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Electronics
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EEG Analysis and Brain–Computer Interface (BCI) Technology
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EEG Analysis and Brain–Computer Interface (BCI) Technology

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 1755

Special Issue Editors

Prof. Dr. Zhao Lv

E-Mail Website
Guest Editor
School of Computer Science and Technology, Anhui University, Hefei 230601, China
Interests: EGG
Dr. Yongjun Zheng

E-Mail Website
Guest Editor
School of Physics, Engineering and Computer Science, College Lane Campus, University of Hertfordshire, Hertfordshire AL10 9AB , UK
Interests: human robotic interaction; AR/VR; data visualization
Dr. Chao Zhang

E-Mail Website
Guest Editor
School of Computer Science and Technology, Anhui University, Hefei 230601, China
Interests: multimodal biomedical signal processing; iPPG

Special Issue Information

Dear Colleagues,

Brain–computer interface (BCI) plays an important role in intelligent interaction systems, which refers to the direct communication link between the brain and external types of equipment to realize information exchange. As one of the most important research fields in intelligence science, BCI has acquired great improvements and potential applications in various fields such as rehabilitation, affective computing, neuroscience, robotics, and gaming.

The aim of this Special Issue is to present advanced research in the field of BCI, and to highlight major open questions to address the outstanding challenges in EEG signal analysis as well as BCI technology. Papers that address innovative applications and algorithms related to EEG analysis and BCI technology are welcome. This Special Issue welcomes submissions of original research and review articles, along with data reports, hypothesis and theory, methods, mini reviews, and study protocol. Topics of interest include, but are not limited to, the following:

  • BCI paradigm including MI, SSVEP, P300, etc;
  • EEG signals analysis;
  • EEG-based affective computing;
  • EEG-based auditory attention decoding;
  • EEG-based neuroimaging and neural mechanism;
  • Other brain–computer interface technologies.

Prof. Dr. Zhao Lv
Dr. Yongjun Zheng
Dr. Chao Zhang
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. Electronics 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 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

  • EEG
  • brain–computer interface
  • signal analysis
  • BCI applications

Published Papers (1 paper)

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Research

14 pages, 1513 KiB  
Article
A Fine-Grained Approach for EEG-Based Emotion Recognition Using Clustering and Hybrid Deep Neural Networks
by Liumei Zhang, Bowen Xia, Yichuan Wang, Wei Zhang and Yu Han
Electronics 2023, 12(23), 4717; https://doi.org/10.3390/electronics12234717 - 21 Nov 2023
Cited by 1 | Viewed by 1309
Abstract
Emotion recognition, as an important part of human-computer interaction, is of great research significance and has already played a role in the fields of artificial intelligence, healthcare, and distance education. In recent times, there has been a growing trend in using deep learning [...] Read more.
Emotion recognition, as an important part of human-computer interaction, is of great research significance and has already played a role in the fields of artificial intelligence, healthcare, and distance education. In recent times, there has been a growing trend in using deep learning techniques for EEG emotion recognition. These methods have shown higher accuracy in recognizing emotions when compared with traditional machine learning methods. However, most of the current EEG emotion recognition performs multi-category single-label prediction, and is a binary classification problem based on the dimensional model. This simplifies the fact that human emotions are mixed and complex. In order to adapt to real-world applications, fine-grained emotion recognition is necessary. We propose a new method for building emotion classification labels using linguistic resource and density-based spatial clustering of applications with noise (DBSCAN). Additionally, we integrate the frequency domain and spatial features of emotional EEG signals and feed these features into a serial network that combines a convolutional neural network (CNN) and a long short-term memory (LSTM) recurrent neural network (RNN) for EEG emotion feature learning and classification. We conduct emotion classification experiments on the DEAP dataset, and the results show that our method has an average emotion classification accuracy of 92.98% per subject, validating the effectiveness of the improvements we have made to our emotion classification method. Our method for emotion classification holds potential for future use in the domain of affective computing, such as mental health care, education, social media, and so on. By constructing an automatic emotion analysis system using our method to enable the machine to understand the emotional implications conveyed by the subjects’ EEG signals, it can provide healthcare professionals with valuable information for effective treatment outcomes. Full article
(This article belongs to the Special Issue EEG Analysis and Brain–Computer Interface (BCI) Technology)
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