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Subject-independent EEG classification based on a hybrid neural network
A brain-computer interface (BCI) based on the electroencephalograph (EEG) signal is a novel technology that provides a direct pathway between human brain and outside world. For a traditional subject-dependent BCI system, a calibration procedure is required to collect sufficient data to build a subje...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10272421/ https://www.ncbi.nlm.nih.gov/pubmed/37332856 http://dx.doi.org/10.3389/fnins.2023.1124089 |
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author | Zhang, Hao Ji, Hongfei Yu, Jian Li, Jie Jin, Lingjing Liu, Lingyu Bai, Zhongfei Ye, Chen |
author_facet | Zhang, Hao Ji, Hongfei Yu, Jian Li, Jie Jin, Lingjing Liu, Lingyu Bai, Zhongfei Ye, Chen |
author_sort | Zhang, Hao |
collection | PubMed |
description | A brain-computer interface (BCI) based on the electroencephalograph (EEG) signal is a novel technology that provides a direct pathway between human brain and outside world. For a traditional subject-dependent BCI system, a calibration procedure is required to collect sufficient data to build a subject-specific adaptation model, which can be a huge challenge for stroke patients. In contrast, subject-independent BCI which can shorten or even eliminate the pre-calibration is more time-saving and meets the requirements of new users for quick access to the BCI. In this paper, we design a novel fusion neural network EEG classification framework that uses a specially designed generative adversarial network (GAN), called a filter bank GAN (FBGAN), to acquire high-quality EEG data for augmentation and a proposed discriminative feature network for motor imagery (MI) task recognition. Specifically, multiple sub-bands of MI EEG are first filtered using a filter bank approach, then sparse common spatial pattern (CSP) features are extracted from multiple bands of filtered EEG data, which constrains the GAN to maintain more spatial features of the EEG signal, and finally we design a convolutional recurrent network classification method with discriminative features (CRNN-DF) to recognize MI tasks based on the idea of feature enhancement. The hybrid neural network proposed in this study achieves an average classification accuracy of 72.74 ± 10.44% (mean ± std) in four-class tasks of BCI IV-2a, which is 4.77% higher than the state-of-the-art subject-independent classification method. A promising approach is provided to facilitate the practical application of BCI. |
format | Online Article Text |
id | pubmed-10272421 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102724212023-06-17 Subject-independent EEG classification based on a hybrid neural network Zhang, Hao Ji, Hongfei Yu, Jian Li, Jie Jin, Lingjing Liu, Lingyu Bai, Zhongfei Ye, Chen Front Neurosci Neuroscience A brain-computer interface (BCI) based on the electroencephalograph (EEG) signal is a novel technology that provides a direct pathway between human brain and outside world. For a traditional subject-dependent BCI system, a calibration procedure is required to collect sufficient data to build a subject-specific adaptation model, which can be a huge challenge for stroke patients. In contrast, subject-independent BCI which can shorten or even eliminate the pre-calibration is more time-saving and meets the requirements of new users for quick access to the BCI. In this paper, we design a novel fusion neural network EEG classification framework that uses a specially designed generative adversarial network (GAN), called a filter bank GAN (FBGAN), to acquire high-quality EEG data for augmentation and a proposed discriminative feature network for motor imagery (MI) task recognition. Specifically, multiple sub-bands of MI EEG are first filtered using a filter bank approach, then sparse common spatial pattern (CSP) features are extracted from multiple bands of filtered EEG data, which constrains the GAN to maintain more spatial features of the EEG signal, and finally we design a convolutional recurrent network classification method with discriminative features (CRNN-DF) to recognize MI tasks based on the idea of feature enhancement. The hybrid neural network proposed in this study achieves an average classification accuracy of 72.74 ± 10.44% (mean ± std) in four-class tasks of BCI IV-2a, which is 4.77% higher than the state-of-the-art subject-independent classification method. A promising approach is provided to facilitate the practical application of BCI. Frontiers Media S.A. 2023-06-02 /pmc/articles/PMC10272421/ /pubmed/37332856 http://dx.doi.org/10.3389/fnins.2023.1124089 Text en Copyright © 2023 Zhang, Ji, Yu, Li, Jin, Liu, Bai and Ye. 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 Zhang, Hao Ji, Hongfei Yu, Jian Li, Jie Jin, Lingjing Liu, Lingyu Bai, Zhongfei Ye, Chen Subject-independent EEG classification based on a hybrid neural network |
title | Subject-independent EEG classification based on a hybrid neural network |
title_full | Subject-independent EEG classification based on a hybrid neural network |
title_fullStr | Subject-independent EEG classification based on a hybrid neural network |
title_full_unstemmed | Subject-independent EEG classification based on a hybrid neural network |
title_short | Subject-independent EEG classification based on a hybrid neural network |
title_sort | subject-independent eeg classification based on a hybrid neural network |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10272421/ https://www.ncbi.nlm.nih.gov/pubmed/37332856 http://dx.doi.org/10.3389/fnins.2023.1124089 |
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