Graph-Based EEG Signal Compression for Human–Machine Interaction
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 Cos...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10375318/ |
| _version_ | 1797365959785185280 |
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| author | Takuya Fujihashi Toshiaki Koike-Akino |
| author_facet | Takuya Fujihashi Toshiaki Koike-Akino |
| author_sort | Takuya Fujihashi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T16:57:23Z |
| format | Article |
| id | doaj.art-f2a5e1fee1554e0dbd5f4ae936f9fcc2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T16:57:23Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f2a5e1fee1554e0dbd5f4ae936f9fcc22024-01-05T00:03:54ZengIEEEIEEE Access2169-35362024-01-01121163117110.1109/ACCESS.2023.334759210375318Graph-Based EEG Signal Compression for Human–Machine InteractionTakuya Fujihashi0https://orcid.org/0000-0002-6960-0122Toshiaki Koike-Akino1https://orcid.org/0000-0002-2578-5372Graduate School of Information and Science, Osaka University, Osaka, JapanMitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USACommunication 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.https://ieeexplore.ieee.org/document/10375318/EEGgraph signal processingparameterized graph shift operator |
| spellingShingle | Takuya Fujihashi Toshiaki Koike-Akino Graph-Based EEG Signal Compression for Human–Machine Interaction IEEE Access EEG graph signal processing parameterized graph shift operator |
| title | Graph-Based EEG Signal Compression for Human–Machine Interaction |
| title_full | Graph-Based EEG Signal Compression for Human–Machine Interaction |
| title_fullStr | Graph-Based EEG Signal Compression for Human–Machine Interaction |
| title_full_unstemmed | Graph-Based EEG Signal Compression for Human–Machine Interaction |
| title_short | Graph-Based EEG Signal Compression for Human–Machine Interaction |
| title_sort | graph based eeg signal compression for human x2013 machine interaction |
| topic | EEG graph signal processing parameterized graph shift operator |
| url | https://ieeexplore.ieee.org/document/10375318/ |
| work_keys_str_mv | AT takuyafujihashi graphbasedeegsignalcompressionforhumanx2013machineinteraction AT toshiakikoikeakino graphbasedeegsignalcompressionforhumanx2013machineinteraction |