes closed. Conclusions: This preliminary study lays the foundation for correctly recording EEG signals as an additional source of information in the study of human movement.

Detection and classification of adult epilepsy using hybrid deep learning approach

The electroencephalogram (EEG) has emerged over the past few decades as one of the key tools used by clinicians to detect seizures and other neurological abnormalities of the human brain. The proper diagnosis of epilepsy is crucial due to its distinctive nature and the subsequent negative effects of epileptic seizures on patients. The classification of minimally pre-processed, raw multichannel EEG signal recordings is the foundation of this article’s unique method for identifying seizures in pre-adult patients. The new method makes use of the automatic feature learning capabilities of a three-dimensional deep convolution auto-encoder (3D-DCAE) associated with a neural network-based classifier to build an integrated framework that endures training in a supervised manner to attain the highest level of classification precision among brain state signals, both ictal and interictal. A pair of models were created and evaluated for testing and assessing our method, utilizing three distinct EEG data section lengths, and a tenfold cross-validation procedure. Based on five evaluation criteria, the labelled hybrid convolutional auto-encoder (LHCAE) model, which utilizes a classifier based on bidirectional long short-term memory (Bi-LSTM) and an EEG segment length of 4 s, had the best efficiency. This proposed model has 99.08 ± 0.54% accuracy, 99.21 ± 0.50% sensitivity, 99.11 ± 0.57% specificity, 99.09 ± 0.55% precision, and an F1-score of 99.16 ± 0.58%, according to the publicly available Children’s Hospital Boston (CHB) dataset. Based on the obtained outcomes, the proposed seizure classification model outperforms the other state-of-the-art method’s performance in the same dataset.

Study of the Correlation between the Motor Ability of the Individual Upper Limbs and Motor Imagery Induced Neural Activities.

Motor imagery based brain-computer interfaces (MI-BCIs) have excellent application prospects in motor enhancement and rehabilitation. However, MI-induced electroencephalogram features applied to MI-BCI usually vary from person to person. This study aimed to investigate whether the motor ability of the individual upper limbs was associated with these features, which helps understand the causes of inter-subject variability. We focused on the behavioral and psychological factors reflecting motor abilities. We first obtained the behavioral scale scores from Edinburgh Handedness Questionnaire, Maximum Grip Strength Test, and Purdue Pegboard Test assessments to evaluate the motor execution ability. We also required the subjects to complete the psychological Movement Imagery Questionnaire-3 estimate, representing MI ability. Then we recorded EEG signals from all twenty-two subjects during MI tasks. Pearson correlation coefficient and stepwise regression were used to analyze the relationships between MI-induced relative event-related desynchronization (rERD) patterns and motor abilities. Both Purdue Pegboard Test and Movement Imagery Questionnaire-3 scores had significant correlations with MI-induced neural oscillation patterns. Notably, the Purdue Pegboard Test of the left hand had the most significant correlation with the alpha rERD. The results of stepwise multiple regression analysis showed that the Purdue Pegboard Test and Movement Imagery Questionnaire-3 could best predict the MI-induced rERD. The results demonstrate that hand dexterity and fine motor coordination are significantly related to MI-induced neural activities. In addition, the method of imagining is also relevant to MI features. Therefore, this study is meaningful for understanding individual differences and the design of user-centered MI-BCI.

Recognition of biosignals with nonlinear properties by approximate entropy parameters

More and more attention is being paid to the development of methods for the objective analysis of biosignals for computer medical systems. The search for new non-standard methods is aimed at improving the reliability of diagnostics and expanding the areas of their practical application. In this paper, methods for recognizing biomedical signals by the degree of severity of their nonlinear components are considered. An approach based on the use of approximate entropy closely related to Kolmogorov entropy (K-entropy) is used. Its parameters can be used to detect dynamic irregularities associated with nonlinear properties of signals. The algorithm for calculating this characteristic is consid-ered in detail. Based on model experiments, its main properties are analyzed. It is shown that the entropy of a finite sequence, calculated in accordance with a multistep pro-cedure, can give an erroneous estimate of the degree of regularity of the signal. A procedure for correcting the approximate entropy is proposed, which expands the area of analysis of this function for estimating nonlinearity. It has been established that the transition to adjusted entropy makes it possible to increase the reliability of the detection of chaotic components. A set of entropy parameters is proposed for constructing recognition procedures. Examples of solving the problems of detecting atrial fibrillation by the parameters of the non-linearity of the rhythmogram, as well as assessing the depth of anesthesia by the electroencephalogram (EEG) are given. Experiments conducted on real recordings of electrocardiogram (ECG) and EEG signals have shown the high efficiency of the proposed algorithms. The proposed methods and algorithms can be used in the development of systems for monitoring ECG of cardiological patients, as well as monitoring the depth of anesthesia by EEG during surgical operations.

