journal article

Journal Article

Text

IEEE Access. 2024, 12, p. 1163-1171

Graph-Based EEG Signal Compression for Human-Machine Interaction

Institute of Electrical and Electronics Engineers Inc.

English

Text

2024-01-04

https://doi.org/10.1109/ACCESS.2023.3347592

Fujihashi T., Koike-Akino T.. Graph-Based EEG Signal Compression for Human-Machine Interaction. IEEE Access 12, 1163 (2024); https://doi.org/10.1109/ACCESS.2023.3347592.

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.

This article is licensed under a Creative Commons Attribution 4.0 International License.

95677

Graph-Based EEG Signal Compression for Human-Machine Interaction

Institute of Electrical and Electronics Engineers Inc.

English

2024-01-04

IEEE Access

12

1163

1171

https://doi.org/10.1109/ACCESS.2023.3347592

https://hdl.handle.net/11094/95677