Abstract
For motor imagery (MI)-based brain–computer interface (BCI) systems, the time latency and length of MI task vary between trials and subjects, due to the differences between subjects’ reaction time and personal habits. Therefore, the starting and ending time point of each MI task can hardly be determined manually for different subjects. Fixed time window may contain task-irrelevant signals or does not contain sufficient task-related signals, which will lead to degraded the performance of MI-based BCI systems. To address this issue, an optimized correlation-based time window selection (OCTWS) algorithm is proposed for MI-based BCIs. The optimized starting point and length of MI task-relevant signals are determined simultaneously based on correlation analysis and performance evaluation. A public EEG dataset (BCI Competition IV Dataset I) is used to evaluate the proposed OCTWS method. Experimental results demonstrate that OCTWS helps improve the feature extraction and classification performance of MI.
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