Abstract
This paper outlines a method for automatic artefact removal from multichannel recordings of event-related potentials (ERPs). The proposed method is based on, firstly, separation of the ERP recordings into independent components using the method of temporal decorrelation source separation (TDSEP). Secondly, the novel lagged auto-mutual information clustering (LAMIC) algorithm is used to cluster the estimated components, together with ocular reference signals, into clusters corresponding to cerebral and non-cerebral activity. Thirdly, the components in the cluster which contains the ocular reference signals are discarded. The remaining components are then recombined to reconstruct the clean ERPs.
Notes
The dataset was kindly contributed by Dr. Vince Calhoun.
The proposed method obtained the first prize in the competition as it outperformed the other entries.
The exact placement of the electrodes was not provided as part of the competition briefing. After the competition it became known that the last two electrodes were vertical and horizontal EOG activity.
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Acknowledgements
The authors would like to thank the competition organisers for making the ERP dataset publicly available as part of the MSLP’05 data analysis competition.
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Nicolaou, N., Nasuto, S.J. Automatic Artefact Removal from Event-related Potentials via Clustering. J VLSI Sign Process Syst Sign Im 48, 173–183 (2007). https://doi.org/10.1007/s11265-006-0011-z
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DOI: https://doi.org/10.1007/s11265-006-0011-z