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Brain-Computer Interfaces for Robotics and Environmental Control
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Brain-Computer Interfaces for Robotics and Environmental Control

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (29 March 2021) | Viewed by 5105

Special Issue Editors

Prof. Dr. Chang-Hwan Im

E-Mail Website
Guest Editor
Department of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 133-791, Republic of Korea
Interests: neural signal processing; brain-computer interface (BCI); biomedical signal analysis
Dr. Sung-Phil Kim

E-Mail Website
Guest Editor
College of Information-Bioconvergence Engineering, Department of Biomedical Engineering, UNIST, Ulsan 44919, Korea
Interests: brain-computer interfaces; neural decoding; artificial tactile perception; EEG signal processing

Special Issue Information

Dear Colleagues,

Brain-computer interfaces (BCIs), also referred to as brain-machine interfaces (BMIs), have been developed to provide those who have lost normal communication pathways with a new mode of communication. Among the diverse application fields of BCIs, this Special Issue focuses on the BCI technologies applicable to robotics and environmental control. The target devices can range from traditional assistive devices, such as robotic arms and electric wheelchairs, to home appliances and mobile devices. Original papers that describe innovative BCI applications and research reports including practical use cases of implemented BCIs are especially welcome. We look forward to and welcome your participation in this Special Issue.

Prof. Dr. Chang-Hwan Im
Prof. Dr. Sung-Phil Kim
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. Sensors 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 2600 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

  • Brain-computer interface (BCI)
  • Brain-machine interface (BMI)
  • Robotics
  • Environmental control
  • Assistive device
  • Neural signal
  • Biomedical signal processing

Published Papers (2 papers)

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Research

17 pages, 2101 KiB  
Article
Single-Trial Kernel-Based Functional Connectivity for Enhanced Feature Extraction in Motor-Related Tasks
by Daniel Guillermo García-Murillo, Andres Alvarez-Meza and German Castellanos-Dominguez
Sensors 2021, 21(8), 2750; https://doi.org/10.3390/s21082750 - 13 Apr 2021
Cited by 10 | Viewed by 2101
Abstract
Motor learning is associated with functional brain plasticity, involving specific functional connectivity changes in the neural networks. However, the degree of learning new motor skills varies among individuals, which is mainly due to the between-subject variability in brain structure and function captured by [...] Read more.
Motor learning is associated with functional brain plasticity, involving specific functional connectivity changes in the neural networks. However, the degree of learning new motor skills varies among individuals, which is mainly due to the between-subject variability in brain structure and function captured by electroencephalographic (EEG) recordings. Here, we propose a kernel-based functional connectivity measure to deal with inter/intra-subject variability in motor-related tasks. To this end, from spatio-temporal-frequency patterns, we extract the functional connectivity between EEG channels through their Gaussian kernel cross-spectral distribution. Further, we optimize the spectral combination weights within a sparse-based ℓ2-norm feature selection framework matching the motor-related labels that perform the dimensionality reduction of the extracted connectivity features. From the validation results in three databases with motor imagery and motor execution tasks, we conclude that the single-trial Gaussian functional connectivity measure provides very competitive classifier performance values, being less affected by feature extraction parameters, like the sliding time window, and avoiding the use of prior linear spatial filtering. We also provide interpretability for the clustered functional connectivity patterns and hypothesize that the proposed kernel-based metric is promising for evaluating motor skills. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces for Robotics and Environmental Control)
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16 pages, 2597 KiB  
Article
Improvement of P300-Based Brain–Computer Interfaces for Home Appliances Control by Data Balancing Techniques
by Taejun Lee, Minju Kim and Sung-Phil Kim
Sensors 2020, 20(19), 5576; https://doi.org/10.3390/s20195576 - 29 Sep 2020
Cited by 16 | Viewed by 2481
Abstract
The oddball paradigm used in P300-based brain–computer interfaces (BCIs) intrinsically poses the issue of data imbalance between target stimuli and nontarget stimuli. Data imbalance can cause overfitting problems and, consequently, poor classification performance. The purpose of this study is to improve BCI performance [...] Read more.
The oddball paradigm used in P300-based brain–computer interfaces (BCIs) intrinsically poses the issue of data imbalance between target stimuli and nontarget stimuli. Data imbalance can cause overfitting problems and, consequently, poor classification performance. The purpose of this study is to improve BCI performance by solving this data imbalance problem with sampling techniques. The sampling techniques were applied to BCI data in 15 subjects controlling a door lock, 15 subjects an electric light, and 14 subjects a Bluetooth speaker. We explored two categories of sampling techniques: oversampling and undersampling. Oversampling techniques, including random oversampling, synthetic minority oversampling technique (SMOTE), borderline-SMOTE, support vector machine (SVM) SMOTE, and adaptive synthetic sampling, were used to increase the number of samples for the class of target stimuli. Undersampling techniques, including random undersampling, neighborhood cleaning rule, Tomek’s links, and weighted undersampling bagging, were used to reduce the class size of nontarget stimuli. The over- or undersampled data were classified by an SVM classifier. Overall, some oversampling techniques improved BCI performance while undersampling techniques often degraded performance. Particularly, using borderline-SMOTE yielded the highest accuracy (87.27%) and information transfer rate (8.82 bpm) across all three appliances. Moreover, borderline-SMOTE led to performance improvement, especially for poor performers. A further analysis showed that borderline-SMOTE improved SVM by generating more support vectors within the target class and enlarging margins. However, there was no difference in the accuracy between borderline-SMOTE and the method of applying the weighted regularization parameter of the SVM. Our results suggest that although oversampling improves performance of P300-based BCIs, it is not just the effect of the oversampling techniques, but rather the effect of solving the data imbalance problem. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces for Robotics and Environmental Control)
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