Neurocomputing 00 (2011) 1–11
A subject transfer framework for EEG classi?cation
Wenting Tu, Shiliang Sun?
Department of Computer Science and Technology, East China Normal University 500 Dongchuan Road, Shanghai 200241, P. R. China
This paper proposes a subject transfer framework for EEG classi?cation. It aims to improve the classi?cation per- formance 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 classi?cation stage. At the feature extraction stage, we ?rst obtain a candidate ?lter set for each subject through a previously pro- posed feature extraction method. Then, we design different criterions to learn two sparse subsets of the candidate ?lter set, which are called the robust ?lter bank and adaptive ?lter bank, respectively. Given robust and adaptive ?lter banks, at the classi?cation step, we learn classi?ers corresponding to these ?lter banks and employ a two-level ensemble strategy to dynamically and locally combine their outcomes to reach a single decision output. The pro- posed framework, as validated by experimental results, can achieve positive knowledge transfer for improving the performance of EEG classi?cation.
EEG Classi?cation, Transfer Learning, Ensemble Learning, Sparse Representation
During recent years, the development of brain computer interface (BCI) technology has both theoretical and practical signi?cance. BCIs have the ability to enable their users to manipulate an external device by means of translating brain