Batch Mode Query by Committee for Motor Imagery-Based BCI

Although brain–computer interface (BCI) has potential application in the rehabilitation of neural disease and performance improvement of the human in the loop system, it is restricted in the laboratory environment. One of the hindrances behind this restriction is the requirement of a long training data collection session for the user prior to operation of the system at each time. Several approaches have been proposed including the reduction of training data 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 paper, one of the AL methods, query by committee (QBC), is applied by forming the committee in heterogeneous and homogeneous feature space. In heterogeneous feature space, three state-of-the-art feature extraction methods are coupled with linear discriminant analysis classifier. For homogeneous feature space, random $K$ -fold sampling is applied after extracting the features using a single method to form the committee of $K$ -members. The joint accuracy by QBC-heterogeneous has obtained the baselines using maximum 35% of the whole training set. It also shows a significant difference at the 5% significance level from QBC-homogeneous selection as well as other contemporary AL methods and random selection method. Thus, QBC-heterogeneous has reduced the labeling effort and 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.