| アイテムタイプ |
共通アイテムタイプ(1) |
| 公開日 |
2025-02-18 |
| タイトル |
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|
タイトル |
Motor Imagery Classification Using Effective Channel Selection of Multichannel EEG |
|
言語 |
en |
| 著者 |
Shiam, Abdullah Al
Hassan, Kazi Mahmudul
Islam, Md. Rabiul
Almassri, Ahmed M. M.
我妻, 広明
Molla, Md. Khademul Islam
|
| 著作権関連情報 |
|
|
権利情報 |
Copyright (c) 2024 by the authors. Licensee MDPI, Basel, Switzerland. |
| 著作権関連情報 |
|
|
権利情報Resource |
https://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain–computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain–computer interface (BCI) systems. The method identifies channels with higher entropy scores, which is an indication of greater information content. It discards redundant or noisy channels leading to reduced computational complexity and improved classification accuracy. High entropy means a more disordered pattern, whereas low entropy means a less disordered pattern with less information. The entropy of each channel for individual trials is calculated. The weight of each channel is represented by the mean entropy of the channel over all the trials. A set of channels with higher mean entropy are selected as effective channels for MI classification. A limited number of sub-band signals are created by decomposing the selected channels. To extract the spatial features, the common spatial pattern (CSP) is applied to each sub-band space of EEG signals. The CSP-based features are used to classify the right-hand and right-foot MI tasks using a support vector machine (SVM). The effectiveness of the proposed approach is validated using two publicly available EEG datasets, known as BCI competition III–IV(A) and BCI competition IV–I. The experimental results demonstrate that the proposed approach surpasses cutting-edge techniques. |
|
言語 |
en |
| 書誌情報 |
en : Brain Sciences
巻 14,
号 5,
p. 462,
発行日 2024-05-03
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| 出版社 |
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|
出版者 |
MDPI |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
brain–computer interface |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
channel selection |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
electroencephalography |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
entropy-based information |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
motor imagery |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.3390/brainsci14050462 |
| ISSN |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
2076-3425 |
| 研究者情報 |
|
|
URL |
https://hyokadb02.jimu.kyutech.ac.jp/html/358_ja.html |
| 論文ID（連携） |
|
|
値 |
10448553 |
| 連携ID |
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|
値 |
13023 |
10448553.pdf (493 KB)
