A Temporal Convolution Network Solution for EEG Motor Imagery Classification EEG motor imagery recognition based brain computer interface has been an import scheme to construct an alternative pathway of the brain to the outside world. EEG signal is usually buried in noise and has very low signal to noise ratio (SNR), which has presented great challenge for efficient motor imagery classification. In addition, the large intrasubject and intersubject signal variance toward one specific motor imagery also brings difficulty for accurate classification. In recent years, some deep learning solutions based on AutoEncoder, Restricted Boltzmann Machine, CNN and RNN have been proposed for EEG motor imagery classification which have well improved the motor imagery classification accuracy. However, the multisubject and multitask motor imagery classification problem remains a challenge. The high computational cost of the existing deep learning solutions is another serious issue to be addressed. In this paper, a new motor imagery classification solution based on Temporal Convolutional Network (TCN) is developed. The dilated causal convolution within TCN could well incorporate the temporal information in a parallel way with much higher computational efficiency than the traditional RNNs. Time stacked spatial EEG signal has been employed as the input to the TCN. Based on which, both the spatial distribution information and temporal variation of the brain signal have been considered. Extensive experiments have shown that the proposed TCN solution has obtained state of the art performance on multisubject and multitask motor imagery classification. A high classification accuracy as 97.89% on 20 subjects and 5 tasks has been reached.

The Mechanism of a MultiBranch Structure for EEGBased Motor Imagery Classification In the field of motor imagery (MI) classification, it is a great challenge to preserve temporal features and spatial ones of the electroencephalogram (EEG) data simultaneously for effective processing. In our former study, a novel framework for MI classification has been proposed, which represents the EEG data into a 3D form and handles it with a multibranch 3D convolutional neural network (3D CNN). The 3D CNN is constructed with three different branches and already proved to be better than one branch network. In this paper, to study the mechanism of the multibranch structure, we modify the framework of the multibranch network with three same networks as the branch and compare different multibranch networks with the corresponding one branch network to find whether multibranch structure is better than one branch structure in MI classification tasks. Experiment results revealed that the modified multibranch 3D CNN can also reach stateoftheart classification kappa value level and performs better than other methods in terms of standard deviation including the original network with three different branches. The good performance of the modified network may reflect multibranch structure is better than one branch structure and shows its application value in EEG classification.