Spatio-Temporal Progressive Attention Model for EEG Classification in Rapid Serial Visual Presentation Task

As a type of multi-dimensional sequential data, the spatial and temporal dependencies of electroencephalogram (EEG) signals should be further investigated. Thus, in this paper, we propose a novel spatial-temporal progressive attention model (STPAM) to improve EEG classification in rapid serial visual presentation (RSVP) tasks. STPAM employs a progressive approach using three sequential spatial experts to learn brain region topology and mitigate interference from irrelevant areas. Each expert refines EEG electrode selection, guiding subsequent experts to focus on significant spatial information, thus enhancing signals from key regions. Subsequently, based on the above spatially-enhanced features, three temporal experts progressively capture temporal dependencies by focusing attention on crucial EEG time slices. Except for the above EEG classification method, in this paper, we build a novel Infrared RSVP Dataset (IRED) which is based on dim infrared images with small targets for the first time, and conduct extensive experiments on it. Experimental results demonstrate that STPAM outperforms all baselines, achieving 2.02% and 1.17% on the public dataset and IRED dataset, respectively.