Multi-classification for EEG Motor Imagery Signals using Auto-selected Filter Bank Regularized Common Spatial Pattern
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT), 2022, 2022-May, pp. 1-6
- Issue Date:
- 2022-07-18
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Multi-classification for EEG Motor Imagery Signals using Auto-selected Filter Bank Regularized Common Spatial Pattern.pdf | Published version | 1.1 MB |
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Motor Imagery MI is a critical topic in Brain Computer Interface BCI Due to the low signal to noise ratio it is not easy to accurately classify motor imagery signals especially for multiple classification tasks Common Spatial Pattern CSP is a spatial transformation method that can effectively extract spatial features of EEG signals However the covariance matrix is inaccurate due to the small training data size Thus in this paper a regularization parameter auto selection algorithm is proposed to automatically adjust the ratio of the covariance matrix calculated by other subjects data based on the mutual information It can be used to tackle the problem of an inaccurate mixed covariance matrix caused by fixed regularization parameters To illustrate the merits of the proposed Auto selected Filter Bank Regularized Common Spatial Pattern AFBRCSP we used the ten folds cross validation accuracy and Kappa as the evaluation metrics to evaluate two data sets BCI4 2a and BCI3a data set Both data set include four mental classes By using BCI4 2a data set we found that the mean accuracy of AFBRSP is 77 31 and the Kappa is 0 6975 which is higher than Filter Bank Regularized Common Spatial Pattern FBRCSP by 5 67 and 0 0756 respectively By using BCI3a data set the proposed AFBRCSP improved the accuracy by 8 34 and the Kappa by 0 1111 compared with FBRCSP where the mean accuracy of AFBRCSP is 80 56 and the kappa is 0 7407 The overall Kappa obtained by the proposed method is also higher than some state of the art methods implying that the proposed method is more reliable
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