An Adaptive Channel Selection and Graph ResNet Based Algorithm for Motor Imagery Classification

In Brain-Computer interface (BCI) applications, achieving accurate control relies heavily on the classification accuracy and efficiency of motor imagery electroencephalogram (EEG) signals. However, factors such as mutual interference between multi-channel signals, inter-individual variability, and n...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2023, Vol.14 (5)
Main Authors: Xia, Yongquan, Dong, Jianhua, Li, Duan, Li, Keyun, Nan, Jiaofen, Xu, Ruyun
Format: Article
Language:English
Subjects:
Accuracy
Adaptive algorithms
Channels
Classification
Correlation coefficients
Datasets
Electroencephalography
Human-computer interface
Image classification
Interference
Machine learning
Signal classification
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Summary:In Brain-Computer interface (BCI) applications, achieving accurate control relies heavily on the classification accuracy and efficiency of motor imagery electroencephalogram (EEG) signals. However, factors such as mutual interference between multi-channel signals, inter-individual variability, and noise interference in the channels pose challenges to motor imagery EEG signal classification. To address these problems, this paper proposes an Adaptive Channel Selection algorithm aimed at optimizing classification accuracy and Information Translate Rate (ITR). First, C3, C4, and Cz are selected as key channels based on neurophysiological evidence and extensive experimental studies. Next, the channel selection is fine-tuned using spatial location and absolute Pearson correlation coefficients. By analyzing the relationship between EEG channels and key channels, the most relevant channel combination is determined for each subject, reducing confounding information and improving classification accuracy. To validate the method, the SHU Dataset and the PhysioNet Dataset are used in experiments. The Graph ResNet classification model is employed to extract features from the selected channel combinations using deep learning techniques. Experimental results show that the average classification accuracy is improved by 5.36% and 9.19%, and the Information Translate Rate is improved by 29.24% and 26.75%, respectively, compared to a single channel combination.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2023.0140525