Learning EEG topographical representation for classification via convolutional neural network

Electroencephalography (EEG) topographical representation (ETR) can monitor regional brain activities and is emerging as a successful technique for causally exploring cortical mechanisms and connections. However, it is a challenge to find a robust method supporting high-dimensional EEG data with low signal-to-noise ratios from multiple objects and multiple channels. To address this issue, a new ETR energy calculation method for learning the EEG patterns of brain activities using a convolutional neural network is reported. It is able to customize temporal ETR training and recognize multiple objects within a common learning model. Specifically, an open-access dataset from the 2008 Brain-Computer Interface (BCI) Competition IV-2a is used for classification of five classes containing four Motor Imagery actions and one relax action. The proposed classification framework outperforms the best state-of-the-art classification method by 10.11% in average subject accuracy. Furthermore, by studying the ETR parameter optimization, a user interface for BCI applications is obtained and a real-time method implemented. (C) 2020 Elsevier Ltd. All rights reserved.