Learning EEG topographical representation for classification via convolutional neural network

Highlights：

• We define a unified time-frequency energy algorithm that makes ETR robust to classifying multiple objects. Compared with existing EEG topology generations, the proposed method can be accurate and functional for spatial location, temporal onset, and stability simultaneously.

• We propose the ETR data structure which not only reflects the intrinsic connection of brain activity status in EEG, but also performs appropriate data structure dimensional reduction on EEG feature values to reduce computational complexity.

• We propose a novel classifier that can accomplish multi-period and multi-object recognition. We extensively evaluate the common classifier on the dataset used in the 2008 BCI competition IV-2a in the machine learning network called ETRCNN. The method achieves state-of-the-art generalization performance in classification accuracy and kappa values.