Feature down-selection in Brain-Computer Interfaces

Current non-invasive brain-computer interface (BCI) designs use as much electroencephalogram (EEG) features as possible rather than few well known motor-reactive features (e.g. rolandic mu-rhythm picked from C3 and C4 channels). Additionally, motor-reactive rhythms do not provide BCI control for every subject. Thus, a subject-specific feature set needs to be determined from a large feature space. Classifier over-fitting is likely for high-dimensional datasets. Therefore, this study introduces an algorithm for feature down-selection on a subject basis based on the across-group variance (AGV). AGV is evaluated in comparison with three other algorithms: recursive feature elimination (RFE); simple genetic algorithm (GA); and RELIEF algorithm. High-dimensional data from 5 healthy subjects were first reduced by the algorithms under experiment and then classified on the alternative right hand or foot movement imagery tasks. AGV outperformed the other tested methods simultaneously selecting the smallest feature subsets. Effective dimensionality reduction (as low as 8 features out of 118) with high discrimination power (as high as 90.4) was best observed on AGV's performance.