Recognition of motor imagery electroencephalography using independent component analysis and machine classifiers.
Motor imagery electroencephalography (EEG), which embodies cortical potentials during mental simulation of left or right finger lifting tasks, can be used to provide neural input signals to activate a brain computer interface (BCI). The effectiveness of such an EEG-based BCI system relies on two indispensable components: distinguishable patterns of brain signals and accurate classifiers. This work aims to extract two reliable neural features, termed contralateral and ipsilateral rebound maps, by removing artifacts from motor imagery EEG based on independent component analysis (ICA), and to employ four classifiers to investigate the efficacy of rebound maps. Results demonstrate that, with the use of ICA, recognition rates for four classifiers (fisher linear discriminant (FLD), back-propagation neural network (BP-NN), radial-basis function neural network (RBF-NN), and support vector machine (SVM)) improved significantly, from 54%, 54%, 57% and 55% to 70.5%, 75.5%, 76.5% and 77.3%, respectively. In addition, the areas under the receiver operating characteristics (ROC) curve, which assess the quality of classification over a wide range of misclassification costs, also improved from .65, .60, .62, and .64 to .74, .76, .80 and .81, respectively.
Hung
Chih-I
CI
Institute of Radiological Sciences, National Yang-Ming University, Taipei, ROC, Taiwan.
Lee
Po-Lei
PL
Wu
Yu-Te
YT
Chen
Li-Fen
LF
Yeh
Tzu-Chen
TC
Hsieh
Jen-Chuen
JC
D000328:Adult; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography; D005260:Female; D006801:Humans; D008297:Male; D008606:Mental Processes / Q000502:physiology*; D010465:Perception / Q000502:physiology*; D021641:Recognition, Psychology / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface