Application of tripolar concentric electrodes and prefeature selection algorithm for brain-computer interface.
For persons with severe disabilities, a brain-computer interface (BCI) may be a viable means of communication. Lapalacian electroencephalogram (EEG) has been shown to improve classification in EEG recognition. In this work, the effectiveness of signals from tripolar concentric electrodes and disc electrodes were compared for use as a BCI. Two sets of left/right hand motor imagery EEG signals were acquired. An autoregressive (AR) model was developed for feature extraction with a Mahalanobis distance based linear classifier for classification. An exhaust selection algorithm was employed to analyze three factors before feature extraction. The factors analyzed were 1) length of data in each trial to be used, 2) start position of data, and 3) the order of the AR model. The results showed that tripolar concentric electrodes generated significantly higher classification accuracy than disc electrodes.
Besio
Walter G
WG
Biomedical Engineering Department, Louisiana Tech University, Ruston, LA 71272, USA. besio@ele.uri.edu
Cao
Hongbao
H
Zhou
Peng
P
D000328:Adult; D000465:Algorithms; D001931:Brain Mapping / Q000295:instrumentation* / Q000379:methods*; D004566:Electrodes; D004867:Equipment Design; D019544:Equipment Failure Analysis; D019054:Evoked Potentials, Motor / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D009044:Motor Cortex / Q000502:physiology; D011597:Psychomotor Performance / Q000502:physiology; D013647:Task Performance and Analysis; D014584:User-Computer Interface