Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

EEG Signal Processing Techniques and Applications—2nd Edition

Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: 30 August 2024 | Viewed by 13137

Share This Special Issue

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

Dr. Hua-Liang Wei

E-Mail Website
Guest Editor

Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield S10 2TN, UK
Interests: bioscience signal processing; data modeling
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, modeling, 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
Dr. Yuzhu Guo
Dr. Hua-Liang Wei
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 (10 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

20 pages, 2552 KiB

Open AccessArticle

Identifying the Effect of Cognitive Motivation with the Method Based on Temporal Association Rule Mining Concept

by Tustanah Phukhachee, Suthathip Maneewongvatana, Chayapol Chaiyanan, Keiji Iramina and Boonserm Kaewkamnerdpong

Sensors 2024, 24(9), 2857; https://doi.org/10.3390/s24092857 (registering DOI) - 30 Apr 2024

Abstract

Being motivated has positive influences on task performance. However, motivation could result from various motives that affect different parts of the brain. Analyzing the motivation effect from all affected areas requires a high number of EEG electrodes, resulting in high cost, inflexibility, and burden to users. In various real-world applications, only the motivation effect is required for performance evaluation regardless of the motive. Analyzing the relationships between the motivation-affected brain areas associated with the task’s performance could limit the required electrodes. This study introduced a method to identify the cognitive motivation effect with a reduced number of EEG electrodes. The temporal association rule mining (TARM) concept was used to analyze the relationships between attention and memorization brain areas under the effect of motivation from the cognitive motivation task. For accuracy improvement, the artificial bee colony (ABC) algorithm was applied with the central limit theorem (CLT) concept to optimize the TARM parameters. From the results, our method can identify the motivation effect with only FCz and P3 electrodes, with 74.5% classification accuracy on average with individual tests. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—2nd Edition)

► Show Figures

Figure 1

21 pages, 840 KiB

Open AccessArticle

Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals

by Jusciaane Chacon Vieira, Luiz Affonso Guedes, Mailson Ribeiro Santos and Ignacio Sanchez-Gendriz

Sensors 2023, 23(24), 9871; https://doi.org/10.3390/s23249871 - 16 Dec 2023

Cited by 1 | Viewed by 911

Abstract

Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their quality of life. Epileptic seizures, a transient occurrence, are characterized by a spectrum of manifestations, including alterations in motor function and consciousness. These events impose restrictions on the daily lives of those affected, frequently resulting in social isolation and psychological distress. In response, numerous efforts have been directed towards the detection and prevention of epileptic seizures through EEG signal analysis, employing machine learning and deep learning methodologies. This study presents a methodology that reduces the number of features and channels required by simpler classifiers, leveraging Explainable Artificial Intelligence (XAI) for the detection of epileptic seizures. The proposed approach achieves performance metrics exceeding 95% in accuracy, precision, recall, and F1-score by utilizing merely six features and five channels in a temporal domain analysis, with a time window of 1 s. The model demonstrates robust generalization across the patient cohort included in the database, suggesting that feature reduction in simpler models—without resorting to deep learning—is adequate for seizure detection. The research underscores the potential for substantial reductions in the number of attributes and channels, advocating for the training of models with strategically selected electrodes, and thereby supporting the development of effective mobile applications for epileptic seizure detection. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—2nd Edition)

► Show Figures

Figure 1

20 pages, 2917 KiB

Open AccessArticle

Illuminating the Neural Landscape of Pilot Mental States: A Convolutional Neural Network Approach with Shapley Additive Explanations Interpretability

