A new perspective of noise removal from EEG

Junhua Li, Chao Li, Nitish Thakor, Andrzej Cichocki, Anastasios Bezerianos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


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.

Original languageEnglish (US)
Title of host publication8th International IEEE EMBS Conference on Neural Engineering, NER 2017
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9781538619162
StatePublished - Aug 10 2017
Externally publishedYes
Event8th International IEEE EMBS Conference on Neural Engineering, NER 2017 - Shanghai, China
Duration: May 25 2017May 28 2017

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554


Other8th International IEEE EMBS Conference on Neural Engineering, NER 2017

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering


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