A frequency-domain MIMO model with spatial and temporal regularization estimates the ECoG-to-EEG brain channel on simultaneous macaque recordings, claiming lower reconstruction error than LS and MMSE.
Noise removal methods on ambulatory EEG: A Survey
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abstract
Over many decades, research is being attempted for the removal of noise in the ambulatory EEG. In this respect, an enormous number of research papers is published for identification of noise removal, It is difficult to present a detailed review of all these literature. Therefore, in this paper, an attempt has been made to review the detection and removal of an noise. More than 100 research papers have been discussed to discern the techniques for detecting and removal the ambulatory EEG. Further, the literature survey shows that the pattern recognition required to detect ambulatory method, eye open and close, varies with different conditions of EEG datasets. This is mainly due to the fact that EEG detected under different conditions has different characteristics. This is, in turn, necessitates the identification of pattern recognition technique to effectively distinguish EEG noise data from a various condition of EEG data.
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eess.SP 1years
2025 1verdicts
REJECT 1representative citing papers
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Bridging BCI and Communications: A MIMO Framework for EEG-to-ECoG Wireless Channel Modeling
A frequency-domain MIMO model with spatial and temporal regularization estimates the ECoG-to-EEG brain channel on simultaneous macaque recordings, claiming lower reconstruction error than LS and MMSE.