A self-paced brain interface system that uses movement related potentials and changes in the power of brain rhythms.
Movement execution results in the simultaneous generation of movement-related potentials (MRP) as well as changes in the power of Mu and Beta rhythms. This paper proposes a new self-paced multi-channel BI that combines features extracted from MRPs and from changes in the power of Mu and Beta rhythms. We developed a new algorithm to classify the high-dimensional feature space. It uses a two-stage multiple-classifier system (MCS). First, an MCS classifies each neurological phenomenon separately using the information extracted from specific EEG channels (EEG channels are selected by a genetic algorithm). In the second stage, another MCS combines the outputs of MCSs developed in the first stage. Analysis of the data of four able-bodied subjects shows the superior performance of the proposed algorithm compared with a scheme where the features were all combined in a single feature vector and then classified.
Fatourechi
Mehrdad
M
Department of Electrical and Computer Engineering, University of British Columbia, 2356 Main Mall, Vancouver, BC, Canada, V6T 1Z4. mehrdadf@ece.ubc.ca
Birch
Gary E
GE
Ward
Rabab K
RK
D001921:Brain / Q000502:physiology*; D004569:Electroencephalography; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D009068:Movement / Q000502:physiology*; D010507:Periodicity; D010775:Photic Stimulation / Q000379:methods; D011597:Psychomotor Performance / Q000502:physiology; D013998:Time Perception / Q000502:physiology*; D014584:User-Computer Interface