Assembling global and local spatial-temporal filters to extract discriminant information of EEG in RSVP task.

Brain-computer interface (BCI) system has developed rapidly in the past decade. And rapid serial visual presentation (RSVP) is an important BCI paradigm to detect the targets in high-speed image streams. For decoding electroencephalography (EEG) in RSVP task, the ensemble-model methods have better performance than the single-model ones.This study proposed a method based on ensemble learning to extract discriminant information of EEG. An extreme gradient boosting framework was utilized to sequentially generate the sub models, including one global spatial-temporal filter and a group of local ones. EEG was reshaped into a three-dimensional form by remapping the electrode dimension into a 2D array to learn the spatial-temporal features from real local space.A benchmark RSVP EEG dataset was utilized to evaluate the performance of the proposed method, where EEG data of 63 subjects were analyzed. Compared with several state-of-the-art methods, the spatial-temporal patterns of proposed method were more consistent with P300, and the proposed method can provide significantly better classification performance.The ensemble model in this study was end-to-end optimized, which can avoid error accumulation. The sub models optimized by gradient boosting theory can extract discriminant information complementarily and non-redundantly.