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

Objective: 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 informati...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering Vol. PP; pp. 1 - 11
Main Authors: Wang, Xiangcun, Wu, Xi, Li, Yuan, Wu, Xia, Zhang, Jiacai
Format: Journal Article
Language:English
Published: United States IEEE 17.07.2025
Subjects:
Arithmetic
Autoencoders
Brain modeling
Data mining
EEG
Electroencephalography
Entropy coding
ERP
Feature extraction
Frequency Attention
High-Ratio Compression
Image coding
Redundancy
Time-frequency analysis
ISSN:0018-9294, 1558-2531, 1558-2531
Online Access:Get full text
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Summary:Objective: 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. Methods: 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. Results: Experiments on public EEG datasets show that TFANet achieves an unprecedented 333× compression ratio while maintaining superior reconstruction quality, significantly outperforming existing methods. Conclusion: These results highlight TFANet's potential for large-scale EEG applications, enabling efficient data transmission and storage while preserving critical neural information. Significance: 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.
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ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2025.3590270