TY - GEN
T1 - Wavelet-based denoising for EEG-based pattern recognition systems
AU - Nguyen, Binh
AU - Ma, Wanli
AU - Tran, Dat
AU - Chung, Younjin
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/1
Y1 - 2020/12/1
N2 - Electroencephalogram (EEG) has been widely studied for EEG-based pattern recognition systems such as seizure, sleep stage, emotion, alcoholics and person recognitions. However, EEG signals are subject to noise and artifacts, which negatively affects to the pattern recognition systems. Hence, an effective EEG denoising technique is becoming necessary. In this paper, we propose an EEG denoising technique in which noisy signals are decomposed by a Wavelet transform operation, followed by Thresholding component using Energy Packing Efficiency, before being reconstructed to obtain the clean signals. The experiments are conducted on two EEG public datasets and the results show that our proposed technique achieves good performance on denoising EEG signals and improves EEG-based pattern recognition systems the most.
AB - Electroencephalogram (EEG) has been widely studied for EEG-based pattern recognition systems such as seizure, sleep stage, emotion, alcoholics and person recognitions. However, EEG signals are subject to noise and artifacts, which negatively affects to the pattern recognition systems. Hence, an effective EEG denoising technique is becoming necessary. In this paper, we propose an EEG denoising technique in which noisy signals are decomposed by a Wavelet transform operation, followed by Thresholding component using Energy Packing Efficiency, before being reconstructed to obtain the clean signals. The experiments are conducted on two EEG public datasets and the results show that our proposed technique achieves good performance on denoising EEG signals and improves EEG-based pattern recognition systems the most.
KW - EEG
KW - EEG-based pattern recognition systems
KW - Energy Packing Efficiency
KW - Wavelet-based denoising
UR - http://www.scopus.com/inward/record.url?scp=85099683352&partnerID=8YFLogxK
UR - http://www.ieeessci2020.org/
U2 - 10.1109/SSCI47803.2020.9308421
DO - 10.1109/SSCI47803.2020.9308421
M3 - Conference contribution
AN - SCOPUS:85099683352
SN - 9781728125480
T3 - 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
SP - 1249
EP - 1256
BT - 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
A2 - Abbass, Hussein
A2 - Coello Coello, Carlos A.
A2 - Singh, Hemant Kumar
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - United States
T2 - 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
Y2 - 1 December 2020 through 4 December 2020
ER -