Calibration Time Reduction for Motor Imagery-Based BCI Using Batch Mode Active Learning

Brain-computer interface (BCI) has enormous potential applications from rehabilitation of neural disease patient to driver’s drowsiness prediction. However, the feasibility of BCI in the practical application other than laboratory prototype is dependent on the usability of a human subject on the go. Most of the time, it needs long and in-depth calibration of the system for training purpose. Different novel methods have been proposed to shorten the calibration time maintaining the robust performance. One of them is active learning (AL) which asks for labeling the training samples and it has the potential to reach robust performance using reduced informative training set. In this work, one of the AL methods, query by committee (QBC) is applied by three state-of-the-art feature extraction methods coupled with linear discriminant analysis classifier for motor imagery-based BCI. The joint accuracy by the members of QBC has obtained the baselines using maximum 33% of the whole training set. It also shows a significant difference at the 5% significance level from contemporary AL methods and random sampling method. Thus, QBC has reduced the labeling effort as well as the training data collection effort significantly more than that of random labeling process. It infers that QBC is a potential candidate for abridging overall calibration time of BCI systems.