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Electronics
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Brain Computer Interface: Theory, Method, and Application
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Brain Computer Interface: Theory, Method, and Application

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (16 March 2024) | Viewed by 4250

Special Issue Editors

Dr. Ziyu Jia

E-Mail Website
Guest Editor
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Interests: brain computer interface and deep learning
Dr. Idris Elbakri

E-Mail Website
Guest Editor
Department of Radiology, University of Manitoba, Winnipeg, MB R3T 2M6, Canada
Interests: brain computer interface

Special Issue Information

Dear Colleagues,

The brain–computer interface (BCI) aims to establish real-time, bidirectional connections between the brain and artificial devices. It is multidisciplinary, encompassing fields such as computer science and neuroscience. Advanced BCI provides new insights into brain functions, leading to improved treatments related to sleep, as well as for neural diseases such as paralysis, epilepsy, and Parkinson's disease. BCI has broad applications and continues to drive new discoveries in the field of medicine, education, entertainment, and marketing.

This Special Issue focuses on the field of the brain–computer interface and its technologies as well as applications. It includes a mix of original research papers, review articles on cutting-edge progress, and perspectives on challenges and future directions. The focus is interdisciplinary research combining computer science, neuroscience, medical and engineering expertise.

Topics of interest include, but are not limited to:

  • Neural signals encoding and decoding;
  • Neural signal processing algorithm;
  • Robotics based on BCI;
  • Clinical BCI;
  • Bio-sensors and bio-actuators;
  • BCI for education;
  • BCI for entertainment;
  • Commercial BCI.

Dr. Ziyu Jia
Dr. Idris Elbakri
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

  • neural signals encoding and decoding
  • neural signal processing algorithm
  • robotics based on BCI
  • clinical BCI
  • bio-sensors and bio-actuators
  • BCI for education
  • BCI for entertainment
  • commercial BCI

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

15 pages, 3914 KiB  
Article
Trial of Brain–Computer Interface for Continuous Motion Using Electroencephalography and Electromyography
by Norihiko Saga, Yukina Okawa, Takuma Saga, Toshiyuki Satoh and Naoki Saito
Electronics 2024, 13(14), 2770; https://doi.org/10.3390/electronics13142770 - 15 Jul 2024
Cited by 2 | Viewed by 1756
Abstract
Most BCI systems used in neurorehabilitation detect EEG features indicating motor intent based on machine learning, focusing on repetitive movements, such as limb flexion and extension. These machine learning methods require large datasets and are time consuming, making them unsuitable for same-day rehabilitation [...] Read more.
Most BCI systems used in neurorehabilitation detect EEG features indicating motor intent based on machine learning, focusing on repetitive movements, such as limb flexion and extension. These machine learning methods require large datasets and are time consuming, making them unsuitable for same-day rehabilitation training following EEG measurements. Therefore, we propose a BMI system based on fuzzy inference that bypasses the need for specific EEG features, introducing an algorithm that allows patients to progress from measurement to training within a few hours. Additionally, we explored the integration of electromyography (EMG) with conventional EEG-based motor intention estimation to capture continuous movements, which is essential for advanced motor function training, such as skill improvement. In this study, we developed an algorithm that detects the initial movement via EEG and switches to EMG for subsequent movements. This approach ensures real-time responsiveness and effective handling of continuous movements. Herein, we report the results of this study. Full article
(This article belongs to the Special Issue Brain Computer Interface: Theory, Method, and Application)
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24 pages, 1130 KiB  
Article
Enhancing Motor Imagery Electroencephalography Classification with a Correlation-Optimized Weighted Stacking Ensemble Model
by Hossein Ahmadi and Luca Mesin
Electronics 2024, 13(6), 1033; https://doi.org/10.3390/electronics13061033 - 10 Mar 2024
Cited by 4 | Viewed by 1532
Abstract
In the evolving field of Brain–Computer Interfaces (BCIs), accurately classifying Electroencephalography (EEG) signals for Motor Imagery (MI) tasks is challenging. We introduce the Correlation-Optimized Weighted Stacking Ensemble (COWSE) model, an innovative ensemble learning framework designed to improve MI EEG signal classification. The COWSE [...] Read more.
In the evolving field of Brain–Computer Interfaces (BCIs), accurately classifying Electroencephalography (EEG) signals for Motor Imagery (MI) tasks is challenging. We introduce the Correlation-Optimized Weighted Stacking Ensemble (COWSE) model, an innovative ensemble learning framework designed to improve MI EEG signal classification. The COWSE model integrates sixteen machine learning classifiers through a weighted stacking approach, optimizing performance by balancing the strengths and weaknesses of each classifier based on error correlation analysis and performance metrics evaluation across benchmark datasets. The COWSE model’s development involves selecting base classifiers, dynamically assigning weights according to performance, and employing a meta-classifier trained on these weighted predictions. Testing on the BNCI2014-002 dataset, the COWSE model achieved classification accuracy exceeding 98.16%, marking a significant advancement in MI EEG classification. This study highlights the potential of integrating multiple machine learning classifiers to address the complex challenges of EEG signal classification. By achieving new benchmarks and showcasing enhanced classification capabilities, the COWSE model contributes significantly to BCI research, encouraging further exploration into advanced ensemble learning strategies. Full article
(This article belongs to the Special Issue Brain Computer Interface: Theory, Method, and Application)
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