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EEG Signal Processing Techniques and Applications
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EEG Signal Processing Techniques and Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 42633

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Yifan Zhao

E-Mail Website
Guest Editor
School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, UK
Interests: computing, simulation and modelling; human factors; industrial automation; instrumentation, sensors and measurement science; systems engineering; through-life engineering services
Special Issues, Collections and Topics in MDPI journals
Dr. Fei He

E-Mail Website
Guest Editor
Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry CV1 2JH, UK
Interests: nonlinear signal processing; system identification; statistical machine learning; frequency-domain analysis; causality analysis; computational neuroscience
Special Issues, Collections and Topics in MDPI journals
Dr. Yuzhu Guo

E-Mail Website
Guest Editor
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Interests: brain dynamics and brain activities; brain–computer interfaces; AI for clinical disease diagnosis; neurorehabilitation; hybrid-augmented intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electroencephalography (EEG) is a well-established non-invasive tool to record brain electrophysiological activity. It is economical, portable, easy to administer and widely available in most hospitals. Compared with other neuroimaging techniques that provide information about the anatomical structure (e.g., MRI, CT and fMRI), EEG offers ultra-high time resolution, which is critical in understanding brain function. Empirical interpretation of EEG is largely based on recognizing abnormal frequencies in specific biological states, the spatial-temporal and morphological characteristics of paroxysmal or persistent discharges, reactivity to external stimuli and activation procedures or intermittent photic stimulation. Despite being useful in many instances, these practical approaches to interpreting EEGs can leave important dynamic and nonlinear interactions between various brain network anatomical constituents undetected within the recordings, as such interactions are far beyond the observational capabilities of any specially trained physician in this field.

This Special Issue will provide a forum for original high-quality research in EEG signal pre-processing, modelling, analysis, and applications in the time, space, frequency, or time-frequency domains. The applications of artificial intelligence and machine learning approaches in this topic are particularly welcomed. The covered applications include but are not limited to:

  • Clinical studies.
  • Human factors.
  • Brain–machine interfaces.
  • Psychology and neuroscience.
  • Social interactions.

Dr. Yifan Zhao
Dr. Fei He
Prof. Dr. Yuzhu Guo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electroencephalography
  • EEG signal processing
  • artificial intelligence in EEG data analysis
  • brain connectivity
  • time-frequency analysis
  • deep learning in EEG data analysis
  • machine learning techniques in EEG data analysis
  • computer-aided diagnosis systems

Published Papers (17 papers)

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Editorial

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5 pages, 186 KiB  
Editorial
EEG Signal Processing Techniques and Applications
by Yifan Zhao, Fei He and Yuzhu Guo
Sensors 2023, 23(22), 9056; https://doi.org/10.3390/s23229056 - 9 Nov 2023
Viewed by 1703
Abstract
Electroencephalography (EEG) is a widely recognised non-invasive method for capturing brain electrophysiological activity [...] Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications)

Research

Jump to: Editorial

20 pages, 7652 KiB  
Article
Multimodal Approach for Pilot Mental State Detection Based on EEG
by Ibrahim Alreshidi, Irene Moulitsas and Karl W. Jenkins
Sensors 2023, 23(17), 7350; https://doi.org/10.3390/s23177350 - 23 Aug 2023
Cited by 5 | Viewed by 1411
Abstract
The safety of flight operations depends on the cognitive abilities of pilots. In recent years, there has been growing concern about potential accidents caused by the declining mental states of pilots. We have developed a novel multimodal approach for mental state detection in [...] Read more.