by Ibrahim Alreshidi, Desmond Bisandu and Irene Moulitsas

Sensors 2023, 23(22), 9052; https://doi.org/10.3390/s23229052 - 08 Nov 2023

Cited by 1 | Viewed by 845

Abstract

Predicting pilots’ mental states is a critical challenge in aviation safety and performance, with electroencephalogram data offering a promising avenue for detection. However, the interpretability of machine learning and deep learning models, which are often used for such tasks, remains a significant issue. This study aims to address these challenges by developing an interpretable model to detect four mental states—channelised attention, diverted attention, startle/surprise, and normal state—in pilots using EEG data. The methodology involves training a convolutional neural network on power spectral density features of EEG data from 17 pilots. The model’s interpretability is enhanced via the use of SHapley Additive exPlanations values, which identify the top 10 most influential features for each mental state. The results demonstrate high performance in all metrics, with an average accuracy of 96%, a precision of 96%, a recall of 94%, and an F1 score of 95%. An examination of the effects of mental states on EEG frequency bands further elucidates the neural mechanisms underlying these states. The innovative nature of this study lies in its combination of high-performance model development, improved interpretability, and in-depth analysis of the neural correlates of mental states. This approach not only addresses the critical need for effective and interpretable mental state detection in aviation but also contributes to our understanding of the neural underpinnings of these states. This study thus represents a significant advancement in the field of EEG-based mental state detection. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—2nd Edition)

► Show Figures

Figure 1

16 pages, 519 KiB

Open AccessFeature PaperArticle

Domain-Specific Processing Stage for Estimating Single-Trail Evoked Potential Improves CNN Performance in Detecting Error Potential

by Andrea Farabbi and Luca Mainardi

Sensors 2023, 23(22), 9049; https://doi.org/10.3390/s23229049 - 08 Nov 2023

Viewed by 654

Abstract

We present a novel architecture designed to enhance the detection of Error Potential (ErrP) signals during ErrP stimulation tasks. In the context of predicting ErrP presence, conventional Convolutional Neural Networks (CNNs) typically accept a raw EEG signal as input, encompassing both the information associated with the evoked potential and the background activity, which can potentially diminish predictive accuracy. Our approach involves advanced Single-Trial (ST) ErrP enhancement techniques for processing raw EEG signals in the initial stage, followed by CNNs for discerning between ErrP and NonErrP segments in the second stage. We tested different combinations of methods and CNNs. As far as ST ErrP estimation is concerned, we examined various methods encompassing subspace regularization techniques, Continuous Wavelet Transform, and ARX models. For the classification stage, we evaluated the performance of EEGNet, CNN, and a Siamese Neural Network. A comparative analysis against the method of directly applying CNNs to raw EEG signals revealed the advantages of our architecture. Leveraging subspace regularization yielded the best improvement in classification metrics, at up to 14% in balanced accuracy and 13.4% in F1-score. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—2nd Edition)

► Show Figures

Figure 1

21 pages, 2109 KiB

Open AccessArticle

Graph Analysis of TMS–EEG Connectivity Reveals Hemispheric Differences following Occipital Stimulation

by Ilaria Siviero, Davide Bonfanti, Gloria Menegaz, Silvia Savazzi, Chiara Mazzi and Silvia Francesca Storti

Sensors 2023, 23(21), 8833; https://doi.org/10.3390/s23218833 - 30 Oct 2023

Cited by 2 | Viewed by 1333

Abstract

(1) Background: Transcranial magnetic stimulation combined with electroencephalography (TMS–EEG) provides a unique opportunity to investigate brain connectivity. However, possible hemispheric asymmetries in signal propagation dynamics following occipital TMS have not been investigated. (2) Methods: Eighteen healthy participants underwent occipital single-pulse TMS at two different EEG sites, corresponding to early visual areas. We used a state-of-the-art Bayesian estimation approach to accurately estimate TMS-evoked potentials (TEPs) from EEG data, which has not been previously used in this context. To capture the rapid dynamics of information flow patterns, we implemented a self-tuning optimized Kalman (STOK) filter in conjunction with the information partial directed coherence (iPDC) measure, enabling us to derive time-varying connectivity matrices. Subsequently, graph analysis was conducted to assess key network properties, providing insight into the overall network organization of the brain network. (3) Results: Our findings revealed distinct lateralized effects on effective brain connectivity and graph networks after TMS stimulation, with left stimulation facilitating enhanced communication between contralateral frontal regions and right stimulation promoting increased intra-hemispheric ipsilateral connectivity, as evidenced by statistical test (p < 0.001). (4) Conclusions: The identified hemispheric differences in terms of connectivity provide novel insights into brain networks involved in visual information processing, revealing the hemispheric specificity of neural responses to occipital stimulation. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—2nd Edition)

► Show Figures

Figure 1

18 pages, 2099 KiB

Open AccessArticle

Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning

by Yanting Xu, Hongyang Zhong, Shangyan Ying, Wei Liu, Guibin Chen, Xiaodong Luo and Gang Li

