Research article summary (published 18 Jul 2006):

Classification of single trial motor imagery EEG recordings with subject adapted non-dyadic arbitrary time-frequency tilings.

Full Abstract

We describe a new technique for the classification of motor imagery electroencephalogram (EEG) recordings in a brain computer interface (BCI) task. The technique is based on an adaptive time-frequency analysis of EEG signals computed using local discriminant bases (LDB) derived from local cosine packets (LCP). In an offline step, the EEG data obtained from the C(3)/C(4) electrode locations of the standard 10/20 system is adaptively segmented in time, over a non-dyadic grid by maximizing the probabilistic distances between expansion coefficients corresponding to left and right hand movement imagery. This is followed by a frequency domain clustering procedure in each adapted time segment to maximize the discrimination power of the resulting time-frequency features. Then, the most discriminant features from the resulting arbitrarily segmented time-frequency plane are sorted. A principal component analysis (PCA) step is applied to reduce the dimensionality of the feature space. This reduced feature set is finally fed to a linear discriminant for classification. The online step simply computes the reduced dimensionality features determined by the offline step and feeds them to the linear discriminant. We provide experimental data to show that the method can adapt to physio-anatomical differences, subject-specific and hemisphere-specific motor imagery patterns. The algorithm was applied to all nine subjects of the BCI Competition 2002. The classification performance of the proposed algorithm varied between 70% and 92.6% across subjects using just two electrodes. The average classification accuracy was 80.6%. For comparison, we also implemented an adaptive autoregressive model based classification procedure that achieved an average error rate of 76.3% on the same subjects, and higher error rates than the proposed approach on each individual subject.

Author information

Author/s: Ince, Nuri Firat (NF); Arica, Sami (S); Tewfik, Ahmed (A);

Affiliation: Department of Electrical and Electronics Engineering, University of Cukurova, Adana 01330, Turkey.

Journal and publication information

Publication Type: 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 235-44

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

PMID: 16921207, 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.

External Links for this article
(including full text providers, if available):

Click Electronic Full-text Provider Links to see options for finding the electronic full text links to this article. Note there may be a subscription or fee required for access to the full text. See our FAQ for information on finding FREE full text articles.

This article may also be located in paper journal collections available in many libraries. Use the Journal and Publication Information above to find the full article.

MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms Artificial Intelligence Diagnosis, Computer-Assisted - methods Discriminant Analysis Electroencephalography - methods Evoked Potentials, Motor - physiology Humans

Imagination - physiology Movement - physiology Pattern Recognition, Automated Principal Component Analysis Reproducibility of Results Sensitivity and Specificity User-Computer Interface

Related articles

These are the highest related articles currently in the database:

• Seperability of four-class motor imagery data using independent components analysis. 25 Jun 2006
• A comparison of common spatial patterns with complex band power features in a four-class BCI experiment. 30 Mar 2006
• Classification of the intention to generate a shoulder versus elbow torque by means of a time-frequency synthesized spatial patterns BCI algorithm. 23 Oct 2005
• A tree-structure mutual information-based feature extraction and its application to EEG-based brain-computer interfacing. 30 Dec 2006
• Classification of motor imagery by means of cortical current density estimation and Von Neumann entropy. 22 Jan 2007
• Characterization of four-class motor imagery EEG data for the BCI-competition 2005. 13 Aug 2005
• Time-variant spatial filtering for motor imagery classification. 30 Dec 2006
• Effect of feature and channel selection on EEG classification. 30 Dec 2005
• Multi-channel linear descriptors for event-related EEG collected in brain computer interface. 4 Feb 2006
• Bipolar electrode selection for a motor imagery based brain-computer interface. 26 Aug 2008

See 100+ related articles.

Related Article Map

Legend: - FREE Full text Article. - Abstract only. - Title only. More help.

See a large map of 100+ related articles.