The safety of flight operations depends on the cognitive abilities of pilots. In recent years, there has been growing concern about potential accidents caused by the declining mental states of pilots. We have developed a novel multimodal approach for mental state detection in pilots using electroencephalography (EEG) signals. Our approach includes an advanced automated preprocessing pipeline to remove artefacts from the EEG data, a feature extraction method based on Riemannian geometry analysis of the cleaned EEG data, and a hybrid ensemble learning technique that combines the results of several machine learning classifiers. The proposed approach provides improved accuracy compared to existing methods, achieving an accuracy of 86% when tested on cleaned EEG data. The EEG dataset was collected from 18 pilots who participated in flight experiments and publicly released at NASA’s open portal. This study presents a reliable and efficient solution for detecting mental states in pilots and highlights the potential of EEG signals and ensemble learning algorithms in developing cognitive cockpit systems. The use of an automated preprocessing pipeline, feature extraction method based on Riemannian geometry analysis, and hybrid ensemble learning technique set this work apart from previous efforts in the field and demonstrates the innovative nature of the proposed approach. Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications)
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23 pages, 8261 KiB  
Article
Modulations of Cortical Power and Connectivity in Alpha and Beta Bands during the Preparation of Reaching Movements
by Davide Borra, Silvia Fantozzi, Maria Cristina Bisi and Elisa Magosso
Sensors 2023, 23(7), 3530; https://doi.org/10.3390/s23073530 - 28 Mar 2023
Cited by 1 | Viewed by 1648
Abstract
Planning goal-directed movements towards different targets is at the basis of common daily activities (e.g., reaching), involving visual, visuomotor, and sensorimotor brain areas. Alpha (8–13 Hz) and beta (13–30 Hz) oscillations are modulated during movement preparation and are implicated in correct motor functioning. [...] Read more.
Planning goal-directed movements towards different targets is at the basis of common daily activities (e.g., reaching), involving visual, visuomotor, and sensorimotor brain areas. Alpha (8–13 Hz) and beta (13–30 Hz) oscillations are modulated during movement preparation and are implicated in correct motor functioning. However, how brain regions activate and interact during reaching tasks and how brain rhythms are functionally involved in these interactions is still limitedly explored. Here, alpha and beta brain activity and connectivity during reaching preparation are investigated at EEG-source level, considering a network of task-related cortical areas. Sixty-channel EEG was recorded from 20 healthy participants during a delayed center-out reaching task and projected to the cortex to extract the activity of 8 cortical regions per hemisphere (2 occipital, 2 parietal, 3 peri-central, 1 frontal). Then, we analyzed event-related spectral perturbations and directed connectivity, computed via spectral Granger causality and summarized using graph theory centrality indices (in degree, out degree). Results suggest that alpha and beta oscillations are functionally involved in the preparation of reaching in different ways, with the former mediating the inhibition of the ipsilateral sensorimotor areas and disinhibition of visual areas, and the latter coordinating disinhibition of the contralateral sensorimotor and visuomotor areas. Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications)
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11 pages, 1339 KiB  
Article
Extraction of Individual EEG Gamma Frequencies from the Responses to Click-Based Chirp-Modulated Sounds
by Aurimas Mockevičius, Yusuke Yokota, Povilas Tarailis, Hatsunori Hasegawa, Yasushi Naruse and Inga Griškova-Bulanova
Sensors 2023, 23(5), 2826; https://doi.org/10.3390/s23052826 - 4 Mar 2023
Cited by 2 | Viewed by 1916
Abstract
Activity in the gamma range is related to many sensory and cognitive processes that are impaired in neuropsychiatric conditions. Therefore, individualized measures of gamma-band activity are considered to be potential markers that reflect the state of networks within the brain. Relatively little has [...] Read more.
