Special Issue "Machine Learning for EEG Signal Processing"

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 15 September 2020.

Special Issue Editor

Assoc. Prof. Dr. Larbi Boubchir
Website
Guest Editor
LIASD research Lab. – University of Paris 8, 2 Rue de la Liberté, 93526 Saint-Denis, France
Interests: biomedical signal processing; EEG; image processing; machine learning; brain–computer interface; biometrics
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Special Issue Information

Dear colleagues,

The 1st International Workshop on Machine Learning for EEG Signal Processing (MLESP 2018) will be held in Madrid, Spain, 3–6 December, 2018. The aim of this workshop is to present and discuss the recent advances in machine learning for EEG signal analysis and processing. For more information about the workshop, please use this link:

https://mlesp2018.sciencesconf.org/

Selected papers which presented at the workshop are invited to submit their extended versions to this Special Issue of the journal Computers after the conference. Submitted papers should be extended to the size of regular research or review articles, with at least 40% extension of new results. All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in open access format in Computers and collected together in this Special Issue website. There are no page limitations for this journal.

We are also inviting original research work covering novel theories, innovative methods, and meaningful applications that can potentially lead to significant advances in EEG data analytics.

The main topics include, but are not limited to:

  • EEG signal processing and analysis
  • Time-frequency EEG signal analysis
  • Signal processing for EEG Data
  • EEG feature extraction and selection
  • Machine learning for EEG signal processing
  • EEG classification and clustering
  • EEG abnormalities detection (e.g. Epileptic seizure, Alzheimer's disease, etc.)
  • Machine learning in EEG Big Data
  • Deep Learning for EEG Big Data
  • Neural Rehabilitation Engineering
  • Brain-Computer Interface
  • Neurofeedback
  • Biometrics with EEG data
  • Related applications

Dr. Larbi Boubchir
Guest Editor

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 papers will be 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. Computers is an international peer-reviewed open access quarterly 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 1000 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)
  • Biomedical signal processing
  • Machine learning
  • Biomedical engineering

Published Papers (3 papers)

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Research

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Open AccessArticle
Evaluation of Features in Detection of Dislike Responses to Audio–Visual Stimuli from EEG Signals
Computers 2020, 9(2), 33; https://doi.org/10.3390/computers9020033 - 20 Apr 2020
Abstract
There is a strong correlation between the like/dislike responses to audio–visual stimuli and the emotional arousal and valence reactions of a person. In the present work, our attention is focused on the automated detection of dislike responses based on EEG activity when music [...] Read more.
There is a strong correlation between the like/dislike responses to audio–visual stimuli and the emotional arousal and valence reactions of a person. In the present work, our attention is focused on the automated detection of dislike responses based on EEG activity when music videos are used as audio–visual stimuli. Specifically, we investigate the discriminative capacity of the Logarithmic Energy (LogE), Linear Frequency Cepstral Coefficients (LFCC), Power Spectral Density (PSD) and Discrete Wavelet Transform (DWT)-based EEG features, computed with and without segmentation of the EEG signal, on the dislike detection task. We carried out a comparative evaluation with eighteen modifications of the above-mentioned EEG features that cover different frequency bands and use different energy decomposition methods and spectral resolutions. For that purpose, we made use of Naïve Bayes classifier (NB), Classification and regression trees (CART), k-Nearest Neighbors (kNN) classifier, and support vector machines (SVM) classifier with a radial basis function (RBF) kernel trained with the Sequential Minimal Optimization (SMO) method. The experimental evaluation was performed on the well-known and widely used DEAP dataset. A classification accuracy of up to 98.6% was observed for the best performing combination of pre-processing, EEG features and classifier. These results support that the automated detection of like/dislike reactions based on EEG activity is feasible in a personalized setup. This opens opportunities for the incorporation of such functionality in entertainment, healthcare and security applications. Full article
(This article belongs to the Special Issue Machine Learning for EEG Signal Processing)
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Open AccessArticle
Statistical-Hypothesis-Aided Tests for Epilepsy Classification
Computers 2019, 8(4), 84; https://doi.org/10.3390/computers8040084 - 20 Nov 2019
Abstract
In this paper, an efficient, accurate, and nonparametric epilepsy detection and classification approach based on electroencephalogram (EEG) signals is proposed. The proposed approach mainly depends on a feature extraction process that is conducted using a set of statistical tests. Among the many existing [...] Read more.
In this paper, an efficient, accurate, and nonparametric epilepsy detection and classification approach based on electroencephalogram (EEG) signals is proposed. The proposed approach mainly depends on a feature extraction process that is conducted using a set of statistical tests. Among the many existing tests, those fit with processed data and for the purpose of the proposed approach were used. From each test, various output scalars were extracted and used as features in the proposed detection and classification task. Experiments that were conducted on the basis of a Bonn University dataset showed that the proposed approach had very accurate results ( 98.4 % ) in the detection task and outperformed state-of-the-art methods in a similar task on the same dataset. The proposed approach also had accurate results ( 94.0 % ) in the classification task, but it did not outperform state-of-the-art methods in a similar task on the same dataset. However, the proposed approach had less time complexity in comparison with those methods that achieved better results. Full article
(This article belongs to the Special Issue Machine Learning for EEG Signal Processing)
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Review

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Open AccessReview
Neural Net-Based Approach to EEG Signal Acquisition and Classification in BCI Applications
Computers 2019, 8(4), 87; https://doi.org/10.3390/computers8040087 - 04 Dec 2019
Abstract
The following contribution describes a neural net-based, noninvasive methodology for electroencephalographic (EEG) signal classification. The application concerns a brain–computer interface (BCI) allowing disabled people to interact with their environment using only brain activity. It consists of classifying user’s thoughts in order to translate [...] Read more.
The following contribution describes a neural net-based, noninvasive methodology for electroencephalographic (EEG) signal classification. The application concerns a brain–computer interface (BCI) allowing disabled people to interact with their environment using only brain activity. It consists of classifying user’s thoughts in order to translate them into commands, such as controlling wheelchairs, cursor movement, or spelling. The proposed method follows a functional model, as is the case for any BCI, and can be achieved through three main phases: data acquisition and preprocessing, feature extraction, and classification of brains activities. For this purpose, we propose an interpretation model implementing a quantization method using both fast Fourier transform with root mean square error for feature extraction and a self-organizing-map-based neural network to generate classifiers, allowing better interpretation of brain activities. In order to show the effectiveness of the proposed methodology, an experimental study was conducted by exploiting five mental activities acquired by a G.tec BCI system containing 16 simultaneously sampled bio-signal channels with 24 bits, with experiments performed on 10 randomly chosen subjects. Full article
(This article belongs to the Special Issue Machine Learning for EEG Signal Processing)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Evaluation of Features in EEG-based Emotion Recognition
Authors: Firgan Feradov, Iosif Mporas, Todor Ganchev
Abstract: This paper presents an evaluation of some recent and traditional methods for EEG feature extraction with respect to their applicability to the automated emotion recognition task. We evaluate the recognition accuracy obtained for signal features extracted with different methods and the effect of using different numbers of filters in the calculation of the cepstral coefficients. All evaluations were performed making use the Weka machine learning toolbox.
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