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Symmetry
Special Issues
Symmetry Applied in EEG and Brain Research: Theory and Applications
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Symmetry Applied in EEG and Brain Research: Theory and Applications

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Life Sciences".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 2104

Special Issue Editor

Dr. Xiaojun Yu

E-Mail Website
Guest Editor
School of Automation, Northwestern Polytechnial University, Xi’an 710072, China
Interests: BCI; EEG; brain research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electroencephalography (EEG) is a conventional screening practice for anatomizing the cognitive and pathological states of brain. Theoretical research corroborates that families of cognitive and diseased cortical activities exhibit unique spatial–temporal patterns, which could be characterized by a particular symmetry transformation. The utility of such symmetric variations could be harnessed for the segregation of EEG signals in clinical application, i.e., epilepsy, schizophrenia, alcoholism, depression, autism, sleep stage analysis, dementia, etc. and brain–computer interface systems. This Special Issue aims to discover, analyze, and apply the concept of symmetry in the EEG domain. Original research on novel ideas, techniques, technical knowledge, fusion with other diagnoses, and relevant applications that may lead to substantial advancements of symmetry in EEG data analytics is encouraged to be submitted.

Dr. Xiaojun Yu
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 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. Symmetry is an international peer-reviewed open access monthly 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 2400 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

  • symmetry
  • electroencephalography
  • brain–computer interface
  • symmetry methods in EEG
  • EEG rhythms
  • disease diagnosis
  • deep learning and machine learning in EEG
  • Artificial Intelligence in EEG
  • optimization problems and applications
  • nonlinear operator theory in EEG and applications
  • symmetry exploitations
  • symmetric matrices

Published Papers (1 paper)

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Research

23 pages, 1173 KiB  
Article
Exploiting Asymmetric EEG Signals with EFD in Deep Learning Domain for Robust BCI
by Binwen Huang, Haiqin Xu, Miao Yuan, Muhammad Zulkifal Aziz and Xiaojun Yu
Symmetry 2022, 14(12), 2677; https://doi.org/10.3390/sym14122677 - 18 Dec 2022
Cited by 4 | Viewed by 1679
Abstract
Motor imagery (MI) is a domineering paradigm in brain–computer interface (BCI) composition, personifying the imaginary limb motion into digital commandments for neural rehabilitation and automation exertions, while many researchers fathomed myriad solutions for asymmetric MI EEG signals classification, the existence of a robust, [...] Read more.
Motor imagery (MI) is a domineering paradigm in brain–computer interface (BCI) composition, personifying the imaginary limb motion into digital commandments for neural rehabilitation and automation exertions, while many researchers fathomed myriad solutions for asymmetric MI EEG signals classification, the existence of a robust, non-complex, and subject-invariant system is far-reaching. Thereupon, we put forward an MI EEG segregation pipeline in the deep-learning domain in an effort to curtail the existing limitations. Our method amalgamates multiscale principal component analysis (MSPCA), a novel empirical Fourier decomposition (EFD) signal resolution method with Hilbert transform (HT), followed by four pre-trained convolutional neural networks for automatic feature estimation and segregation. The conceived architecture is validated upon three binary class datasets: IVa, IVb from BCI Competition III, GigaDB from the GigaScience repository, and one tertiary class dataset V from BCI competition III. The average 10-fold outcomes capitulate 98.63%, 96.33%, and 89.96%, the highest classification accuracy for the aforesaid datasets accordingly using the AlexNet CNN model in a subject-dependent context, while in subject-independent cases, the highest success score was 97.69%, outperforming the contemporary studies by a fair margin. Further experiments such as the resolution scale of EFD, comparison with other signal decomposition (SD) methods, deep feature extraction, and classification with machine learning methods also accredits the supremacy of our proposed EEG signal processing pipeline. The overall findings imply that pre-trained models are reliable in identifying EEG signals due to their capacity to maintain the time-frequency structure of EEG signals, non-complex architecture, and their potential for robust classification performance. Full article
(This article belongs to the Special Issue Symmetry Applied in EEG and Brain Research: Theory and Applications)
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