Activity in the gamma range is related to many sensory and cognitive processes that are impaired in neuropsychiatric conditions. Therefore, individualized measures of gamma-band activity are considered to be potential markers that reflect the state of networks within the brain. Relatively little has been studied in respect of the individual gamma frequency (IGF) parameter. The methodology for determining the IGF is not well established. In the present work, we tested the extraction of IGFs from electroencephalogram (EEG) data in two datasets where subjects received auditory stimulation consisting of clicks with varying inter-click periods, covering a 30–60 Hz range: in 80 young subjects EEG was recorded with 64 gel-based electrodes; in 33 young subjects, EEG was recorded using three active dry electrodes. IGFs were extracted from either fifteen or three electrodes in frontocentral regions by estimating the individual-specific frequency that most consistently exhibited high phase locking during the stimulation. The method showed overall high reliability of extracted IGFs for all extraction approaches; however, averaging over channels resulted in somewhat higher reliability scores. This work demonstrates that the estimation of individual gamma frequency is possible using a limited number of both the gel and dry electrodes from responses to click-based chirp-modulated sounds. Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications)
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18 pages, 3144 KiB  
Article
A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing
by Vangelis P. Oikonomou, Kostas Georgiadis, Fotis Kalaganis, Spiros Nikolopoulos and Ioannis Kompatsiaris
Sensors 2023, 23(5), 2480; https://doi.org/10.3390/s23052480 - 23 Feb 2023
Cited by 4 | Viewed by 1876
Abstract
In this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli using EEG signals. The most crucial component of our approach is the proposed classification algorithm that is based on a sparse representation [...] Read more.
In this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli using EEG signals. The most crucial component of our approach is the proposed classification algorithm that is based on a sparse representation classification scheme. The basic assumption of our approach is that EEG features from a cognitive or affective process lie on a linear subspace. Hence, a test brain signal can be represented as a linear (or weighted) combination of brain signals from all classes in the training set. The class membership of the brain signals is determined by adopting the Sparse Bayesian Framework with graph-based priors over the weights of linear combination. Furthermore, the classification rule is constructed by using the residuals of linear combination. The experiments on a publicly available neuromarketing EEG dataset demonstrate the usefulness of our approach. For the two classification tasks offered by the employed dataset, namely affective state recognition and cognitive state recognition, the proposed classification scheme manages to achieve a higher classification accuracy compared to the baseline and state-of-the art methods (more than 8% improvement in classification accuracy). Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications)
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18 pages, 2576 KiB  
Article
Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models
by Chia-Yen Yang, Pin-Chen Chen and Wen-Chen Huang
Sensors 2023, 23(5), 2458; https://doi.org/10.3390/s23052458 - 23 Feb 2023
Cited by 4 | Viewed by 2491
Abstract
Electroencephalography (EEG) is often used to evaluate several types of neurological brain disorders because of its noninvasive and high temporal resolution. In contrast to electrocardiography (ECG), EEG can be uncomfortable and inconvenient for patients. Moreover, deep-learning techniques require a large dataset and a [...] Read more.
Electroencephalography (EEG) is often used to evaluate several types of neurological brain disorders because of its noninvasive and high temporal resolution. In contrast to electrocardiography (ECG), EEG can be uncomfortable and inconvenient for patients. Moreover, deep-learning techniques require a large dataset and a long time for training from scratch. Therefore, in this study, EEG–EEG or EEG–ECG transfer learning strategies were applied to explore their effectiveness for the training of simple cross-domain convolutional neural networks (CNNs) used in seizure prediction and sleep staging systems, respectively. The seizure model detected interictal and preictal periods, whereas the sleep staging model classified signals into five stages. The patient-specific seizure prediction model with six frozen layers achieved 100% accuracy for seven out of nine patients and required only 40 s of training time for personalization. Moreover, the cross-signal transfer learning EEG–ECG model for sleep staging achieved an accuracy approximately 2.5% higher than that of the ECG model; additionally, the training time was reduced by >50%. In summary, transfer learning from an EEG model to produce personalized models for a more convenient signal can both reduce the training time and increase the accuracy; moreover, challenges such as data insufficiency, variability, and inefficiency can be effectively overcome. Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications)
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24 pages, 3286 KiB  
Article
An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG
by Lamiaa Abdel-Hamid
Sensors 2023, 23(3), 1255; https://doi.org/10.3390/s23031255 - 21 Jan 2023
Cited by 7 | Viewed by 2800
Abstract
Emotion artificial intelligence (AI) is being increasingly adopted in several industries such as healthcare and education. Facial expressions and tone of speech have been previously considered for emotion recognition, yet they have the drawback of being easily manipulated by subjects to mask their [...] Read more.
