Independent component analysis: comparison of algorithms for the investigation of surface electrical brain activity.
We compared the performance of 22 algorithms for independent component analysis with the aim to find suitable algorithms for applications in the field of surface electrical brain activity analysis. The quality of the separation is assessed with four performance measures: a correlation coefficient ba...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 47; no. 4; pp. 413 - 424 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Apr2009
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105472862&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105472862 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2009 vid: 47 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105472862 NLM19214614 2010218367 10.1007/s11517-009-0452-1 NLM19214614 105472862 ppf: 413 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Independent component analysis: comparison of algorithms for the investigation of surface electrical brain activity. aug: au: Klemm M Haueisen J Ivanova G Klemm, Matthias Haueisen, Jens Ivanova, Galina affil: Biomedical Engineering Department, Faculty of Computer Science and Automation, Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, P. O. Box 100565, 98684, Ilmenau, Thuringia, Germany sug: subj: Algorithms Brain Physiology Electroencephalography Methods Evoked Potentials Signal Processing, Computer Assisted ab: We compared the performance of 22 algorithms for independent component analysis with the aim to find suitable algorithms for applications in the field of surface electrical brain activity analysis. The quality of the separation is assessed with four performance measures: a correlation coefficient based index, a signal-to-interference ratio, a signal-to-distortion-ratio and the computational demand. Artificial data are used consisting of typical electroencephalogram and evoked potentials signal patterns, e.g. spikes, polyspikes, sharp waves and spindles. We evaluate different noise scenarios and the influence of pre-whitening. The comparisons reveal considerable differences between the algorithms, especially concerning the computational load. Algorithms based on the time structure of the data set seem to have advantages in separation quality especially for sine-shaped signals. Derivates of FastICA and Infomax also attain good results. Our results can serve as a reference for selecting a task-specific algorithm to analyze a large number of signal patterns occurring in the surface electrical brain activity. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|