Cargando…
Motor Imagery EEG Signal Recognition Using Deep Convolution Neural Network
Brain computer interaction (BCI) based on EEG can help patients with limb dyskinesia to carry out daily life and rehabilitation training. However, due to the low signal-to-noise ratio and large individual differences, EEG feature extraction and classification have the problems of low accuracy and ef...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8027090/ https://www.ncbi.nlm.nih.gov/pubmed/33841094 http://dx.doi.org/10.3389/fnins.2021.655599 |
_version_ | 1783675753526722560 |
---|---|
author | Xiao, Xiongliang Fang, Yuee |
author_facet | Xiao, Xiongliang Fang, Yuee |
author_sort | Xiao, Xiongliang |
collection | PubMed |
description | Brain computer interaction (BCI) based on EEG can help patients with limb dyskinesia to carry out daily life and rehabilitation training. However, due to the low signal-to-noise ratio and large individual differences, EEG feature extraction and classification have the problems of low accuracy and efficiency. To solve this problem, this paper proposes a recognition method of motor imagery EEG signal based on deep convolution network. This method firstly aims at the problem of low quality of EEG signal characteristic data, and uses short-time Fourier transform (STFT) and continuous Morlet wavelet transform (CMWT) to preprocess the collected experimental data sets based on time series characteristics. So as to obtain EEG signals that are distinct and have time-frequency characteristics. And based on the improved CNN network model to efficiently recognize EEG signals, to achieve high-quality EEG feature extraction and classification. Further improve the quality of EEG signal feature acquisition, and ensure the high accuracy and precision of EEG signal recognition. Finally, the proposed method is validated based on the BCI competiton dataset and laboratory measured data. Experimental results show that the accuracy of this method for EEG signal recognition is 0.9324, the precision is 0.9653, and the AUC is 0.9464. It shows good practicality and applicability. |
format | Online Article Text |
id | pubmed-8027090 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80270902021-04-09 Motor Imagery EEG Signal Recognition Using Deep Convolution Neural Network Xiao, Xiongliang Fang, Yuee Front Neurosci Neuroscience Brain computer interaction (BCI) based on EEG can help patients with limb dyskinesia to carry out daily life and rehabilitation training. However, due to the low signal-to-noise ratio and large individual differences, EEG feature extraction and classification have the problems of low accuracy and efficiency. To solve this problem, this paper proposes a recognition method of motor imagery EEG signal based on deep convolution network. This method firstly aims at the problem of low quality of EEG signal characteristic data, and uses short-time Fourier transform (STFT) and continuous Morlet wavelet transform (CMWT) to preprocess the collected experimental data sets based on time series characteristics. So as to obtain EEG signals that are distinct and have time-frequency characteristics. And based on the improved CNN network model to efficiently recognize EEG signals, to achieve high-quality EEG feature extraction and classification. Further improve the quality of EEG signal feature acquisition, and ensure the high accuracy and precision of EEG signal recognition. Finally, the proposed method is validated based on the BCI competiton dataset and laboratory measured data. Experimental results show that the accuracy of this method for EEG signal recognition is 0.9324, the precision is 0.9653, and the AUC is 0.9464. It shows good practicality and applicability. Frontiers Media S.A. 2021-03-25 /pmc/articles/PMC8027090/ /pubmed/33841094 http://dx.doi.org/10.3389/fnins.2021.655599 Text en Copyright © 2021 Xiao and Fang. 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 Xiao, Xiongliang Fang, Yuee Motor Imagery EEG Signal Recognition Using Deep Convolution Neural Network |
title | Motor Imagery EEG Signal Recognition Using Deep Convolution Neural Network |
title_full | Motor Imagery EEG Signal Recognition Using Deep Convolution Neural Network |
title_fullStr | Motor Imagery EEG Signal Recognition Using Deep Convolution Neural Network |
title_full_unstemmed | Motor Imagery EEG Signal Recognition Using Deep Convolution Neural Network |
title_short | Motor Imagery EEG Signal Recognition Using Deep Convolution Neural Network |
title_sort | motor imagery eeg signal recognition using deep convolution neural network |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8027090/ https://www.ncbi.nlm.nih.gov/pubmed/33841094 http://dx.doi.org/10.3389/fnins.2021.655599 |
work_keys_str_mv | AT xiaoxiongliang motorimageryeegsignalrecognitionusingdeepconvolutionneuralnetwork AT fangyuee motorimageryeegsignalrecognitionusingdeepconvolutionneuralnetwork |