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 el...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering 2008-04, Vol.16 (2), p.191-194
Main Authors: Besio, Walter G., Cao, Hongbao, Zhou, Peng
Format: Article
Language:English
Subjects:
Adult
Algorithm design and analysis
Algorithms
Biomedical electrodes
Biomedical engineering
Brain Mapping - instrumentation
Brain Mapping - methods
Brain modeling
Brain-computer interface (BCI)
Electrodes
electroencephalogram (EEG) classification
Electroencephalography
Equipment Design
Equipment Failure Analysis
Evoked Potentials, Motor - physiology
Feature extraction
Female
Frequency
Humans
Laplace equations
Laplacian estimation
Male
Motor Cortex - physiology
Parameter estimation
parameter selection
Psychomotor Performance - physiology
Spline
Task Performance and Analysis
tripolar electrode
User-Computer Interface
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Summary: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.
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2007.916303