| Summary: | Cooperative interactions among neural groups scattered in adjacent brain regions emerge in cognitive acts, which implies that local spatial relationships between EEG channels could benefit the classification for motor imagery (MI) tasks. Due to the lack of research exploring this issue, this paper propose a novel feature extraction method, termed channel binary pattern (CBP), to extract the local spatial information. CBP is used to discover local spatial patterns by binarizing the interaction of adjacent local regions. Besides, we further propose a new feature extraction method for the global spatial feature termed a combination of the multiclass spatial pattern (CMSP), which computes the maximal global variance for each class. Then, the fusion of local and global information is performed by the proposed algorithm of spatial information fusion based on electroencephalography (EEG-SIF), which is able to explore comprehensive spatial information for brain activity when MI occurs. An experimental study is implemented with BCI Competition IV Dataset 2a. And the superior classification result confirms the effectiveness of spatial information extraction in different scales (local and global space) and the feasibility of information fusion.
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