Sensors 2023, 23(20), 8639; https://doi.org/10.3390/s23208639 - 23 Oct 2023

Cited by 2 | Viewed by 1245

Abstract

Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person’s psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and subjective scales, lacking objective, accurate, practical, and automatic diagnosis technologies. Recently, electroencephalogram (EEG) signals have been widely applied for DD diagnosis, but mainly with high-density EEG, which can severely limit the efficiency of the EEG data acquisition and reduce the practicability of diagnostic techniques. The current study attempts to achieve accurate and practical DD diagnoses based on combining frontal six-channel electroencephalogram (EEG) signals and deep learning models. To this end, 10 min clinical resting-state EEG signals were collected from 41 DD patients and 34 healthy controls (HCs). Two deep learning models, multi-resolution convolutional neural network (MRCNN) combined with long short-term memory (LSTM) (named MRCNN-LSTM) and MRCNN combined with residual squeeze and excitation (RSE) (named MRCNN-RSE), were proposed for DD recognition. The results of this study showed that the higher EEG frequency band obtained the better classification performance for DD diagnosis. The MRCNN-RSE model achieved the highest classification accuracy of 98.48 ± 0.22% with 8–30 Hz EEG signals. These findings indicated that the proposed analytical framework can provide an accurate and practical strategy for DD diagnosis, as well as essential theoretical and technical support for the treatment and efficacy evaluation of DD. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—2nd Edition)

► Show Figures

Figure 1

16 pages, 4776 KiB

Open AccessArticle

Characterisation of Cognitive Load Using Machine Learning Classifiers of Electroencephalogram Data

by Qi Wang, Daniel Smythe, Jun Cao, Zhilin Hu, Karl J. Proctor, Andrew P. Owens and Yifan Zhao

Sensors 2023, 23(20), 8528; https://doi.org/10.3390/s23208528 - 17 Oct 2023

Viewed by 1270

Abstract

A high cognitive load can overload a person, potentially resulting in catastrophic accidents. It is therefore important to ensure the level of cognitive load associated with safety-critical tasks (such as driving a vehicle) remains manageable for drivers, enabling them to respond appropriately to changes in the driving environment. Although electroencephalography (EEG) has attracted significant interest in cognitive load research, few studies have used EEG to investigate cognitive load in the context of driving. This paper presents a feasibility study on the simulation of various levels of cognitive load through designing and implementing four driving tasks. We employ machine learning-based classification techniques using EEG recordings to differentiate driving conditions. An EEG dataset containing these four driving tasks from a group of 20 participants was collected to investigate whether EEG can be used as an indicator of changes in cognitive load. The collected dataset was used to train four Deep Neural Networks and four Support Vector Machine classification models. The results showed that the best model achieved a classification accuracy of 90.37%, utilising statistical features from multiple frequency bands in 24 EEG channels. Furthermore, the Gamma and Beta bands achieved higher classification accuracy than the Alpha and Theta bands during the analysis. The outcomes of this study have the potential to enhance the Human–Machine Interface of vehicles, contributing to improved safety. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—2nd Edition)

► Show Figures

Figure 1

13 pages, 2045 KiB

Open AccessArticle

Electrocortical Dynamics of Usual Walking and the Planning to Step over Obstacles in Parkinson’s Disease

by Rodrigo Vitório, Ellen Lirani-Silva, Diego Orcioli-Silva, Victor Spiandor Beretta, Anderson Souza Oliveira and Lilian Teresa Bucken Gobbi

