Despite many deep learning models are proposed for content understanding or pattern recognition of brain activities via EEGs, EEG-based object classification still demands efforts for the improvement with respect to its accuracy, generalization, and interpretation, yet some characters of human brains have been relatively unexplored. In this paper, we propose a regionlevel bi-directional deep learning framework for EEG-based object classification. Inspired by the hemispheric lateralization of human brain, we propose to extract additional information at regional level to strengthen and emphasize the differences between two hemispheres. The bi-directional long short-term memory is used to capture the dynamic correlations hidden from both the past and the future to the current state in EEG sequence. Extensive experiments are carried out and our results demonstrate the effectiveness of our proposed framework. Compared with the existing work, our framework achieves outstanding performances in EEG-based object classification task. |
