Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/TBME.2006.870258 | Biomedical Engineering, IEEE Transactions |
Keywords | Field | DocType |
bioelectric potentials,blind source separation,electroencephalography,independent component analysis,medical signal processing,noise,somatosensory phenomena,ICA-based extracted ERP,blind source separation,event-related potentials,multichannel EEG recordings,phase-locking property,regularization,signal-to-noise ratio enhancement,single trial electroencephalography decomposition,single-trial somatosensory evoked potentials,Bioelectrical potentials,electroencephalogram (EEG),independent component analysis (ICA),signal-to-noise ratio | Subspace topology,Pattern recognition,Computer science,Signal-to-noise ratio,Speech recognition,Regularization (mathematics),Independent component analysis,Artificial intelligence,Blind signal separation,Principal component analysis,Electroencephalography,Source separation | Journal |
Volume | Issue | ISSN |
53 | 4 | 0018-9294 |
Citations | PageRank | References |
8 | 1.30 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steven Lemm | 1 | 1006 | 80.98 |
| Gabriel Curio | 2 | 1220 | 201.67 |
| Yevhen Hlushchuk | 3 | 39 | 5.95 |
| Klaus-Robert Müller | 4 | 12756 | 1615.17 |