Ocular Artifacts Elimination From Multivariate EEG Signal Using Frequency-Spatial Filtering

The electroencephalogram (EEG) signals record electrical activities generated by the brain cells and are used as a state-of-the-art diagnosis tool for various neural disorders. However, the unwanted artifacts often contaminate the recorded EEG signals and disturb the interpretation of the neuronal activity. This paper aims to propose an efficient automatic method to eliminate the ocular artifacts (OAs) from the multi-channel EEG signals with novel frequency-spatial filtering. The method combines dictionary-based spatial filtering and frequency based signal decomposition method, namely empirical wavelet transform (EWT). The artifact dictionary needed for spatial filtering is isolated from the raw data by (1) selecting the contaminated channels and (2) frequency-domain filtering. More precisely, the δ-rhythms of identified highly contaminated channels are selected and placed into an artifact dictionary. Afterward, the δ-rhythms of multi-channel EEG signals are spatially filtered using the built dictionary to seclude the OAs within a limited number of components. Further, the artifact components are eliminated and clean δ-rhythms are recovered using inverse spatial filtering technique. Finally, the clean δ-rhythms are combined with other EEG rhythms to reconstruct the OA-free signals. The proposed method is applied to OA contaminated synthetic and real multi-channel EEG signals with a convincing performance as compared to state of the art approaches. The proposed method removes the OAs without affecting the background EEG information. The proposed method can ease sensor signal interpretation and further processing, e.g. for BCI applications.

Learning classifiers in clustered data: BCI pattern recognition model for EEG-based human emotion recognition

Evidence suggests that human emotions can be detected using Electroencephalography (EEG) brain signals. Recorded EEG signals, due to their large size, may not initially perform well in classification. For this reason, various feature selection methods are used to improve the performance of classification. The nature of EEG signals is complex and unstable. This article uses the Empirical Mode Decomposition (EMD) method, which is one of the most successful methods in analyzing these signals in recent years. In the proposed model, first, the EEG signals are decomposed using EMD into the number of Intrinsic Mode Functions (IMF), and then, the statistical properties of the IMFs are extracted. To improve the performance of the proposed model, using the RBF kernel and Least Absolute Shrinkage and Selection Operator (LASSO) feature selection, an effective subset of the features that have changed the space is selected. The data are then clustered, and finally, each cluster is classified with a decision tree and random forest and KNN. The purpose of clustering is to increase the accuracy of the classification, which is achieved by focusing each cluster on a limited number of classes. This experiment was performed on the DEAP dataset. The results show that the proposed model with 99.17% accuracy could perform better than recent research such as deep learning and show good performance. In the latest years, with the development of the BCI system, the demand for recognizing emotions based on EEG has increased. We provide a method for classifying clustered data that is efficient for high accuracy.

Inter-subject correlations of EEG reflect subjective arousal and acoustic features of music.