Sensors 2023, 23(10), 4866; https://doi.org/10.3390/s23104866 - 18 May 2023

Viewed by 1893

Abstract

The neural correlates of locomotion impairments observed in people with Parkinson’s disease (PD) are not fully understood. We investigated whether people with PD present distinct brain electrocortical activity during usual walking and the approach phase of obstacle avoidance when compared to healthy individuals. Fifteen people with PD and fourteen older adults walked overground in two conditions: usual walking and obstacle crossing. Scalp electroencephalography (EEG) was recorded using a mobile 64-channel EEG system. Independent components were clustered using a k-means clustering algorithm. Outcome measures included absolute power in several frequency bands and alpha/beta ratio. During the usual walk, people with PD presented a greater alpha/beta ratio in the left sensorimotor cortex than healthy individuals. While approaching obstacles, both groups reduced alpha and beta power in the premotor and right sensorimotor cortices (balance demand) and increased gamma power in the primary visual cortex (visual demand). Only people with PD reduced alpha power and alpha/beta ratio in the left sensorimotor cortex when approaching obstacles. These findings suggest that PD affects the cortical control of usual walking, leading to a greater proportion of low-frequency (alpha) neuronal firing in the sensorimotor cortex. Moreover, the planning for obstacle avoidance changes the electrocortical dynamics associated with increased balance and visual demands. People with PD rely on increased sensorimotor integration to modulate locomotion. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—2nd Edition)

► Show Figures

Figure 1

29 pages, 2248 KiB

Open AccessArticle

Method for Automatic Estimation of Instantaneous Frequency and Group Delay in Time–Frequency Distributions with Application in EEG Seizure Signals Analysis

by Vedran Jurdana, Miroslav Vrankic, Nikola Lopac and Guruprasad Madhale Jadav

Sensors 2023, 23(10), 4680; https://doi.org/10.3390/s23104680 - 11 May 2023

Cited by 1 | Viewed by 1457

Abstract

Instantaneous frequency (IF) is commonly used in the analysis of electroencephalogram (EEG) signals to detect oscillatory-type seizures. However, IF cannot be used to analyze seizures that appear as spikes. In this paper, we present a novel method for the automatic estimation of IF and group delay (GD) in order to detect seizures with both spike and oscillatory characteristics. Unlike previous methods that use IF alone, the proposed method utilizes information obtained from localized Rényi entropies (LREs) to generate a binary map that automatically identifies regions requiring a different estimation strategy. The method combines IF estimation algorithms for multicomponent signals with time and frequency support information to improve signal ridge estimation in the time–frequency distribution (TFD). Our experimental results indicate the superiority of the proposed combined IF and GD estimation approach over the IF estimation alone, without requiring any prior knowledge about the input signal. The LRE-based mean squared error and mean absolute error metrics showed improvements of up to 95.70% and 86.79%, respectively, for synthetic signals and up to 46.45% and 36.61% for real-life EEG seizure signals. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—2nd Edition)

► Show Figures

Figure 1

20 pages, 1200 KiB

Open AccessArticle

Motor Imagery Multi-Tasks Classification for BCIs Using the NVIDIA Jetson TX2 Board and the EEGNet Network

by Tat’y Mwata-Velu, Edson Niyonsaba-Sebigunda, Juan Gabriel Avina-Cervantes, Jose Ruiz-Pinales, Narcisse Velu-A-Gulenga and Adán Antonio Alonso-Ramírez

Sensors 2023, 23(8), 4164; https://doi.org/10.3390/s23084164 - 21 Apr 2023

Cited by 1 | Viewed by 2111

Abstract

Nowadays, Brain–Computer Interfaces (BCIs) still captivate large interest because of multiple advantages offered in numerous domains, explicitly assisting people with motor disabilities in communicating with the surrounding environment. However, challenges of portability, instantaneous processing time, and accurate data processing remain for numerous BCI system setups. This work implements an embedded multi-tasks classifier based on motor imagery using the EEGNet network integrated into the NVIDIA Jetson TX2 card. Therefore, two strategies are developed to select the most discriminant channels. The former uses the accuracy based-classifier criterion, while the latter evaluates electrode mutual information to form discriminant channel subsets. Next, the EEGNet network is implemented to classify discriminant channel signals. Additionally, a cyclic learning algorithm is implemented at the software level to accelerate the model learning convergence and fully profit from the NJT2 hardware resources. Finally, motor imagery Electroencephalogram (EEG) signals provided by HaLT’s public benchmark were used, in addition to the k-fold cross-validation method. Average accuracies of 83.7% and 81.3% were achieved by classifying EEG signals per subject and motor imagery task, respectively. Each task was processed with an average latency of 48.7 ms. This framework offers an alternative for online EEG-BCI systems’ requirements, dealing with short processing times and reliable classification accuracy. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—2nd Edition)

► Show Figures

Journal Menu

Journal Browser

EEG Signal Processing Techniques and Applications—2nd Edition

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (10 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI