Calibration time reduction using subjective features selection based transfer learning for Multiclass BCI

Brain-computer interface (BCI) using machine learning has the requirement for a large number of training data. This requirement makes the long training session inevitable for a new user. Many approaches including transfer learning (TL) already have been reported in the literature to abridge the long training data collection session. One of them is transferring informative instances using active learning (AL) which was approached in our previous attempts for both binary and multiclass BCI. It was associated with the classic common spatial pattern (CSP) feature extraction method. It showed the potential to obtain the benchmark performance using a reduced amount of training data. However, it has subject dependent performance and was not up to the expectation particularly for multiclass BCI. For binary BCI, it is addressed by selecting the best subject-specific features from subjective narrow frequency window using filter bank CSP (FBCSP). Since multiclass BCI has different characteristics in terms of output performance and nature of features, this work investigates the incorporation of FBCSP into informative transfer learning with AL (ITAL) for multiclass BCI. Comparing with existing direct transfer with AL (DTAL) and ITAL with CSP for multiclass BCI, ITAL with FBCSP reaches the benchmark performance for six out of nine subjects using average 42% of the full training set which is significant at 5% (p < 0.05) significance level. For multiclass BCI as well, ITAL combined with discriminating feature extraction ensures better transfer which yields to effective reduction of the training session without sacrificing the benchmark robustness.