Lossless EEG Compression Algorithm Based on Semi-Supervised Learning for VLSI Implementation

Yi Hong Chen; Yan Ting Liu; Tsun Kuang Chi; Chiung An Chen; Yih Shyh Chiou; Ting Lan Lin; Shih Lun Chen

doi:10.1109/APCCAS50809.2020.9301714

Lossless EEG Compression Algorithm Based on Semi-Supervised Learning for VLSI Implementation

Yi Hong Chen
, Yan Ting Liu
, Tsun Kuang Chi
, Chiung An Chen
, Yih Shyh Chiou
, Ting Lan Lin
, Shih Lun Chen

Department of Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

In this paper, a hardware-oriented lossless EEG compression algorithm including a two-stage prediction, voting prediction and tri-entropy coding is proposed. In two stages prediction, 27 conditions and 6 functions are used to decide how to predict the current data from previous data. Then, voting prediction finds optimal function according to 27 conditions for best function to produce best Error (the difference of predicted data and current data). Moreover, a tri-entropy coding technique is developed based on normal distribution. The two-stage Huffman coding and Golomb-Rice coding was used to generate the binary code of Error value. In CHB-MIT Scalp EEG Database, the novel EEG compression algorithm achieves average compression rate to 2.37. The proposed hardware-oriented algorithm is suitable for VLSI implementation due to its low complexity.

Original language	English
Title of host publication	Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020
Editors	Xuan-Tu Tran, Duy-Hieu Bui
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	217-219
Number of pages	3
ISBN (Electronic)	9781728193960
DOIs	https://doi.org/10.1109/APCCAS50809.2020.9301714
State	Published - 8 Dec 2020
Event	16th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020 - Virtual, Halong, Viet Nam Duration: 8 Dec 2020 → 10 Dec 2020

Publication series

Name	Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020

Conference

Conference	16th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020
Country/Territory	Viet Nam
City	Virtual, Halong
Period	8/12/20 → 10/12/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Dynamic voting
EEG
WBSN
fuzzy decision
lossless
machine learning
tri-entropy coding

Access to Document

10.1109/APCCAS50809.2020.9301714

Cite this

Chen, Y. H., Liu, Y. T., Chi, T. K., Chen, C. A., Chiou, Y. S., Lin, T. L., & Chen, S. L. (2020). Lossless EEG Compression Algorithm Based on Semi-Supervised Learning for VLSI Implementation. In X.-T. Tran, & D.-H. Bui (Eds.), Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020 (pp. 217-219). Article 9301714 (Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APCCAS50809.2020.9301714

Chen, Yi Hong ; Liu, Yan Ting ; Chi, Tsun Kuang et al. / Lossless EEG Compression Algorithm Based on Semi-Supervised Learning for VLSI Implementation. Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020. editor / Xuan-Tu Tran ; Duy-Hieu Bui. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 217-219 (Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020).

@inproceedings{b062a68928974ab2a364a0b1427d02a4,

title = "Lossless EEG Compression Algorithm Based on Semi-Supervised Learning for VLSI Implementation",

abstract = "In this paper, a hardware-oriented lossless EEG compression algorithm including a two-stage prediction, voting prediction and tri-entropy coding is proposed. In two stages prediction, 27 conditions and 6 functions are used to decide how to predict the current data from previous data. Then, voting prediction finds optimal function according to 27 conditions for best function to produce best Error (the difference of predicted data and current data). Moreover, a tri-entropy coding technique is developed based on normal distribution. The two-stage Huffman coding and Golomb-Rice coding was used to generate the binary code of Error value. In CHB-MIT Scalp EEG Database, the novel EEG compression algorithm achieves average compression rate to 2.37. The proposed hardware-oriented algorithm is suitable for VLSI implementation due to its low complexity.",

keywords = "Dynamic voting, EEG, WBSN, fuzzy decision, lossless, machine learning, tri-entropy coding",

author = "Chen, \{Yi Hong\} and Liu, \{Yan Ting\} and Chi, \{Tsun Kuang\} and Chen, \{Chiung An\} and Chiou, \{Yih Shyh\} and Lin, \{Ting Lan\} and Chen, \{Shih Lun\}",

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Chen, YH, Liu, YT, Chi, TK, Chen, CA, Chiou, YS, Lin, TL & Chen, SL 2020, Lossless EEG Compression Algorithm Based on Semi-Supervised Learning for VLSI Implementation. in X-T Tran & D-H Bui (eds), Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020., 9301714, Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020, Institute of Electrical and Electronics Engineers Inc., pp. 217-219, 16th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020, Virtual, Halong, Viet Nam, 8/12/20. https://doi.org/10.1109/APCCAS50809.2020.9301714

Lossless EEG Compression Algorithm Based on Semi-Supervised Learning for VLSI Implementation. / Chen, Yi Hong; Liu, Yan Ting; Chi, Tsun Kuang et al.
Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020. ed. / Xuan-Tu Tran; Duy-Hieu Bui. Institute of Electrical and Electronics Engineers Inc., 2020. p. 217-219 9301714 (Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Lin, Ting Lan

AU - Chen, Shih Lun

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AB - In this paper, a hardware-oriented lossless EEG compression algorithm including a two-stage prediction, voting prediction and tri-entropy coding is proposed. In two stages prediction, 27 conditions and 6 functions are used to decide how to predict the current data from previous data. Then, voting prediction finds optimal function according to 27 conditions for best function to produce best Error (the difference of predicted data and current data). Moreover, a tri-entropy coding technique is developed based on normal distribution. The two-stage Huffman coding and Golomb-Rice coding was used to generate the binary code of Error value. In CHB-MIT Scalp EEG Database, the novel EEG compression algorithm achieves average compression rate to 2.37. The proposed hardware-oriented algorithm is suitable for VLSI implementation due to its low complexity.

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Chen YH, Liu YT, Chi TK, Chen CA, Chiou YS, Lin TL et al. Lossless EEG Compression Algorithm Based on Semi-Supervised Learning for VLSI Implementation. In Tran XT, Bui DH, editors, Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 217-219. 9301714. (Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020). doi: 10.1109/APCCAS50809.2020.9301714

Lossless EEG Compression Algorithm Based on Semi-Supervised Learning for VLSI Implementation

Abstract

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Keywords

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