Emotion artificial intelligence (AI) is being increasingly adopted in several industries such as healthcare and education. Facial expressions and tone of speech have been previously considered for emotion recognition, yet they have the drawback of being easily manipulated by subjects to mask their true emotions. Electroencephalography (EEG) has emerged as a reliable and cost-effective method to detect true human emotions. Recently, huge research effort has been put to develop efficient wearable EEG devices to be used by consumers in out of the lab scenarios. In this work, a subject-dependent emotional valence recognition method is implemented that is intended for utilization in emotion AI applications. Time and frequency features were computed from a single time series derived from the Fp1 and Fp2 channels. Several analyses were performed on the strongest valence emotions to determine the most relevant features, frequency bands, and EEG timeslots using the benchmark DEAP dataset. Binary classification experiments resulted in an accuracy of 97.42% using the alpha band, by that outperforming several approaches from literature by ~3–22%. Multiclass classification gave an accuracy of 95.0%. Feature computation and classification required less than 0.1 s. The proposed method thus has the advantage of reduced computational complexity as, unlike most methods in the literature, only two EEG channels were considered. In addition, minimal features concluded from the thorough analyses conducted in this study were used to achieve state-of-the-art performance. The implemented EEG emotion recognition method thus has the merits of being reliable and easily reproducible, making it well-suited for wearable EEG devices. Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications)
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16 pages, 4713 KiB  
Article
Estimating the Depth of Anesthesia from EEG Signals Based on a Deep Residual Shrinkage Network
by Meng Shi, Ziyu Huang, Guowen Xiao, Bowen Xu, Quansheng Ren and Hong Zhao
Sensors 2023, 23(2), 1008; https://doi.org/10.3390/s23021008 - 15 Jan 2023
Cited by 5 | Viewed by 3011
Abstract
The reliable monitoring of the depth of anesthesia (DoA) is essential to control the anesthesia procedure. Electroencephalography (EEG) has been widely used to estimate DoA since EEG could reflect the effect of anesthetic drugs on the central nervous system (CNS). In this study, [...] Read more.
The reliable monitoring of the depth of anesthesia (DoA) is essential to control the anesthesia procedure. Electroencephalography (EEG) has been widely used to estimate DoA since EEG could reflect the effect of anesthetic drugs on the central nervous system (CNS). In this study, we propose that a deep learning model consisting mainly of a deep residual shrinkage network (DRSN) and a 1 × 1 convolution network could estimate DoA in terms of patient state index (PSI) values. First, we preprocessed the four raw channels of EEG signals to remove electrical noise and other physiological signals. The proposed model then takes the preprocessed EEG signals as inputs to predict PSI values. Then we extracted 14 features from the preprocessed EEG signals and implemented three conventional feature-based models as comparisons. A dataset of 18 patients was used to evaluate the models’ performances. The results of the five-fold cross-validation show that there is a relatively high similarity between the ground-truth PSI values and the predicted PSI values of our proposed model, which outperforms the conventional models, and further, that the Spearman’s rank correlation coefficient is 0.9344. In addition, an ablation experiment was conducted to demonstrate the effectiveness of the soft-thresholding module for EEG-signal processing, and a cross-subject validation was implemented to illustrate the robustness of the proposed method. In summary, the procedure is not merely feasible for estimating DoA by mimicking PSI values but also inspired us to develop a precise DoA-estimation system with more convincing assessments of anesthetization levels. Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications)
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19 pages, 1457 KiB  
Article
Comprehensive Analysis of Feature Extraction Methods for Emotion Recognition from Multichannel EEG Recordings
by Rajamanickam Yuvaraj, Prasanth Thagavel, John Thomas, Jack Fogarty and Farhan Ali
Sensors 2023, 23(2), 915; https://doi.org/10.3390/s23020915 - 12 Jan 2023
Cited by 13 | Viewed by 3445
Abstract
Advances in signal processing and machine learning have expedited electroencephalogram (EEG)-based emotion recognition research, and numerous EEG signal features have been investigated to detect or characterize human emotions. However, most studies in this area have used relatively small monocentric data and focused on [...] Read more.
