A Boosting-Based Spatial-Spectral Model for Stroke Patients' EEG Analysis in Rehabilitation Training

Studies have shown that a motor imagery electro encephalogram (EEG)-based brain-computer interface (BCI) system can be used as a rehabilitation tool for stroke patients. Efficient classification of EEG from stroke patients is fundamental in the BCI-based stroke rehabilitation systems. One of the most successful algorithms for EEG classification is the common spatial patterns (CSP). However, studies have reported that the performance of CSP heavily relies on its operational frequency band and channels configuration. To the best of our knowledge, there is no agreed upon clinical conclusion about motor imagery patterns of stroke patients. In this case, it is not available to obtain the active channels and frequency bands related to brain activities of stroke patients beforehand. Hence, for using the CSP algorithm, we usually set a relatively broad frequency range and channels, or try to find subject-related frequency bands and channels. To address this problem, we propose an adaptive boosting algorithm to perform autonomous selection of key channels and frequency band. In the proposed method, the spatial-spectral configurations are divided into multiple preconditions, and a new heuristic supervisor of stochastic gradient boost strategy is utilized to train weak classifiers under these preconditions. Extensive experiment comparisons have been performed on three datasets including two benchmark datasets from the famous BCI competition III and BCI competition IV as well as one self-acquired dataset from stroke patients. Results show that our algorithm yields relatively higher classification accuracies compared with seven state-of-the-art approaches. In addition, the spatial patterns (spatial weights) and spectral patterns (bandpass filters) determined by the algorithm can also be used for further analysis of the data, e.g., for brain source localization and physiological knowledge exploration.