Wavelet transform and adaptive arithmetic coding techniques for EEG lossy compression

The Binh NGUYEN; Dang Nguyen; Wanli Ma; Dat Tran

doi:10.1109/IJCNN.2017.7966249

Wavelet transform and adaptive arithmetic coding techniques for EEG lossy compression

The Binh NGUYEN, Dang Nguyen, Wanli Ma, Dat Tran

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

15 Citations (Scopus)

Abstract

Electroencephalogram (EEG) has been widely used in diagnosing brain-related diseases, brain-computer interface applications, and user authentication and identification in security systems. Large EEG databases have been built and therefore, an effective EEG compression technique is necessary to reduce data for transmitting, processing and storing. In this paper, we propose an EEG lossy compression scheme in which EEG signals are undergoing a Wavelet Transform operation, followed by Quantisation and Thresholding, before being coded by Adaptive Arithmetic Coder. Our experiments are performed on a large set of EEG signals taken from two public databases and the results show that the proposed compression technique gives better performance than current techniques.

Original language	English
Title of host publication	2017 International Joint Conference on Neural Networks (IJCNN 2017) - Proceedings
Place of Publication	Anchorage, USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	3153-3160
Number of pages	8
ISBN (Electronic)	9781509061822
ISBN (Print)	9781509061839
DOIs	https://doi.org/10.1109/IJCNN.2017.7966249
Publication status	Published - 14 May 2017
Event	2017 International Joint Conference on Neural Networks - Anchorage, United States Duration: 14 May 2017 → 19 May 2017

Conference

Conference	2017 International Joint Conference on Neural Networks
Abbreviated title	IJCNN 2017
Country/Territory	United States
City	Anchorage
Period	14/05/17 → 19/05/17

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10.1109/IJCNN.2017.7966249

Cite this

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title = "Wavelet transform and adaptive arithmetic coding techniques for EEG lossy compression",

abstract = "Electroencephalogram (EEG) has been widely used in diagnosing brain-related diseases, brain-computer interface applications, and user authentication and identification in security systems. Large EEG databases have been built and therefore, an effective EEG compression technique is necessary to reduce data for transmitting, processing and storing. In this paper, we propose an EEG lossy compression scheme in which EEG signals are undergoing a Wavelet Transform operation, followed by Quantisation and Thresholding, before being coded by Adaptive Arithmetic Coder. Our experiments are performed on a large set of EEG signals taken from two public databases and the results show that the proposed compression technique gives better performance than current techniques.",

keywords = "Adaptive Arithmetic Coder, Discrete Wavelet Transform, EEG lossy compression, Quantization",

author = "NGUYEN, {The Binh} and Dang Nguyen and Wanli Ma and Dat Tran",

year = "2017",

month = may,

day = "14",

doi = "10.1109/IJCNN.2017.7966249",

language = "English",

isbn = "9781509061839",

pages = "3153--3160",

booktitle = "2017 International Joint Conference on Neural Networks (IJCNN 2017) - Proceedings",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2017 International Joint Conference on Neural Networks, IJCNN 2017 ; Conference date: 14-05-2017 Through 19-05-2017",

}

NGUYEN, TB, Nguyen, D, Ma, W & Tran, D 2017, Wavelet transform and adaptive arithmetic coding techniques for EEG lossy compression. in 2017 International Joint Conference on Neural Networks (IJCNN 2017) - Proceedings., 7966249, IEEE, Institute of Electrical and Electronics Engineers, Anchorage, USA, pp. 3153-3160, 2017 International Joint Conference on Neural Networks, Anchorage, United States, 14/05/17. https://doi.org/10.1109/IJCNN.2017.7966249

Wavelet transform and adaptive arithmetic coding techniques for EEG lossy compression. / NGUYEN, The Binh; Nguyen, Dang; Ma, Wanli et al.
2017 International Joint Conference on Neural Networks (IJCNN 2017) - Proceedings. Anchorage, USA: IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 3153-3160 7966249.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - Wavelet transform and adaptive arithmetic coding techniques for EEG lossy compression

AU - NGUYEN, The Binh

AU - Nguyen, Dang

AU - Ma, Wanli

AU - Tran, Dat

PY - 2017/5/14

Y1 - 2017/5/14

N2 - Electroencephalogram (EEG) has been widely used in diagnosing brain-related diseases, brain-computer interface applications, and user authentication and identification in security systems. Large EEG databases have been built and therefore, an effective EEG compression technique is necessary to reduce data for transmitting, processing and storing. In this paper, we propose an EEG lossy compression scheme in which EEG signals are undergoing a Wavelet Transform operation, followed by Quantisation and Thresholding, before being coded by Adaptive Arithmetic Coder. Our experiments are performed on a large set of EEG signals taken from two public databases and the results show that the proposed compression technique gives better performance than current techniques.

AB - Electroencephalogram (EEG) has been widely used in diagnosing brain-related diseases, brain-computer interface applications, and user authentication and identification in security systems. Large EEG databases have been built and therefore, an effective EEG compression technique is necessary to reduce data for transmitting, processing and storing. In this paper, we propose an EEG lossy compression scheme in which EEG signals are undergoing a Wavelet Transform operation, followed by Quantisation and Thresholding, before being coded by Adaptive Arithmetic Coder. Our experiments are performed on a large set of EEG signals taken from two public databases and the results show that the proposed compression technique gives better performance than current techniques.

KW - Adaptive Arithmetic Coder

KW - Discrete Wavelet Transform

KW - EEG lossy compression

KW - Quantization

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U2 - 10.1109/IJCNN.2017.7966249

DO - 10.1109/IJCNN.2017.7966249

M3 - Conference contribution

AN - SCOPUS:85031000895

SN - 9781509061839

SP - 3153

EP - 3160

BT - 2017 International Joint Conference on Neural Networks (IJCNN 2017) - Proceedings

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Anchorage, USA

T2 - 2017 International Joint Conference on Neural Networks

Y2 - 14 May 2017 through 19 May 2017

ER -

Wavelet transform and adaptive arithmetic coding techniques for EEG lossy compression

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