Informative instance transfer learning with subject specific frequency responses for motor imagery Brain Computer Interface

Motor imagery based brain computer interface (BCI) has drawback of long subject dependent calibration session times. This can be a very exhausting and a time consuming process. In order to alleviate it, transfer learning and active learning approaches can be utilised. Informative instances are selected by applying active learning concept from other subjects under similar circumstances. Then, they are transferred to target user domain which has low number of training data. This informative transfer learning approach is associated with common spatial pattern (CSP) as feature extraction method in our previous attempt. CSP features are widely used for motor imagery-based BCI systems. However, the classical CSP algorithm will perform poorly when operational frequency bands are inadequately selected. Therefore, in the present study, filter bank common spatial pattern (FBCSP) algorithm has been applied for extracting features from the multi-class motor imagery data. FBCSP algorithm selects subject-specific operational frequency bands for extracting discriminative features. We incorporated FBCSP features into informative instance transfer learning framework to investigate the effect of subject specific feature selection. Results show that performance of new users can be improved with reduced number of training samples when FBCSP features are used compared to the classical CSP-based features