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Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement

Recently, convolutional neural networks (CNNs) have been widely applied in brain-computer interface (BCI) based on electroencephalogram (EEG) signals. Due to the subject-specific nature of EEG signal patterns and the multi-dimensionality of EEG features, it is necessary to employ appropriate feature...

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Autores principales: Fan, Chengcheng, Yang, Banghua, Li, Xiaoou, Zan, Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10493321/
https://www.ncbi.nlm.nih.gov/pubmed/37700746
http://dx.doi.org/10.3389/fnins.2023.1250991
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author Fan, Chengcheng
Yang, Banghua
Li, Xiaoou
Zan, Peng
author_facet Fan, Chengcheng
Yang, Banghua
Li, Xiaoou
Zan, Peng
author_sort Fan, Chengcheng
collection PubMed
description Recently, convolutional neural networks (CNNs) have been widely applied in brain-computer interface (BCI) based on electroencephalogram (EEG) signals. Due to the subject-specific nature of EEG signal patterns and the multi-dimensionality of EEG features, it is necessary to employ appropriate feature representation methods to enhance the decoding accuracy of EEG. In this study, we proposed a method for representing EEG temporal, frequency, and phase features, aiming to preserve the multi-domain information of EEG signals. Specifically, we generated EEG temporal segments using a sliding window strategy. Then, temporal, frequency, and phase features were extracted from different temporal segments and stacked into 3D feature maps, namely temporal-frequency-phase features (TFPF). Furthermore, we designed a compact 3D-CNN model to extract these multi-domain features efficiently. Considering the inter-individual variability in EEG data, we conducted individual testing for each subject. The proposed model achieved an average accuracy of 89.86, 78.85, and 63.55% for 2-class, 3-class, and 4-class motor imagery (MI) classification tasks, respectively, on the PhysioNet dataset. On the GigaDB dataset, the average accuracy for 2-class MI classification was 91.91%. For the comparison between MI and real movement (ME) tasks, the average accuracy for the 2-class were 87.66 and 80.13% on the PhysioNet and GigaDB datasets, respectively. Overall, the method presented in this paper have obtained good results in MI/ME tasks and have a good application prospect in the development of BCI systems based on MI/ME.
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spelling pubmed-104933212023-09-12 Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement Fan, Chengcheng Yang, Banghua Li, Xiaoou Zan, Peng Front Neurosci Neuroscience Recently, convolutional neural networks (CNNs) have been widely applied in brain-computer interface (BCI) based on electroencephalogram (EEG) signals. Due to the subject-specific nature of EEG signal patterns and the multi-dimensionality of EEG features, it is necessary to employ appropriate feature representation methods to enhance the decoding accuracy of EEG. In this study, we proposed a method for representing EEG temporal, frequency, and phase features, aiming to preserve the multi-domain information of EEG signals. Specifically, we generated EEG temporal segments using a sliding window strategy. Then, temporal, frequency, and phase features were extracted from different temporal segments and stacked into 3D feature maps, namely temporal-frequency-phase features (TFPF). Furthermore, we designed a compact 3D-CNN model to extract these multi-domain features efficiently. Considering the inter-individual variability in EEG data, we conducted individual testing for each subject. The proposed model achieved an average accuracy of 89.86, 78.85, and 63.55% for 2-class, 3-class, and 4-class motor imagery (MI) classification tasks, respectively, on the PhysioNet dataset. On the GigaDB dataset, the average accuracy for 2-class MI classification was 91.91%. For the comparison between MI and real movement (ME) tasks, the average accuracy for the 2-class were 87.66 and 80.13% on the PhysioNet and GigaDB datasets, respectively. Overall, the method presented in this paper have obtained good results in MI/ME tasks and have a good application prospect in the development of BCI systems based on MI/ME. Frontiers Media S.A. 2023-08-28 /pmc/articles/PMC10493321/ /pubmed/37700746 http://dx.doi.org/10.3389/fnins.2023.1250991 Text en Copyright © 2023 Fan, Yang, Li and Zan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Fan, Chengcheng
Yang, Banghua
Li, Xiaoou
Zan, Peng
Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement
title Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement
title_full Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement
title_fullStr Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement
title_full_unstemmed Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement
title_short Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement
title_sort temporal-frequency-phase feature classification using 3d-convolutional neural networks for motor imagery and movement
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10493321/
https://www.ncbi.nlm.nih.gov/pubmed/37700746
http://dx.doi.org/10.3389/fnins.2023.1250991
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