TFANet: A Time-Frequency Aware Network With Joint Entropy Coding for High-Ratio EEG Compression

The transmission and storage of large-scale EEG data require high-ratio EEG compression. However, existing EEG compression methods struggle to achieve high compression efficiency while preserving reconstruction quality due to statistical redundancy and the loss of high-frequency information at extreme compression ratios.

To address these limitations, we propose TFANet, a novel high-ratio EEG compression framework that integrates autoencoder learning with entropy coding to optimize the latent space distribution, effectively reducing redundancy and maximizing compression efficiency. To address the issue of high-frequency information loss in existing methods, which leads to significant detail degradation at high compression ratios, we propose the frequency attention block (FAB) and the time-frequency enhancement block (TFEB). FAB leverages fast fourier transform for global frequency-aware compression, while TFEB integrates discrete wavelet transform with channel attention to preserve fine-grained time-frequency features. By utilizing global frequency awareness to guide local feature extraction, our approach ensures more effective retention of critical EEG details.

Experiments on public EEG datasets show that TFANet achieves an unprecedented 333× compression ratio while maintaining superior reconstruction quality, significantly outperforming existing methods.

These results highlight TFANet's potential for large-scale EEG applications, enabling efficient data transmission and storage while preserving critical neural information.

TFANet reduces the storage and transmission costs of large-scale EEG data, laying the foundation for its practical applications in medical diagnosis and remote monitoring.