Document Detail


Evaluation of feature extraction methods for EEG-based brain-computer interfaces in terms of robustness to slight changes in electrode locations.
MedLine Citation:
PMID:  23325145     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
To date, most EEG-based brain-computer interface (BCI) studies have focused only on enhancing BCI performance in such areas as classification accuracy and information transfer rate. In practice, however, test-retest reliability of the developed BCI systems must also be considered for use in long-term, daily life applications. One factor that can affect the reliability of BCI systems is the slight displacement of EEG electrode locations that often occurs due to the removal and reattachment of recording electrodes. The aim of this study was to evaluate and compare various feature extraction methods for motor-imagery-based BCI in terms of robustness to slight changes in electrode locations. To this end, EEG signals were recorded from three reference electrodes (Fz, C3, and C4) and from six additional electrodes located close to the reference electrodes with a 1-cm inter-electrode distance. Eight healthy participants underwent 180 trials of left- and right-hand motor imagery tasks. The performance of four different feature extraction methods [power spectral density (PSD), phase locking value (PLV), a combination of PSD and PLV, and cross-correlation (CC)] were evaluated using five-fold cross-validation and linear discriminant analysis, in terms of robustness to electrode location changes as well as regarding absolute classification accuracy. The quantitative evaluation results demonstrated that the use of either PSD- or CC-based features led to higher classification accuracy than the use of PLV-based features, while PSD-based features showed much higher sensitivity to changes in EEG electrode location than CC- or PLV-based features. Our results suggest that CC can be used as a promising feature extraction method in motor-imagery-based BCI studies, since it provides high classification accuracy along with being little affected by slight changes in the EEG electrode locations.
Authors:
Sun-Ae Park; Han-Jeong Hwang; Jeong-Hwan Lim; Jong-Ho Choi; Hyun-Kyo Jung; Chang-Hwan Im
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-1-17
Journal Detail:
Title:  Medical & biological engineering & computing     Volume:  -     ISSN:  1741-0444     ISO Abbreviation:  Med Biol Eng Comput     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-1-17     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7704869     Medline TA:  Med Biol Eng Comput     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Department of Electrical Engineering and Computer Science, Seoul National University, Seoul, 133-791, Republic of Korea.
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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