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

TL;DR

This paper introduces a novel spatio-temporal attention model (STPAM) for EEG classification in RSVP tasks, utilizing progressive attention mechanisms to focus on significant brain regions and time slices, and also presents a new infrared EEG dataset.

Contribution

The paper proposes a novel spatial-temporal progressive attention model (STPAM) and introduces a new infrared EEG dataset for RSVP tasks, improving classification performance.

Findings

STPAM outperforms existing methods in EEG classification accuracy.

The model effectively identifies important brain regions and time slices.

The new infrared EEG dataset (IRED) enables better evaluation of EEG analysis methods.

Abstract

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 first adopts three distinct spatial experts to learn the spatial topological information of brain regions progressively, which is used to minimize the interference of irrelevant brain regions. Concretely, the former expert filters out EEG electrodes in the relative brain regions to be used as prior knowledge for the next expert, ensuring that the subsequent experts gradually focus their attention on information from significant EEG electrodes. This process strengthens the effect of the important brain regions. Then, based on the above-obtained feature sequence with spatial information, three temporal experts are adopted to capture the temporal dependence by progressively assigning attention to the crucial EEG slices. Except for the above EEG classification method, in this paper, we build a novel Infrared RSVP EEG Dataset (IRED) which is based on dim infrared images with small targets for the first time, and conduct extensive experiments on it. The results show that our STPAM can achieve better performance than all the compared methods.