Decoding Working Memory Representation Based on single-trial EEG By a Deep Learning Framework

Although current research has made significant progress in decoding working memory (WM) representation information, this area still faces several limitations that cannot be ignored. These limitations mainly include a lack of understanding of the dynamic neural mechanisms of WM and the limited feature extraction capability that traditional machine learning encounters when dealing with nonlinear temporal data. Inspired by the deep learning model - attention-based temporal convolutional networks (ATCNet), the decoding of representation information for WM has seen a new light. The ATCNet, through its unique attention mechanism and temporal convolutional networks (TCN) structure, demonstrates potential advantages in processing nonlinear temporal data and capturing dynamic patterns. In this deep learning framework, we successfully decoded the orientation information during a WM task based on single-trial electroencephalogram (EEG) with the average accuracy of 85.81%. The findings demonstrate the potential applications of ATCNet in decoding WM and suggest that EEG signals carrying task-related information can be effectively utilized for robustly decoding memory representations.