Research on music-induced emotion and brain activity is constantly expanding. Although studies using inter-subject correlation (ISC), a collectively shared brain activity analysis method, have been conducted, whether ISC during music listening represents the music preferences of a large population remains uncertain; additionally, it remains unclear which factors influence ISC during music listening. Therefore, here, we aimed to investigate whether the ISCs of electroencephalography (EEG) during music listening represent a preference for music reflecting engagement or interest of a large population in music. First, we selected 21 pieces of music from the Billboard Japan Hot 100 chart of 2017, which served as an indicator of preference reflecting the engagement and interest of a large population. To ensure even representation, we chose one piece for every fifth song on the chart, spanning from highly popular music to less popular ones. Next, we recorded EEG signals while the subjects listened to the selected music, and they were asked to evaluate four aspects (preference, enjoyment, frequency of listening, and arousal) for each song. Subsequently, we conducted ISC analysis by utilizing the first three principal components of EEG, which were highly correlated across subjects and extracted through correlated component analysis (CorrCA). We then explored whether music with high preferences that reflected the engagement and interest of large population had high ISC values. Additionally, we employed cluster analysis on all 21 pieces of music, utilizing the first three principal components of EEG, to investigate the impact of emotions and musical characteristics on EEG ISC during music listening. A significant distinction was noted between the mean ISC values of the 10 higher-ranked pieces of music compared to the 10 lower-ranked pieces of music [t(542) = -1.97, p = 0.0025]. This finding suggests that ISC values may correspond preferences reflecting engagement or interest of a large population. Furthermore, we found that significant variations were observed in the first three principal component values among the three clusters identified through cluster analysis, along with significant differences in arousal levels. Moreover, the characteristics of the music (tonality and tempo) differed among the three clusters. This indicates that the principal components, which exhibit high correlation among subjects and were employed in calculating ISC values, represent both subjects' arousal levels and specific characteristics of the music. Subjects' arousal values during music listening and music characteristics (tonality and tempo) affect ISC values, which represent the interest of a large population in music.

Identifying relevant asymmetry features of EEG for emotion processing.

The left and right hemispheres of the brain process emotion differently. Neuroscientists have proposed two models to explain this difference. The first model states that the right hemisphere is dominant over the left to process all emotions. In contrast, the second model states that the left hemisphere processes positive emotions, whereas the right hemisphere processes negative emotions. Previous studies have used these asymmetry models to enhance the classification of emotions in machine learning models. However, little research has been conducted to explore how machine learning models can help identify associations between hemisphere asymmetries and emotion processing. To address this gap, we conducted two experiments using a subject-independent approach to explore how the asymmetry of the brain hemispheres is involved in processing happiness, sadness, fear, and neutral emotions. We analyzed electroencephalogram (EEG) signals from 15 subjects collected while they watched video clips evoking these four emotions. We derived asymmetry features from the recorded EEG signals by calculating the log ratio between the relative energy of symmetrical left and right nodes. Using the asymmetry features, we trained four binary logistic regressions, one for each emotion, to identify which features were more relevant to the predictions. The average AUC-ROC across the 15 subjects was 56.2, 54.6, 51.6, and 58.4% for neutral, sad, fear, and happy, respectively. We validated these results with an independent dataset, achieving comparable AUC-ROC values. Our results showed that brain lateralization was observed primarily in the alpha frequency bands, whereas for the other frequency bands, both hemispheres were involved in emotion processing. Furthermore, the logistic regression analysis indicated that the gamma and alpha bands were the most relevant for predicting emotional states, particularly for the lateral frontal, parietal, and temporal EEG pairs, such as FT7-FT8, T7-T8, and TP7-TP8. These findings provide valuable insights into which brain areas and frequency bands need to be considered when developing predictive models for emotion recognition.

A BCI system for imagined Bengali speech recognition

Non-invasive brain-computer interface (BCI) can be used in imagined speech recognition to aid people with speech disorders and has been investigated in different languages. However, this technology is in a very early stage for Bengali speech recognition. Therefore, the aim of this research is to develop a BCI system that can recognize imagined Bengali speech. In this study, a non-invasive 14-channel electroencephalography (EEG) headset was employed to record the EEG signals from 30 subjects. The subjects were instructed to imagine 11 Bengali vowels ▪ and 10 digits ▪. Then, we extracted four statistical features from the recorded EEG signals. A coarse-to-fine level classification was performed to categorize the imagined EEG data by exploiting these features. The data was classified at coarse level to distinguish between a vowel and a digit. Each vowel and digit were then classified at the fine level. In our classification, random forest achieved the highest recognition accuracy of 84.28% at the coarse level and 76.13% at the fine level. In order to comprehend the role played by each brain lobe in recognizing imagined speech, we additionally examined each of the brain lobes. The frontal lobe demonstrated the highest accuracy, with a coarse level accuracy of 80.65% and a fine level accuracy of 66.31%. Moreover, the proposed model outperformed the reported literature in this field.

PLI-Based Connectivity in Resting-EEG is a Robust and Generalizable Feature for Detecting MCI and AD: A Validation on a Diverse Multisite Clinical Dataset.

