Sparse representation-based classification scheme for motor imagery-based brain-computer interface systems

Motor imagery (MI)-based brain-computer interface systems (BCIs) normally use a powerful spatial filtering and classification method to maximize their performance. The common spatial pattern (CSP) algorithm is a widely used spatial filtering method for MI-based BCIs. In this work, we propose a new s...

Full description

Saved in:
Bibliographic Details
Published in:Journal of neural engineering 2012-10, Vol.9 (5), p.056002-056002
Main Authors: Shin, Younghak, Lee, Seungchan, Lee, Junho, Lee, Heung-No
Format: Article
Language:English
Subjects:
Algorithms
brain-computer interface system (BCIs)
Brain-Computer Interfaces - classification
Classification
common spatial pattern (CSP)
compressive sensing (CS)
Databases, Factual
Discriminant analysis
electroencephalogram (EEG)
Electroencephalography - methods
Evoked Potentials, Motor - physiology
Frequency bands
Human-computer interface
Humans
Imagery
Imagination - physiology
Motors
sensorimotor rhythms (SMRs)
sparse representation
Spatial filtering
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Motor imagery (MI)-based brain-computer interface systems (BCIs) normally use a powerful spatial filtering and classification method to maximize their performance. The common spatial pattern (CSP) algorithm is a widely used spatial filtering method for MI-based BCIs. In this work, we propose a new sparse representation-based classification (SRC) scheme for MI-based BCI applications. Sensorimotor rhythms are extracted from electroencephalograms and used for classification. The proposed SRC method utilizes the frequency band power and CSP algorithm to extract features for classification. We analyzed the performance of the new method using experimental datasets. The results showed that the SRC scheme provides highly accurate classification results, which were better than those obtained using the well-known linear discriminant analysis classification method. The enhancement of the proposed method in terms of the classification accuracy was verified using cross-validation and a statistical paired t-test (p < 0.001).
ISSN:1741-2560
1741-2552
DOI:10.1088/1741-2560/9/5/056002