Key result
Graph-based EEG signal compression reduced the bit rate by at least 25% for the same reconstruction quality compared to existing DCT-based and DWT-based schemes.
Why the study?
Existing EEG signal compression methods rely on orthogonal transforms like DWT or DCT, creating a need to improve signal quality at a given rate for human-machine interaction.
Effect estimate: BD-rate -25.0% to -82.0%
A novel graph-based EEG signal compression scheme using GFT achieves superior reconstruction quality at lower bit rates compared to traditional DCT and DWT methods, facilitating efficient human-machine interaction.
Graph-based EEG compression may improve remote robot interaction quality; leaves open validation in clinical neurophysiology.
Communication of bioelectric signals, such as electroencephalography (EEG) signals, will be a key technology for smooth interaction between users and remote robots. The existing solutions use an orthogonal transform for EEG signal compression, such as Discrete Wavelet Transform (DWT) or Discrete Cosine Transform (DCT). This paper proposes a graph-based compression scheme for EEG signals to improve the quality at the given rate. The proposed scheme constructs a graph from the positions of the EEG sensors and adopts parameterized graph shift operators to obtain the graph basis functions for decorrelating the EEG signals. Graph Fourier Transform (GFT) based on the graph basis functions with the combination of quantization and entropy coding can send high quality EEG signals with fewer bits. Evaluations using the EEG signal dataset show that the proposed GFT-based compression can send better quality EEG signals than the existing DCT-based and DWT-based schemes at the same bit rates. In addition, an optimal parameter of the graph shift operator under the given rate is discussed to maximize the reconstruction quality of the graph-based scheme.
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Fujihashi et al. (2023) studied Healthy subjects (Motor-Imagery EEG dataset) (n=52). Graph-Based EEG Signal Compression (GFT) vs. 1D-DCT, 2D-DCT, and DWT-based compression schemes was evaluated on Bjøntegaard delta (BD)-rate for Normalized MSE (NMSE) between -67.0 dB and -80.0 dB (BD-rate -25.0% to -82.0%). Graph-based EEG signal compression reduced the bit rate by at least 25% for the same reconstruction quality compared to existing DCT-based and DWT-based schemes.
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