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 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 electrodes×5 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%.