Spatio-Temporal Progressive Attention Model for EEG Classification in Rapid Serial Visual Presentation Task
Journal:
arXiv
Published Date:
Feb 2, 2025
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.