The high prevalence rate of Alzheimer's disease (AD) and mild cognitive impairment (MCI) has been a serious public health threat to the modern society. Recently, many studies have demonstrated the potential of using non-invasive electroencephalography (EEG) and machine learning to assist the diagnosis of AD/MCI. However, the majority of these research recorded EEG signals from a single center, leading to significant concerns regarding the generalizability of the findings in clinical settings. The current study aims to reevaluate the effectiveness of EEG-based machine learning model for the detection of AD/MCI in the case of a relatively large and diverse data set. We collected resting-state EEG data from 150 participants across six hospitals and examined the classification performances of Linear Discriminative Analysis (LDA) classifiers on the phase lag index (PLI) feature. We also compared the performance of PLI over the other commonly-used EEG features and other classifiers. The model was first tested on a training set to select the feature subset and then further validated with an independent test set. The results demonstrate that PLI performs the best compared to other features. The LDA classifier trained with the optimal PLI features can provide 82.50% leave-one-participant-out cross-validation (LOPO-CV) accuracy on the training set and maintain a good enough performance with 75.00% accuracy on the test set. Our results suggest that PLI-based functional connectivity could be considered as a reliable bio-maker to detect AD/MCI in the real-world clinical settings.

Design and implementation of high sampling rate and multichannel wireless recorder for EEG monitoring and SSVEP response detection.

The collection and process of human brain activity signals play an essential role in developing brain-computer interface (BCI) systems. A portable electroencephalogram (EEG) device has become an important tool for monitoring brain activity and diagnosing mental diseases. However, the miniaturization, portability, and scalability of EEG recorder are the current bottleneck in the research and application of BCI. For scalp EEG and other applications, the current study designs a 32-channel EEG recorder with a sampling rate up to 30 kHz and 16-bit accuracy, which can meet both the demands of scalp and intracranial EEG signal recording. A fully integrated electrophysiology microchip RHS2116 controlled by FPGA is employed to build the EEG recorder, and the design meets the requirements of high sampling rate, high transmission rate and channel extensive. The experimental results show that the developed EEG recorder provides a maximum 30 kHz sampling rate and 58 Mbps wireless transmission rate. The electrophysiological experiments were performed on scalp and intracranial EEG collection. An inflatable helmet with adjustable contact impedance was designed, and the pressurization can improve the SNR by approximately 4 times, the average accuracy of steady-state visual evoked potential (SSVEP) was 93.12%. Animal experiments were also performed on rats, and spike activity was captured successfully. The designed multichannel wireless EEG collection system is simple and comfort, the helmet-EEG recorder can capture the bioelectric signals without noticeable interference, and it has high measurement performance and great potential for practical application in BCI systems.

CNN-FEBAC: A framework for attention measurement of autistic individuals

Electroencephalogram (EEG) signals are a cost-effective and efficient method to measure and analyse neurological data and brain-related ailments. Autism Spectrum Disorder (ASD) is a globally prevalent neurological disorder that is of significant concern to the medical research community regarding its diagnosis and treatment. Artificial Intelligence (AI) algorithms utilized to study EEG signals of autistic patients have shown promising results to make progress in this domain. In this study, the authors have used the BCIAUT-P300 dataset for attention measurement and analysis of EEG signals of autistic patients. The dataset comprises the EEG signal data of ASD patients when they are exposed to external stimuli in a controlled environment. The authors propose a Convolutional Neural Network based Feature Extractor for BCI Attention Classification (CNN-FEBAC) framework to achieve the research objective of predicting the response of ASD patients by studying their EEG signal recordings. The CNN-FEBAC framework consists of a feature extractor architecture followed by a shallow classifier to predict the patient’s response to the stimuli. The proposed model was evaluated using performance metrics such as — confusion matrix, accuracy and F1 scores. The best accuracy achieved by the proposed model was 91%. The authors have explored and described the limitations of previously established methods and highlighted the performance improvements achieved with the proposed CNN-FEBAC framework. The authors further highlight the challenges encountered in the study and suggest the scope for improvement.

