Electroencephalography recognition based on encephalic region and temporal sequence transformer

Abstract

Stereoscopic vision is the key to good motor control and accurate cognition, and its formation is closely related to brain control. The early methods of measuring stereoscopic vision rely on the subject’s judgment, which might be influenced by inadvertent misjudgments. To solve this problem, we collected the Electroencephalography (EEG) of subjects watching dynamic random dot stereogram for stereogram recognition. To analyze stereogram EEG signals, this paper proposed a transformer-based encephalic region temporal sequence analysis network. Inspired by the concept of brain regions, this network designs an encephalic region Transformer module to capture global spatial features in each brain region and among the whole brain regions. Based on the spatial features of electrodes in different brain regions, the global spatial dependence of all electrodes can be further obtained. Then, the temporal sequence Transformer module is adopted to learn the global temporal EEG features. Finally, we utilize the spatial-temporal multi-scale convolution module to extract advanced spatial and temporal fusion features for recognition. The simulation results on two public EEG datasets illustrate the excellent classification performance of the proposed model, which is better than 9 existing comparison models in EEG recognition.