Abstract: A multibranch adaptive graph convolutional network is proposed for human action recognition by combining graph convolutional networks (GCNs), adaptive learning, and multibranch feature ...

Abstract: This paper investigates traffic data cognitive modelling problem in real traffic scene by fully utilizing multiscale spatio-temporal dependence between multiple traffic nodes, along with a ...

Electroencephalography (EEG) holds immense potential for decoding complex brain patterns associated with cognitive states and neurological conditions. In this paper, we propose an end-to-end framework ...

In the decoding process, the classifier is one of the key factors, and the graph information of the EEG was ignored by most researchers. In this paper, a graph convolutional network (GCN) based on ...