Subject transfer BCI based on Composite Local Temporal Correlation Common Spatial Pattern.

In this paper, a subject transfer framework is proposed for the classification of Electroencephalogram (EEG) signals in brain-computer interfaces (BCIs). This study introduces a modification of Common Spatial Pattern (CSP) for subject transfer BCIs, where similar characteristics are considered to transfer knowledge from other subjects׳ data. With this aim, we proposed a new approach based on Composite Local Temporal Correlation CSP, namely Composite LTCCSP with selected subjects, which considers the similarity between subjects using Frobenius distance. The performance of the proposed method is compared with different methods like traditional CSP, Composite CSP, LTCCSP and Composite LTCCSP. Experimental results have shown that our proposed method has increased the performance compared to all these different methods. Furthermore, our results suggest that it is worth emphasizing the data of subjects with similar characteristics in a subject transfer diagram. The suggested framework, as demonstrated by experimental results, can obtain a positive knowledge transfer for enhancing the performance of BCIs.