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Applied Sciences
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Brain-Computer Interfaces: Novel Technologies and Applications
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Brain-Computer Interfaces: Novel Technologies and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Neuroscience and Neural Engineering".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 6637

Special Issue Editor

Prof. Dr. Banghua Yang

E-Mail Website
Guest Editor
Schoolof Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
Interests: brain–computer interface; deep learning; virtual reality/augmented reality technology; electric/magnetic nerve regulation technology

Special Issue Information

Dear Colleagues,

With advances in brain science and computer science, the brain-computer interface (BCI) has become a top research area in applied science. A BCI can provide humans with capabilities to communicate and control through brain activities instead of peripheral nerves and muscles. BCIs are a multidisciplinary emerging technology that integrates neuroscience, information science, computer science, and robot technology, etc. It can establish a direct connection between brain activity and external devices by decoding, so that humans have the ability to control external devices. In recent years, BCIs have been applied in many fields, especially in clinical and rehabilitation fields. In addition, its achievements in various aspects such as brain-controlled prosthetics, disease diagnosis, and motor rehabilitation are outstanding.

This Special Issue aims to provide an opportunity for researchers to contribute their most recent research and developments in the BCI field. Topics include but are not limited to the following:

  • The brain-computer interface;
  • Human-machine interaction;
  • Rehabilitation robotics;
  • Brain signal decoding;
  • Neuroregulation and the brain-computer interface.

Prof. Dr. Banghua Yang
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. Applied Sciences 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.

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Published Papers (3 papers)

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Research

12 pages, 4778 KiB  
Article
Motor Imagery Classification Improvement of Two-Class Data with Covariance Decentering Eigenface Analysis for Brain–Computer Interface Systems
by Hojong Choi, Junghun Park and Yeon-Mo Yang
Appl. Sci. 2024, 14(21), 10062; https://doi.org/10.3390/app142110062 - 4 Nov 2024
Cited by 1 | Viewed by 993
Abstract
This study is intended to improve the motor imagery classification performance of two-class data points using newly developed covariance decentering eigenface analysis (CDC-EFA). When extracting the classification for the given data points, it is necessary to precisely distinguish the classes because the left [...] Read more.
This study is intended to improve the motor imagery classification performance of two-class data points using newly developed covariance decentering eigenface analysis (CDC-EFA). When extracting the classification for the given data points, it is necessary to precisely distinguish the classes because the left and right features are difficult to differentiate. However, when centering is performed, the unique average data of each feature are lost, making them difficult to distinguish. CDC-EFA reverses the centering method to enhance data characteristics, making it possible to assign weights to data with a high correlation with other data. In experiments with the BCI dataset, the proposed CDC-EFA method was used after preprocessing by filtering and selecting the electroencephalogram data. The decentering process was then performed on the covariance matrix calculated when acquiring the unique face. Subsequently, we verified the classification improvement performance via simulations using several BCI competition datasets. Several signal processing methods were applied to compare the accuracy results of the motor imagery classification. The proposed CDC-EFA method yielded an average accuracy result of 98.89%. Thus, it showed improved accuracy compared with the other methods and stable performance with a low standard deviation. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces: Novel Technologies and Applications)
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33 pages, 8831 KiB  
Article
A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression
by Athanasios Delis, George Tsavdaridis and Panayiotis Tsanakas
Appl. Sci. 2024, 14(16), 6886; https://doi.org/10.3390/app14166886 - 6 Aug 2024
Viewed by 2495
Abstract
In this study, the fundamentals of electroencephalography signals, their categorization into frequency sub-bands, the circuitry used for their acquisition, and the impact of noise interference on signal acquisition are examined. Additionally, design specifications for medical-grade and research-grade EEG circuits and a comprehensive analysis [...] Read more.
In this study, the fundamentals of electroencephalography signals, their categorization into frequency sub-bands, the circuitry used for their acquisition, and the impact of noise interference on signal acquisition are examined. Additionally, design specifications for medical-grade and research-grade EEG circuits and a comprehensive analysis of various analog front-end architectures for electroencephalograph (EEG) circuit design are presented. Three distinct selected case studies are examined in terms of comparative evaluation with generic EEG circuit design templates. Moreover, a novel one-channel battery-supplied EEG analog front-end circuit designed to address the requirements of usage protocols containing strong compound muscle movements is introduced. Furthermore, a realistic input signal generator circuit is proposed that models the human body and the electromagnetic interference from its surroundings. Experimental simulations are conducted in 50 Hz and 60 Hz electrical grid environments to evaluate the performance of the novel design. The results demonstrate the efficacy of the proposed system, particularly in terms of bandwidth, portability, Common Mode Rejection Ratio, gain, suppression of muscle movement artifacts, electrostatic discharge and leakage current protection. Conclusively, the novel design is cost-effective and suitable for both commercial and research single-channel EEG applications. It can be easily incorporated in Brain–Computer Interfaces and neurofeedback training systems. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces: Novel Technologies and Applications)
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18 pages, 2035 KiB  
Article
Comparing Several P300-Based Visuo-Auditory Brain-Computer Interfaces for a Completely Locked-in ALS Patient: A Longitudinal Case Study
by Rute Bettencourt, Miguel Castelo-Branco, Edna Gonçalves, Urbano J. Nunes and Gabriel Pires
Appl. Sci. 2024, 14(8), 3464; https://doi.org/10.3390/app14083464 - 19 Apr 2024
Cited by 1 | Viewed by 2319
Abstract
In a completely locked-in state (CLIS), often resulting from traumatic brain injury or neurodegenerative diseases like amyotrophic lateral sclerosis (ALS), patients lose voluntary muscle control, including eye movement, making communication impossible. Brain-computer interfaces (BCIs) offer hope for restoring communication, but achieving reliable communication [...] Read more.
In a completely locked-in state (CLIS), often resulting from traumatic brain injury or neurodegenerative diseases like amyotrophic lateral sclerosis (ALS), patients lose voluntary muscle control, including eye movement, making communication impossible. Brain-computer interfaces (BCIs) offer hope for restoring communication, but achieving reliable communication with these patients remains a challenge. This study details the design, testing, and comparison of nine visuo-auditory P300-based BCIs (combining different visual and auditory stimuli and different visual layouts) with a CLIS patient over ten months. The aim was to evaluate the impact of these stimuli in achieving effective communication. While some interfaces showed promising progress, achieving up to 90% online accuracy in one session, replicating this success in subsequent sessions proved challenging, with the average online accuracy across all sessions being 56.4 ± 15.2%. The intertrial variability in EEG signals and the low discrimination between target and non-target events were the main challenge. Moreover, the lack of communication with the patient made BCI design a challenging blind trial-and-error process. Despite the inconsistency of the results, it was possible to infer that the combination of visual and auditory stimuli had a positive impact, and that there was an improvement over time. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces: Novel Technologies and Applications)
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