Wavelet transform and adaptive arithmetic coding techniques for EEG lossy compression

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

Research output: A Conference proceeding or a Chapter in BookConference contribution

8 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 languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks (IJCNN 2017) - Proceedings
Place of PublicationAnchorage, USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages3153-3160
Number of pages8
ISBN (Electronic)9781509061822
ISBN (Print)9781509061839
DOIs
Publication statusPublished - 14 May 2017
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: 14 May 201719 May 2017

Conference

Conference2017 International Joint Conference on Neural Networks, IJCNN 2017
CountryUnited States
CityAnchorage
Period14/05/1719/05/17

Fingerprint

Electroencephalography
Wavelet transforms
Brain computer interface
Security systems
Authentication
Brain
Processing
Experiments

Cite this

NGUYEN, T. B., 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 (pp. 3153-3160). [7966249] Anchorage, USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2017.7966249
NGUYEN, The Binh ; Nguyen, Dang ; Ma, Wanli ; Tran, Dat. / Wavelet transform and adaptive arithmetic coding techniques for EEG lossy compression. 2017 International Joint Conference on Neural Networks (IJCNN 2017) - Proceedings. Anchorage, USA : IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 3153-3160
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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, IJCNN 2017, 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; Tran, Dat.

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 BookConference contribution

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AU - NGUYEN, The Binh

AU - Nguyen, Dang

AU - Ma, Wanli

AU - Tran, Dat

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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.

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