Spatial–temporal attention enhanced high-ratio EEG compression using deep variational autoencoder and arithmetic coding

Efficient storage and transmission of EEG data require high-ratio EEG compression. However, due to their limited capacity in removing statistical redundancy and insufficient focus on complex signal regions, existing convolution-based compression methods struggle to achieve a high compression ratio without sacrificing reconstruction quality. In addition, these methods are constrained by fixed network architectures, lacking the flexibility to adjust compression rates according to application-specific requirements. To address these challenges, we propose a hybrid compression framework that combines variational autoencoder with arithmetic coding. By jointly training, the EEG data are mapped to a more compressible probability distribution, which guides the entropy coding process and substantially enhances the overall compression ratio. In addition, we design a spatial–temporal attention module tailored to EEG characteristics. This module separately extracts and integrates temporal and spatial attention, allowing the network to focus more precisely on complex and informative regions of the signal. Furthermore, by jointly constraining both compression rate and reconstruction error, the proposed method supports adjustable compression ratios. Results on public datasets show that the proposed method maintains the best reconstruction performance even with a compression ratio far exceeding existing methods (100 vs.16).