Deep Learning-Based Approach for Classification Of Mental Tasks From Electroencephalogram Signals

Background and Objective: Electroencephalography (EEG) analysis is an important tool for neuroscience, brain-computer interface studies, and biomedical studies. The primary purpose of Brain-Computer Interface (BCI) studies is to establish communication between disabled individuals, other individuals, and machines with brain signals. Interpreting and classifying the brain's response during different cognitive tasks will contribute to brain-computer interface studies. Therefore, in this study, five cognitive tasks were classified from EEG signals. Material and Methods: In this study, five neuropsychological tests (Öktem Verbal Memory Processes Test, WMS-R Visual Memory Subtest, Digit Span Test, Corsi Block Test, and Stroop Test) were administered to 30 healthy individuals. The tests assess the volunteers' abilities in verbal memory, visual memory, attention, concentration, working memory, and reaction time. The EEG signals were recorded while the tests were administered to the volunteers. The tests were classified using two different deep learning algorithms, 1D Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), from the recorded EEG signals. Results: When the success of the tests was evaluated, classification success was achieved with an accuracy of 88.53% in the CNN deep learning algorithm and 89.80% in the LSTM deep algorithm. Precision, recall, and F1-score values for CNN were calculated at 0.88, 0.87, and 0.87, respectively, while precision, recall, and f1-score values for the LSTM network were obtained at 0.90, 0.89, and 0.89. Conclusion: Following the findings of the present study, five different cognitive tasks were able to be classified with high accuracy from EEG signals using deep learning algorithms.

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.

Using machine learning methods and EEG to discriminate aircraft pilot cognitive workload during flight

Pilots of aircraft face varying degrees of cognitive workload even during normal flight operations. Periods of low cognitive workload may be followed by periods of high cognitive workload and vice versa. During such changing demands, there exists potential for increased error on behalf of the pilots due to periods of boredom or excessive cognitive task demand. To further understand cognitive workload in aviation, the present study involved collection of electroencephalogram (EEG) data from ten (10) collegiate aviation students in a live-flight environment in a single-engine aircraft. Each pilot possessed a Federal Aviation Administration (FAA) commercial pilot certificate and either FAA class I or class II medical certificate. Each pilot flew a standardized flight profile representing an average instrument flight training sequence. For data analysis, we used four main sub-bands of the recorded EEG signals: delta, theta, alpha, and beta. Power spectral density (PSD) and log energy entropy of each sub-band across 20 electrodes were computed and subjected to two feature selection algorithms (recursive feature elimination (RFE) and lasso cross-validation (LassoCV), and a stacking ensemble machine learning algorithm composed of support vector machine, random forest, and logistic regression. Also, hyperparameter optimization and tenfold cross-validation were used to improve the model performance, reliability, and generalization. The feature selection step resulted in 15 features that can be considered an indicator of pilots' cognitive workload states. Then these features were applied to the stacking ensemble algorithm, and the highest results were achieved using the selected features by the RFE algorithm with an accuracy of 91.67% (± 0.11), a precision of 93.89% (± 0.09), recall of 91.67% (± 0.11), F-score of 91.22% (± 0.12), and the mean ROC-AUC of 0.93 (± 0.06). The achieved results indicated that the combination of PSD and log energy entropy, along with well-designed machine learning algorithms, suggest the potential for the use of EEG to discriminate periods of the low, medium, and high workload to augment aircraft system design, including flight automation features to improve aviation safety.

Nitrogen‐Doped Multilayer Graphene Microtubes for High‐Density Recording of Occipital EEG Signals

AbstractGraphene microelectrodes exhibit potential in recording high‐density electroencephalography (EEG) signals from various brain regions. In this study, microtubes composed of nitrogen‐doped graphene (NG) nanosheets are synthesized by chemical vapor deposition to record high‐density EEG signals. The N‐content of the NG samples ranges from 1.35 to 2.22 at.%. One of the fabricated microtubes with an N‐content of 2.22% exhibits low scalp‐contact resistance and high signal‐to‐noise ratio (SNR) of EEG signals. The NG‐microtube has the advantages of high water‐retention, scalp affinity, water absorption, salt interception, and low resistance; these advantages reduce the scalp‐contact resistance and improve the response of EEG signals. Furthermore, an occipital 24‐lead EEG sensor with 72 optimized NG microtubes is assembled to record spontaneous and visually evoked EEG signals. Untrained volunteers are asked to wear the NG‐EEG sensor, and thus, spontaneous EEG with a high SNR and visually evoked EEG signals with an interval of 0.1 Hz are obtained easily. The occipital NG‐based EEG sensor is convenient to wear and can identify high‐density EEG signals. Therefore, the EEG sensor is not only suitable for the peripheral fine control of motion imagination but also used in the clinical diagnosis of functional disorders in various brain regions, promoting the development of healthcare electronics.