Xiyu Song
ABSTRACT
Single-trial electroencephalography (EEG) image classification algorithms mainly depend on P300 component detection. To overcome the P300 latency instability, an enhanced hierarchical discriminant component analysis (eHDCA) algorithm is proposed based on previous hierarchical discriminant component analysis (HDCA) algorithm. In the proposed method, the overlapping time window is introduced to enhance the correlation of adjacent time window to deal with the problem of P300 latency instability. A rapid serial visual presentation (RSVP) paradigm is designed to train a single-trial image classification model. Results indicate that the proposed eHDCA outperforms existing sliding HDCA (sHDCA) with better detection performance and less computational complexity, which will contribute to real-time detection system..
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Index Terms
- An Enhanced HDCA Algorithm for Single-trial EEG Classification
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