A novel transformer for attention decoding using EEG - Lee, Joon Hei (Nanyang Technological University, 2024)
Electroencephalography (EEG) attention classification plays a crucial role in brain-computer interface (BCI) applications. This paper introduces EEG-PatchFormer, a novel deep learning model leveraging transformers to achieve superior EEG attention decoding. We posit that transformers’ strength in capturing long-range temporal dependencies, coupled with their recent success on spatial data, makes them ideally suited for processing EEG signals.