Comparison of EEG Pattern Classification Methods for Brain-Computer Interfaces

The aim of this study is to compare 2 EEG pattern classification methods towards the development of BCI. The methods are: (1) discriminant stepwise, and (2) principal component analysis (PCA) - linear discriminant analysis (LDA) joint method. Both methods use Fisher's LDA approach, but differ i...

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Published in2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2007; pp. 2540 - 2543
Main Authors Dias, N.S., Kamrunnahar, M., Mendes, P.M., Schiff, S.J., Correia, J.H.
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.01.2007
Subjects
Adolescent
Adult
Algorithms
Brain - physiology
Brain computer interfaces
Electroencephalography
Electroencephalography - classification
Electroencephalography - instrumentation
Electroencephalography - methods
Feature extraction
Frequency
Humans
Linear discriminant analysis
Merging
Movement - physiology
Pattern classification
Principal component analysis
Testing
Tongue
User-Computer Interface
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Summary:The aim of this study is to compare 2 EEG pattern classification methods towards the development of BCI. The methods are: (1) discriminant stepwise, and (2) principal component analysis (PCA) - linear discriminant analysis (LDA) joint method. Both methods use Fisher's LDA approach, but differ in the data dimensionality reduction procedure. Data were recorded from 3 male subjects 20-30 years old. Three runs per subject took place. The classification methods were tested in 240 trials per subject after merging all runs for the same subject. The mental tasks performed were feet, tongue, left hand and right hand movement imagery. In order to avoid previous assumptions on preferable channel locations and frequency ranges, 105 (21 electrodestimes5 frequency ranges) electroencephalogram (EEG) features were extracted from the data. The best performance for each classification method was taken into account. The discriminant stepwise method showed better performance than the PCA based method. The classification error by the stepwise method varied between 31.73% and 38.5% for all subjects whereas the error range using the PCA based method was 39.42% to 54%.
ISBN:9781424407873
1424407877
ISSN:1094-687X
1557-170X
1558-4615
DOI:10.1109/IEMBS.2007.4352846