Advances in signal processing and machine learning have expedited electroencephalogram (EEG)-based emotion recognition research, and numerous EEG signal features have been investigated to detect or characterize human emotions. However, most studies in this area have used relatively small monocentric data and focused on a limited range of EEG features, making it difficult to compare the utility of different sets of EEG features for emotion recognition. This study addressed that by comparing the classification accuracy (performance) of a comprehensive range of EEG feature sets for identifying emotional states, in terms of valence and arousal. The classification accuracy of five EEG feature sets were investigated, including statistical features, fractal dimension (FD), Hjorth parameters, higher order spectra (HOS), and those derived using wavelet analysis. Performance was evaluated using two classifier methods, support vector machine (SVM) and classification and regression tree (CART), across five independent and publicly available datasets linking EEG to emotional states: MAHNOB-HCI, DEAP, SEED, AMIGOS, and DREAMER. The FD-CART feature-classification method attained the best mean classification accuracy for valence (85.06%) and arousal (84.55%) across the five datasets. The stability of these findings across the five different datasets also indicate that FD features derived from EEG data are reliable for emotion recognition. The results may lead to the possible development of an online feature extraction framework, thereby enabling the development of an EEG-based emotion recognition system in real time. Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications)
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12 pages, 3996 KiB  
Article
Electroencephalography Reflects User Satisfaction in Controlling Robot Hand through Electromyographic Signals
by Hyeonseok Kim, Makoto Miyakoshi, Yeongdae Kim, Sorawit Stapornchaisit, Natsue Yoshimura and Yasuharu Koike
Sensors 2023, 23(1), 277; https://doi.org/10.3390/s23010277 - 27 Dec 2022
Viewed by 1657
Abstract
This study addresses time intervals during robot control that dominate user satisfaction and factors of robot movement that induce satisfaction. We designed a robot control system using electromyography signals. In each trial, participants were exposed to different experiences as the cutoff frequencies of [...] Read more.
This study addresses time intervals during robot control that dominate user satisfaction and factors of robot movement that induce satisfaction. We designed a robot control system using electromyography signals. In each trial, participants were exposed to different experiences as the cutoff frequencies of a low-pass filter were changed. The participants attempted to grab a bottle by controlling a robot. They were asked to evaluate four indicators (stability, imitation, response time, and movement speed) and indicate their satisfaction at the end of each trial by completing a questionnaire. The electroencephalography signals of the participants were recorded while they controlled the robot and responded to the questionnaire. Two independent component clusters in the precuneus and postcentral gyrus were the most sensitive to subjective evaluations. For the moment that dominated satisfaction, we observed that brain activity exhibited significant differences in satisfaction not immediately after feeding an input but during the later stage. The other indicators exhibited independently significant patterns in event-related spectral perturbations. Comparing these indicators in a low-frequency band related to the satisfaction with imitation and movement speed, which had significant differences, revealed that imitation covered significant intervals in satisfaction. This implies that imitation was the most important contributing factor among the four indicators. Our results reveal that regardless of subjective satisfaction, objective performance evaluation might more fully reflect user satisfaction. Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications)
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27 pages, 1474 KiB  
Article
An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications
by Syed Mohsin Ali Shah, Syed Muhammad Usman, Shehzad Khalid, Ikram Ur Rehman, Aamir Anwar, Saddam Hussain, Syed Sajid Ullah, Hela Elmannai, Abeer D. Algarni and Waleed Manzoor
Sensors 2022, 22(24), 9744; https://doi.org/10.3390/s22249744 - 12 Dec 2022
Cited by 8 | Viewed by 3060
Abstract
Traditional advertising techniques seek to govern the consumer’s opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers’ actual purchase [...] Read more.
