Spatiotemporal Pooling on Appropriate Topological Maps Represented as Two-Dimensional Images for EEG Classification

arXiv:2403.04353v1 Announce Type: new
Abstract: Motor imagery classification based on electroencephalography (EEG) signals is one of the most important brain-computer interface applications, although it needs further improvement. Several methods have attempted to obtain useful information from EEG signals by using recent deep learning techniques such as transformers. To improve the classification accuracy, this study proposes a novel EEG-based motor imagery classification method with three key features: generation of a topological map represented as a two-dimensional image from EEG signals with …

abstract applications arxiv brain brain-computer interface classification computer cs.cv deep learning deep learning techniques eeg images improvement information maps pooling transformers type