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A Bi-LSTM Based Network with Attention Mechanism for EEG Visual Classification

The classification of Electroencephalogram (EEG) signals is one of the important tasks in the EEG signal research. The classification of visual cognitive EEG signals has high research value, which can help the brain-computer interface to obtain visual cognitive abilities similar to humans. Most of the current EEG visual classification methods only focus on the static information, ignoring the dynamic characteristics of the EEG signals in time. It is difficult to establish a dependency on the long-term sequence of EEG signals due to distance limitations. Therefore, we propose a bi-directional long short-term memory (Bi-LSTM) method with global attention mechanism, which add a global attention mechanism and the Swish activation function to effectively improve the performance of the method. Compared with the baseline model and the state of the art methods, our model can achieve a classification accuracy of 94.15%, which proves that our method has superior performance of EEG visual classification.