Towards correlation-based time window selection method for motor imagery BCIs.

The start of the cue is often used to initiate the feature window used to control motor imagery (MI)-based brain-computer interface (BCI) systems. However, the time latency during an MI period varies between trials for each participant. Fixing the starting time point of MI features can lead to decreased system performance in MI-based BCI systems. To address this issue, we propose a novel correlation-based time window selection (CTWS) algorithm for MI-based BCIs. Specifically, the optimized reference signals for each class were selected based on correlation analysis and performance evaluation. Furthermore, the starting points of time windows for both training and testing samples were adjusted using correlation analysis. Finally, the feature extraction and classification algorithms were used to calculate the classification accuracy. With two datasets, the results demonstrate that the CTWS algorithm significantly improved the system performance when compared to directly using feature extraction approaches. Importantly, the average improvement in accuracy of the CTWS algorithm on the datasets of healthy participants and stroke patients was 16.72% and 5.24%, respectively when compared to traditional common spatial pattern (CSP) algorithm. In addition, the average accuracy increased 7.36% and 9.29%, respectively when the CTWS was used in conjunction with Sub-Alpha-Beta Log-Det Divergences (Sub-ABLD) algorithm. These findings suggest that the proposed CTWS algorithm holds promise as a general feature extraction approach for MI-based BCIs.