Traditional advertising techniques seek to govern the consumer’s opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers’ actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behavior and decision making, as well as the prediction of their gestures for product utilization through an unconscious process. Existing methods do not focus on effective preprocessing and classification techniques of electroencephalogram (EEG) signals, so in this study, an effective method for preprocessing and classification of EEG signals is proposed. The proposed method involves effective preprocessing of EEG signals by removing noise and a synthetic minority oversampling technique (SMOTE) to deal with the class imbalance problem. The dataset employed in this study is a publicly available neuromarketing dataset. Automated features were extracted by using a long short-term memory network (LSTM) and then concatenated with handcrafted features like power spectral density (PSD) and discrete wavelet transform (DWT) to create a complete feature set. The classification was done by using the proposed hybrid classifier that optimizes the weights of two machine learning classifiers and one deep learning classifier and classifies the data between like and dislike. The machine learning classifiers include the support vector machine (SVM), random forest (RF), and deep learning classifier (DNN). The proposed hybrid model outperforms other classifiers like RF, SVM, and DNN and achieves an accuracy of 96.89%. In the proposed method, accuracy, sensitivity, specificity, precision, and F1 score were computed to evaluate and compare the proposed method with recent state-of-the-art methods. Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications)
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16 pages, 2217 KiB  
Article
Implementing Performance Accommodation Mechanisms in Online BCI for Stroke Rehabilitation: A Study on Perceived Control and Frustration
by Mads Jochumsen, Bastian Ilsø Hougaard, Mathias Sand Kristensen and Hendrik Knoche
Sensors 2022, 22(23), 9051; https://doi.org/10.3390/s22239051 - 22 Nov 2022
Cited by 2 | Viewed by 1720
Abstract
Brain–computer interfaces (BCIs) are successfully used for stroke rehabilitation, but the training is repetitive and patients can lose the motivation to train. Moreover, controlling the BCI may be difficult, which causes frustration and leads to even worse control. Patients might not adhere to [...] Read more.
Brain–computer interfaces (BCIs) are successfully used for stroke rehabilitation, but the training is repetitive and patients can lose the motivation to train. Moreover, controlling the BCI may be difficult, which causes frustration and leads to even worse control. Patients might not adhere to the regimen due to frustration and lack of motivation/engagement. The aim of this study was to implement three performance accommodation mechanisms (PAMs) in an online motor imagery-based BCI to aid people and evaluate their perceived control and frustration. Nineteen healthy participants controlled a fishing game with a BCI in four conditions: (1) no help, (2) augmented success (augmented successful BCI-attempt), (3) mitigated failure (turn unsuccessful BCI-attempt into neutral output), and (4) override input (turn unsuccessful BCI-attempt into successful output). Each condition was followed-up and assessed with Likert-scale questionnaires and a post-experiment interview. Perceived control and frustration were best predicted by the amount of positive feedback the participant received. PAM-help increased perceived control for poor BCI-users but decreased it for good BCI-users. The input override PAM frustrated the users the most, and they differed in how they wanted to be helped. By using PAMs, developers have more freedom to create engaging stroke rehabilitation games. Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications)
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21 pages, 5698 KiB  
Article
Learning Optimal Time-Frequency-Spatial Features by the CiSSA-CSP Method for Motor Imagery EEG Classification
by Hai Hu, Zihang Pu, Haohan Li, Zhexian Liu and Peng Wang
Sensors 2022, 22(21), 8526; https://doi.org/10.3390/s22218526 - 5 Nov 2022
Cited by 7 | Viewed by 1819
Abstract
The common spatial pattern (CSP) is a popular method in feature extraction for motor imagery (MI) electroencephalogram (EEG) classification in brain–computer interface (BCI) systems. However, combining temporal and spectral information in the CSP-based spatial features is still a challenging issue, which greatly affects [...] Read more.
