TY - GEN
T1 - A new perspective of noise removal from EEG
AU - Li, Junhua
AU - Li, Chao
AU - Thakor, Nitish
AU - Cichocki, Andrzej
AU - Bezerianos, Anastasios
N1 - Funding Information:
*This work was supported by the Ministry of Education of Singapore under the grant MOE2014-T2-1-115. The authors also thank the National University of Singapore for supporting the Cognitive Engineering Group at the Singapore Institute for Neurotechnology (SINAPSE) under grant R-719-001-102-232.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/10
Y1 - 2017/8/10
N2 - Denoising, noise or interferences are removed from recorded signal to enhance the signal-to-noise ratio (SNR), is a crucial and ubiquitous step in the procedure of signal processing, especially for neurophysiological signal. This step facilitates following processing, such as feature extraction, classification, and data analyses. Conventional methods are based on the principle of separating noise components from the recorded signal and removing them, but these methods do not remove noise completely. In particular, conventional methods seems powerless to eliminate irregular and occasional noise bursts, which are caused by transient electrode contacting problem, head movements, or unpredictable factors. In this paper, we tackled the problem of noise removal from a new perspective, which is opposite to the conventional methods. Data portions that are contaminated by noise are entirely removed and then restored according to their relationships with the remaining signal. The rationale of this procedure is to purify the signal through addition rather than deduction that is normally executed in conventional methods. The results of both synthetic data and real EEG demonstrated that our idea is feasible and provides a new promising manner for noise removal.
AB - Denoising, noise or interferences are removed from recorded signal to enhance the signal-to-noise ratio (SNR), is a crucial and ubiquitous step in the procedure of signal processing, especially for neurophysiological signal. This step facilitates following processing, such as feature extraction, classification, and data analyses. Conventional methods are based on the principle of separating noise components from the recorded signal and removing them, but these methods do not remove noise completely. In particular, conventional methods seems powerless to eliminate irregular and occasional noise bursts, which are caused by transient electrode contacting problem, head movements, or unpredictable factors. In this paper, we tackled the problem of noise removal from a new perspective, which is opposite to the conventional methods. Data portions that are contaminated by noise are entirely removed and then restored according to their relationships with the remaining signal. The rationale of this procedure is to purify the signal through addition rather than deduction that is normally executed in conventional methods. The results of both synthetic data and real EEG demonstrated that our idea is feasible and provides a new promising manner for noise removal.
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U2 - 10.1109/NER.2017.8008399
DO - 10.1109/NER.2017.8008399
M3 - Conference contribution
AN - SCOPUS:85028620086
T3 - International IEEE/EMBS Conference on Neural Engineering, NER
SP - 501
EP - 504
BT - 8th International IEEE EMBS Conference on Neural Engineering, NER 2017
PB - IEEE Computer Society
T2 - 8th International IEEE EMBS Conference on Neural Engineering, NER 2017
Y2 - 25 May 2017 through 28 May 2017
ER -