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Motor Imagery EEG Signal Recognition Using Deep Convolution Neural Network
- Source :
- Frontiers in Neuroscience, Frontiers in Neuroscience, Vol 15 (2021)
- Publication Year :
- 2021
- Publisher :
- Frontiers Media SA, 2021.
-
Abstract
- 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.
- Subjects :
- Computer science
Electroencephalography
Convolutional neural network
lcsh:RC321-571
Convolution
Motor imagery
deep convolutional neural network
medicine
Feature (machine learning)
lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry
Original Research
Brain–computer interface
BCI classifier
motor imagination
EEG signal
medicine.diagnostic_test
business.industry
General Neuroscience
Short-time Fourier transform
Pattern recognition
Morlet wavelet
short time fourier transform
continuous morlet wavelet transform
Artificial intelligence
business
CSP algorithm
Neuroscience
Subjects
Details
- ISSN :
- 1662453X
- Volume :
- 15
- Database :
- OpenAIRE
- Journal :
- Frontiers in Neuroscience
- Accession number :
- edsair.doi.dedup.....91c6e6752db8ebbe70a0a9c2b16135d1