A subject transfer framework for EEG classification

This paper proposes a subject transfer framework for EEG classification. It aims to improve the classification performance when the training set of the target subject (namely user) is small owing to the need to reduce the calibration session. Our framework pursues improvement not only at the feature extraction stage, but also at the classification stage. At the feature extraction stage, we first obtain a candidate filter set for each subject through a previously proposed feature extraction method. Then, we design different criterions to learn two sparse subsets of the candidate filter set, which are called the robust filter bank and adaptive filter bank, respectively. Given robust and adaptive filter banks, at the classification step, we learn classifiers corresponding to these filter banks and employ a two-level ensemble strategy to dynamically and locally combine their outcomes to reach a single decision output. The proposed framework, as validated by experimental results, can achieve positive knowledge transfer for improving the performance of EEG classification.