Special Issue "The Challenges in Brain-Computer Interface (BCI) - Toward Practical BCI"

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

Deadline for manuscript submissions: 29 February 2020.

Special Issue Editors

Dr. Han-Jeong Hwang
E-Mail Website
Guest Editor
Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology (KIT), Gyeongbuk, Korea
Interests: brain–computer Interface (BCI); neuromodulation; myoelectric control; deep learning; machine learning
Dr. Chang-Hee Han
E-Mail Website
Guest Editor
Department of Software Engineering and Theoretical Computer Science, Technical University of Berlin, Berlin, Germany
Interests: brain-computer interface (BCI), machine learning, EEG/NIRS hybrid brain signal analysis

Special Issue Information

Dear Colleagues,

For the last several decades, the brain–computer interface (BCI) has been intensively studied to establish a novel method of communication using brain activity for those who are paralyzed but have intact brain functions. However, the performance and reliability of BCI technologies are still limited due to various challenging issues, such as the nonstationarity nature of brain activity, session-to-session transfer, physiological artifacts contained in brain activity, and so on. Consequently, clinically available BCI systems have rarely been introduced to date.

This Special Issue aims to share the current state-of-the-art trends and future directions in the BCI field, thereby encouraging the development of practical solutions to tackle the aforementioned challenging issues. We invite researchers to submit original research articles, clinical studies, and review/survey articles that contribute to the advance of BCI technologies based on non-invasive neuroimaging modalities, i.e., electroencephalography (EEG) and near-infrared spectroscopy (NIRS). This Special Issue will focus in the challenges in practical BCI, including but not limited to:

  • Enhanced BCI Performance: Development of new devices, algorithms, and paradigms;
  • Reliable BCI: Evaluation of test-retest reliability and session-to-session transfer based on multiday datasets;
  • Ambulatory BCI: Development of portable, easy-to-use, and wireless EEG/NIRS recording systems and their related methodologies;
  • Neuromodulation-based BCI: Use of electrical and magnetic brain stimulation to improve the performance and reliability of BCI systems;
  • Practical BCI: Development of BCI applications, e.g., rehabilitation, entertainment, drowsiness detection, emotion decoding;
  • Development of new artifact rejection algorithms for EOG, EMG, ECG, etc.;
  • Releasing publicly available BCI datasets;
  • Review/survey articles.

Dr. Han-Jeong Hwang
Dr. Chang-Hee Han
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 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. Electronics 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 1400 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.

Published Papers (3 papers)

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Research

Open AccessArticle
Decoding EEG in Motor Imagery Tasks with Graph Semi-Supervised Broad Learning
Electronics 2019, 8(11), 1273; https://doi.org/10.3390/electronics8111273 - 01 Nov 2019
Abstract
In recent years, the accurate and real-time classification of electroencephalogram (EEG) signals has drawn increasing attention in the application of brain-computer interface technology (BCI). Supervised methods used to classify EEG signals have gotten satisfactory results. However, unlabeled samples are more frequent than labeled [...] Read more.
In recent years, the accurate and real-time classification of electroencephalogram (EEG) signals has drawn increasing attention in the application of brain-computer interface technology (BCI). Supervised methods used to classify EEG signals have gotten satisfactory results. However, unlabeled samples are more frequent than labeled samples, so how to simultaneously utilize limited labeled samples and many unlabeled samples becomes a research hotspot. In this paper, we propose a new graph-based semi-supervised broad learning system (GSS-BLS), which combines the graph label propagation method to obtain pseudo-labels and then trains the GSS-BLS classifier together with other labeled samples. Three BCI competition datasets are used to assess the GSS-BLS approach and five comparison algorithms: BLS, ELM, HELM, LapSVM and SMIR. The experimental results show that GSS-BLS achieves satisfying Cohen’s kappa values in three datasets. GSS-BLS achieves the better results of each subject in the 2-class and 4-class datasets and has significant improvements compared with original BLS except subject C6. Therefore, the proposed GSS-BLS is an effective semi-supervised algorithm for classifying EEG signals. Full article
Open AccessArticle
Online Home Appliance Control Using EEG-Based Brain–Computer Interfaces
Electronics 2019, 8(10), 1101; https://doi.org/10.3390/electronics8101101 - 30 Sep 2019
Abstract
Brain–computer interfaces (BCIs) allow patients with paralysis to control external devices by mental commands. Recent advances in home automation and the Internet of things may extend the horizon of BCI applications into daily living environments at home. In this study, we developed an [...] Read more.
Brain–computer interfaces (BCIs) allow patients with paralysis to control external devices by mental commands. Recent advances in home automation and the Internet of things may extend the horizon of BCI applications into daily living environments at home. In this study, we developed an online BCI based on scalp electroencephalography (EEG) to control home appliances. The BCI users controlled TV channels, a digital door-lock system, and an electric light system in an unshielded environment. The BCI was designed to harness P300 and N200 components of event-related potentials (ERPs). On average, the BCI users could control TV channels with an accuracy of 83.0% ± 17.9%, the digital door-lock with 78.7% ± 16.2% accuracy, and the light with 80.0% ± 15.6% accuracy, respectively. Our study demonstrates a feasibility to control multiple home appliances using EEG-based BCIs. Full article
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Open AccessArticle
A Dry Electrode Cap and Its Application in a Steady-State Visual Evoked Potential-Based Brain–Computer Interface
Electronics 2019, 8(10), 1080; https://doi.org/10.3390/electronics8101080 - 23 Sep 2019
Cited by 1
Abstract
The wearable electroencephalogram (EEG) dry electrode acquisition system has shown great application prospects in mental state monitoring, the brain–computer interface (BCI), and other fields due to advantages such as being small in volume, light weight, and a ready-to-use facility. This study demonstrates a [...] Read more.
The wearable electroencephalogram (EEG) dry electrode acquisition system has shown great application prospects in mental state monitoring, the brain–computer interface (BCI), and other fields due to advantages such as being small in volume, light weight, and a ready-to-use facility. This study demonstrates a novel EEG cap with concise structure, easy adjustment size, as well as independently adjustable electrodes. The cap can be rapidly worn and adjusted in both horizontal and vertical dimensions. The dry electrodes on it can be adjusted independently to fit the scalp as quickly as possible. The accuracy of the BCI test employing this device is higher than when employing a headband. The proposed EEG cap makes adjustment easier and the contact impedance of the dry electrodes more uniform. Full article
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