Enhancing the signal-to-noise ratio of ICA-based extracted ERPs
- When decomposing single trial electroencephalography it is a challenge to incorporate prior physiological knowledge. Here, we develop a method that uses prior information about the phase-locking property of event-related potentials in a regularization framework to bias a blind source separation algorithm toward an improved separation of single-trial phase-locked responses in terms of an increased signal-to-noise ratio. In particular, we suggest a transformation of the data, using weighted average of the single trial and trial-averaged response, that redirects the focus of source separation methods onto the subspace of event-related potentials. The practical benefit with respect to an improved separation of such components from ongoing background activity and extraneous noise is first illustrated on artificial data and finally verified in a real-world application of extracting single-trial somatosensory evoked potentials from multichannel EEG-recordings
Author details: | Steven Lemm, Gabriel Curio, Yevhen Hlushchuk, Klaus-Robert Müller |
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URL: | http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=10 |
DOI: | https://doi.org/10.1109/Tbme.2006.870258 |
ISSN: | 0018-9294 |
Publication type: | Article |
Language: | English |
Year of first publication: | 2006 |
Publication year: | 2006 |
Release date: | 2017/03/24 |
Source: | IEEE transactions on biomedical engineering. - ISSN 0018-9294. - 53 (2006), 4, S. 601 - 607 |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik und Computational Science |
Peer review: | Referiert |
Institution name at the time of the publication: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik |