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

電子工程系

研究成果: 書籍/報告/會議論文中的章節 › 會議投稿 › 同行評審

3 引文斯高帕斯（Scopus）

摘要

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.

原文	English
主出版物標題	Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020
編輯	Xuan-Tu Tran, Duy-Hieu Bui
發行者	Institute of Electrical and Electronics Engineers Inc.
頁面	217-219
頁數	3
ISBN（電子）	9781728193960
DOIs	https://doi.org/10.1109/APCCAS50809.2020.9301714
出版狀態	Published - 8 12月 2020
事件	16th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020 - Virtual, Halong, Viet Nam 持續時間: 8 12月 2020 → 10 12月 2020

出版系列

名字	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
國家/地區	Viet Nam
城市	Virtual, Halong
期間	8/12/20 → 10/12/20

文獻附註

Publisher Copyright:
© 2020 IEEE.

存取文件

10.1109/APCCAS50809.2020.9301714

其它文件與鏈接

Link to publication in Scopus

引用此

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. 於 X.-T. Tran, & D.-H. Bui (編輯), Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020 (頁 217-219). 文章 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 等. / 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. 編輯 / Xuan-Tu Tran ; Duy-Hieu Bui. Institute of Electrical and Electronics Engineers Inc., 2020. 頁 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\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 16th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020 ; Conference date: 08-12-2020 Through 10-12-2020",

year = "2020",

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series = "Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020",

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booktitle = "Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020",

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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. 於 X-T Tran & D-H Bui (編輯), 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., 頁 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 等.
Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020. 編輯 / 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).

研究成果: 書籍/報告/會議論文中的章節 › 會議投稿 › 同行評審

TY - GEN

T1 - Lossless EEG Compression Algorithm Based on Semi-Supervised Learning for VLSI Implementation

AU - Chen, Yi Hong

AU - Liu, Yan Ting

AU - Chi, Tsun Kuang

AU - Chen, Chiung An

AU - Chiou, Yih Shyh

AU - Lin, Ting Lan

AU - Chen, Shih Lun

PY - 2020/12/8

Y1 - 2020/12/8

N2 - 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.

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.

KW - Dynamic voting

KW - EEG

KW - WBSN

KW - fuzzy decision

KW - lossless

KW - machine learning

KW - tri-entropy coding

UR - https://www.scopus.com/pages/publications/85099559965

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DO - 10.1109/APCCAS50809.2020.9301714

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AN - SCOPUS:85099559965

T3 - Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020

SP - 217

EP - 219

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

A2 - Tran, Xuan-Tu

A2 - Bui, Duy-Hieu

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 16th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020

Y2 - 8 December 2020 through 10 December 2020

ER -

Chen YH, Liu YT, Chi TK, Chen CA, Chiou YS, Lin TL 等. Lossless EEG Compression Algorithm Based on Semi-Supervised Learning for VLSI Implementation. 於 Tran XT, Bui DH, 編輯, 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

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