A CNNbased Approach for threeclass classification of motor imagery EEG data including xe2x80x98rest statexe2x80x99 in hybrid multiuser BCI Multiuser BCI (brain computer interface) refers to a kind of BCI in which there are several users participating in one task. Differently from P300 and SSVEP (steadystate visual evoked potential), MI (motor imagery)BCI does not rely on external stimulus, which is more widely used in the assistance of disabled people. The main problem of MIBCI is to achieve asynchronous control, which needs to improve the rest state recognition accuracy. We proposed a CNNbased approach for threeclass classification of motor imagery EEG data including rest state in hybrid multiuser BCI. Firstly, we designed a twouser hybrid MIBCI experimental paradigm and moreover proposed a CNNbased processing framework method which contains several strategies for interbrain phase locked value (PLV) features fusion. Results show that the alpha band shows the significantly better performance than other bands and the classification performance of multiuser MI is better than singleuser MI. Multiuser BCI is a potential way to enhance the performance of asynchronous MIBCI.

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.