Name
Title | ||
|---|---|---|
Maximum dependency and minimum redundancy-based channel selection for motor imagery of walking EEG signal detection |
Abstract | ||
|---|---|---|
This paper proposes a novel method to detect motor imagery of walking for the rehabilitation of stroke patients using the Laplacian derivatives (LAD) of power averaged across frequency bands as the feature. We propose to select the most correlated channels by jointly considering the mutual information between the LAD power features of the channels and the class labels, and the redundancy between the LAD power features of the channel with that of the selected channels. Experiments are conducted on the EEG data collected for 11 healthy subjects using proposed method and compared with existing methods. The results show that the proposed method yielded an average classification accuracy of 67.19% by selecting as few as 4 LAD channels. An improved result of 71.45% and 73.23% are achieved by selecting 10 and 22 LAD channels, respectively. Comparison results revealed significantly superior performance of our proposed method compared to that obtained using common spatial pattern and filter bank with power features. Most importantly, our proposed method achieves significant better accuracy for poor BCI performers compared to existing methods. Thus, the results demonstrated the potential of using the proposed method for detecting motor imagery of walking for the rehabilitation of stroke patients. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICASSP.2013.6637838 | ICASSP |
Keywords | Field | DocType |
eeg signal,medical signal detection,minimum redundancy,electroencephalography,rehabilitation,eeg signal detection,feature extraction,patient rehabilitation,laplacian derivatives,signal classification,correlated channels,minimum redundancy based channel selection,lad power features,motor imagery of walking,classification accuracy,stroke patient rehabilitation,brain computer interface,mutual information,walking motor imagery,accuracy,redundancy,electrodes | Pattern recognition,Detection theory,Computer science,Brain–computer interface,Filter bank,Communication channel,Feature extraction,Speech recognition,Redundancy (engineering),Artificial intelligence,Mutual information,Motor imagery | Conference |
ISSN | Citations | PageRank |
1520-6149 | 2 | 0.36 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huijuan Yang | 1 | 23 | 4.93 |
| Cuntai Guan | 2 | 1298 | 124.69 |
| Chuanchu Wang | 3 | 93 | 17.16 |
| Kai Keng Ang | 4 | 804 | 64.19 |