The Detection of Attentive Mental State Using a Mixed Neural Network Model

The application of deep learning (DL) in various brain computer interface (BCI) systems has achieved great success, but the results on the attention classification task are still not satisfactory. In this paper, an end-to-end mixed neural network model was proposed to classify the attention and non- attention mental states from multi-channel electroencephalography (EEG) data. During the experiment, a cross-subject strategy was performed on the attention detection task. Evaluated on a different electrodes combination of a publicly available dataset, the proposed model outperforms these baseline methods while maintaining relatively low computational complexity. The improved performance is meaningful for the attentive mental state classification task and is useful for the process of attention enhancement.