The common spatial pattern (CSP) is a popular method in feature extraction for motor imagery (MI) electroencephalogram (EEG) classification in brain–computer interface (BCI) systems. However, combining temporal and spectral information in the CSP-based spatial features is still a challenging issue, which greatly affects the performance of MI-based BCI systems. Here, we propose a novel circulant singular spectrum analysis embedded CSP (CiSSA-CSP) method for learning the optimal time-frequency-spatial features to improve the MI classification accuracy. Specifically, raw EEG data are first segmented into multiple time segments and spectrum-specific sub-bands are further derived by CiSSA from each time segment in a set of non-overlapping filter bands. CSP features extracted from all time-frequency segments contain more sufficient time-frequency-spatial information. An experimental study was implemented on the publicly available EEG dataset (BCI Competition III dataset IVa) and a self-collected experimental EEG dataset to validate the effectiveness of the CiSSA-CSP method. Experimental results demonstrate that discriminative and robust features are extracted effectively. Compared with several state-of-the-art methods, the proposed method exhibited optimal accuracies of 96.6% and 95.2% on the public and experimental datasets, respectively, which confirms that it is a promising method for improving the performance of MI-based BCIs. Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications)
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12 pages, 2697 KiB  
Article
Spatio-Temporal Neural Dynamics of Observing Non-Tool Manipulable Objects and Interactions
by Zhaoxuan Li and Keiji Iramina
Sensors 2022, 22(20), 7771; https://doi.org/10.3390/s22207771 - 13 Oct 2022
Cited by 1 | Viewed by 1300
Abstract
Previous studies have reported that a series of sensory–motor-related cortical areas are affected when a healthy human is presented with images of tools. This phenomenon has been explained as familiar tools launching a memory-retrieval process to provide a basis for using the tools. [...] Read more.
Previous studies have reported that a series of sensory–motor-related cortical areas are affected when a healthy human is presented with images of tools. This phenomenon has been explained as familiar tools launching a memory-retrieval process to provide a basis for using the tools. Consequently, we postulated that this theory may also be applicable if images of tools were replaced with images of daily objects if they are graspable (i.e., manipulable). Therefore, we designed and ran experiments with human volunteers (participants) who were visually presented with images of three different daily objects and recorded their electroencephalography (EEG) synchronously. Additionally, images of these objects being grasped by human hands were presented to the participants. Dynamic functional connectivity between the visual cortex and all the other areas of the brain was estimated to find which of them were influenced by visual stimuli. Next, we compared our results with those of previous studies that investigated brain response when participants looked at tools and concluded that manipulable objects caused similar cerebral activity to tools. We also looked into mu rhythm and found that looking at a manipulable object did not elicit a similar activity to seeing the same object being grasped. Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications)
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17 pages, 6019 KiB  
Article
EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning
by Jun Cao, Enara Martin Garro and Yifan Zhao
Sensors 2022, 22(19), 7623; https://doi.org/10.3390/s22197623 - 8 Oct 2022
Cited by 13 | Viewed by 3564
Abstract
There is high demand for techniques to estimate human mental workload during some activities for productivity enhancement or accident prevention. Most studies focus on a single physiological sensing modality and use univariate methods to analyse multi-channel electroencephalography (EEG) data. This paper proposes a [...] Read more.
