Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI.

Objective: Despite the effective application of deep learning (DL) in brain-computer interface (BCI) systems, the successful execution of this technique, especially for inter-subject classification, in cognitive BCI has not been accomplished yet. In this paper, we propose a framework based on the deep convolutional neural network (CNN) to detect the attentive mental state from single-channel raw electroencephalography (EEG) data.

Approach: We develop an end-to-end deep CNN to decode the attentional information from an EEG time series.