An adaptive session-incremental broad learning system for continuous motor imagery EEG classification.

Abstract

Motor imagery electroencephalography (MI-EEG) is usually used as a driving signal in neuro-rehabilitation systems, and its feature space varies with the recovery progress. It is required to endow the recognition model with continuous learning and self-updating capability. Broad learning system (BLS) can be remodeled in an efficient incremental learning way. However, its architecture is intractable to change automatically to adapt to new incoming MI-EEG with time-varying and complex temporal-spatial characteristics. In this paper, an adaptive session-incremental BLS (ASiBLS) is proposed based on mutual information theory and BLS. For the initial session data, a compact temporal-spatial feature extractor (CTS) is designed to acquire the temporal-spatial features, which are input to a baseline BLS (bBLS). Furthermore, for new session data, a mutual information maximization constraint (MIMC) is introduced into the loss function of CTS to make the features' probability distribution sufficiently similar to that of the previous session, a new incremental BLS sequence (iBLS) is obtained by adding a small number of nodes to the previous model, and so on. Experiments are conducted based on the BCI Competition IV-2a dataset with two sessions and IV-2b dataset with five sessions, ASiBLS achieves average decoding accuracies of 79.89% and 87.04%, respectively. The kappa coefficient and forgetting rate are also used to evaluate the model performance. The results show that ASiBLS can adaptively generate an optimized and reduced model for each session successively, which has better plasticity in learning new knowledge and stability in retaining old knowledge as well.