There is high demand for techniques to estimate human mental workload during some activities for productivity enhancement or accident prevention. Most studies focus on a single physiological sensing modality and use univariate methods to analyse multi-channel electroencephalography (EEG) data. This paper proposes a new framework that relies on the features of hybrid EEG–functional near-infrared spectroscopy (EEG–fNIRS), supported by machine-learning features to deal with multi-level mental workload classification. Furthermore, instead of the well-used univariate power spectral density (PSD) for EEG recording, we propose using bivariate functional brain connectivity (FBC) features in the time and frequency domains of three bands: delta (0.5–4 Hz), theta (4–7 Hz) and alpha (8–15 Hz). With the assistance of the fNIRS oxyhemoglobin and deoxyhemoglobin (HbO and HbR) indicators, the FBC technique significantly improved classification performance at a 77% accuracy for 0-back vs. 2-back and 83% for 0-back vs. 3-back using a public dataset. Moreover, topographic and heat-map visualisation indicated that the distinguishing regions for EEG and fNIRS showed a difference among the 0-back, 2-back and 3-back test results. It was determined that the best region to assist the discrimination of the mental workload for EEG and fNIRS is different. Specifically, the posterior area performed the best for the posterior midline occipital (POz) EEG in the alpha band and fNIRS had superiority in the right frontal region (AF8). Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications)
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13 pages, 4085 KiB  
Article
A Classification Model of EEG Signals Based on RNN-LSTM for Diagnosing Focal and Generalized Epilepsy
by Tahereh Najafi, Rosmina Jaafar, Rabani Remli and Wan Asyraf Wan Zaidi
Sensors 2022, 22(19), 7269; https://doi.org/10.3390/s22197269 - 25 Sep 2022
Cited by 10 | Viewed by 2667
Abstract
Epilepsy is a chronic neurological disorder caused by abnormal neuronal activity that is diagnosed visually by analyzing electroencephalography (EEG) signals. Background: Surgical operations are the only option for epilepsy treatment when patients are refractory to treatment, which highlights the role of classifying focal [...] Read more.
Epilepsy is a chronic neurological disorder caused by abnormal neuronal activity that is diagnosed visually by analyzing electroencephalography (EEG) signals. Background: Surgical operations are the only option for epilepsy treatment when patients are refractory to treatment, which highlights the role of classifying focal and generalized epilepsy syndrome. Therefore, developing a model to be used for diagnosing focal and generalized epilepsy automatically is important. Methods: A classification model based on longitudinal bipolar montage (LB), discrete wavelet transform (DWT), feature extraction techniques, and statistical analysis in feature selection for RNN combined with long short-term memory (LSTM) is proposed in this work for identifying epilepsy. Initially, normal and epileptic LB channels were decomposed into three levels, and 15 various features were extracted. The selected features were extracted from each segment of the signals and fed into LSTM for the classification approach. Results: The proposed algorithm achieved a 96.1% accuracy, a 96.8% sensitivity, and a 97.4% specificity in distinguishing normal subjects from subjects with epilepsy. This optimal model was used to analyze the channels of subjects with focal and generalized epilepsy for diagnosing purposes, relying on statistical parameters. Conclusions: The proposed approach is promising, as it can be used to detect epilepsy with satisfactory classification performance and diagnose focal and generalized epilepsy. Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications)
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18 pages, 2327 KiB  
Article
Epileptic Disorder Detection of Seizures Using EEG Signals
by Mariam K. Alharthi, Kawthar M. Moria, Daniyal M. Alghazzawi and Haythum O. Tayeb
Sensors 2022, 22(17), 6592; https://doi.org/10.3390/s22176592 - 31 Aug 2022
Cited by 12 | Viewed by 3264
Abstract
Epilepsy is a nervous system disorder. Encephalography (EEG) is a generally utilized clinical approach for recording electrical activity in the brain. Although there are a number of datasets available, most of them are imbalanced due to the presence of fewer epileptic EEG signals [...] Read more.
Epilepsy is a nervous system disorder. Encephalography (EEG) is a generally utilized clinical approach for recording electrical activity in the brain. Although there are a number of datasets available, most of them are imbalanced due to the presence of fewer epileptic EEG signals compared with non-epileptic EEG signals. This research aims to study the possibility of integrating local EEG signals from an epilepsy center in King Abdulaziz University hospital into the CHB-MIT dataset by applying a new compatibility framework for data integration. The framework comprises multiple functions, which include dominant channel selection followed by the implementation of a novel algorithm for reading XLtek EEG data. The resulting integrated datasets, which contain selective channels, are tested and evaluated using a deep-learning model of 1D-CNN, Bi-LSTM, and attention. The results achieved up to 96.87% accuracy, 96.98% precision, and 96.85% sensitivity, outperforming the other latest systems that have a larger number of EEG channels. Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications)
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