Research article summary (published 25 Jun 2006):

Seperability of four-class motor imagery data using independent components analysis.

Full Abstract

This paper compares different ICA preprocessing algorithms on cross-validated training data as well as on unseen test data. The EEG data were recorded from 22 electrodes placed over the whole scalp during motor imagery tasks consisting of four different classes, namely the imagination of right hand, left hand, foot and tongue movements. Two sessions on different days were recorded for eight subjects. Three different independent components analysis (ICA) algorithms (Infomax, FastICA and SOBI) were studied and compared to common spatial patterns (CSP), Laplacian derivations and standard bipolar derivations, which are other well-known preprocessing methods. Among the ICA algorithms, the best performance was achieved by Infomax when using all 22 components as well as for the selected 6 components. However, the performance of Laplacian derivations was comparable with Infomax for both cross-validated and unseen data. The overall best four-class classification accuracies (between 33% and 84%) were obtained with CSP. For the cross-validated training data, CSP performed slightly better than Infomax, whereas for unseen test data, CSP yielded significantly better classification results than Infomax in one of the sessions.

Author information

Author/s: Naeem, M (M); Brunner, C (C); Leeb, R (R); Graimann, B (B); Pfurtscheller, G (G);

Affiliation: Laboratory of Brain-Computer Interfaces (BCI-Lab), Graz University of Technology, Krenngasse 37, 8010 Graz, Austria.

Journal and publication information

Publication Type: Clinical Trial; Journal Article; Research Support, Non-U.S. Gov't

Journal: Journal of neural engineering (J Neural Eng), published in England. (Language: eng)

Reference: 2006-Sep; vol 3 (issue 3) : pp 208-16

Dates: Created 2006/08/21; Completed 2006/11/20;

PMID: 16921204, status: MEDLINE (last retrieval date: 2/18/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Adult Algorithms Artificial Intelligence Diagnosis, Computer-Assisted - methods Electroencephalography - methods Evoked Potentials, Motor - physiology Female Humans

Humans Imagination - physiology Male Movement - physiology Pattern Recognition, Automated Principal Component Analysis Reproducibility of Results Sensitivity and Specificity

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