Cognitive tasks for driving a brain-computer interfacing system: a pilot study

Different cognitive tasks were investigated for use with a brain-computer interface (BCI). The main aim was to evaluate which two of several candidate tasks lead to patterns of electroencephalographic (EEG) activity that could be differentiated most reliably and, therefore, produce the highest commu...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering 2004-03, Vol.12 (1), p.48-54
Main Authors: Curran, E., Sykacek, P., Stokes, M., Roberts, S.J., Penny, W., Johnsrude, I., Owen, A.M.
Format: Article
Language:English
Subjects:
Adult
Aging
Algorithms
Brain computer interfaces
Brain Mapping - methods
Brain modeling
Classification
Cognition - physiology
Communication
Communication Aids for Disabled
Data analysis
Electroencephalography
Electroencephalography - methods
Female
Humans
Logistics
Male
Middle Aged
Navigation
Pattern Recognition, Automated
Pilot Projects
Robustness
Signal generators
Signal processing
Signal Processing, Computer-Assisted
Studies
User-Computer Interface
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Summary:Different cognitive tasks were investigated for use with a brain-computer interface (BCI). The main aim was to evaluate which two of several candidate tasks lead to patterns of electroencephalographic (EEG) activity that could be differentiated most reliably and, therefore, produce the highest communication rate. An optimal signal processing method was also sought to enhance differentiation of EEG profiles across tasks. In ten normal subjects (five male), aged 29-54 years, EEG activity was recorded from four channels during cognitive tasks grouped in pairs, and performed alternately. Four imagery tasks were: spatial navigation around a familiar environment; auditory imagery of a familiar tune; and right and left motor imagery of opening and closing the hand. Signal processing methodology included autoregressive (AR) modeling and classification based on logistic regression and a nonlinear generative classifier. The highest communication rate was found using the navigation and auditory imagery tasks. In terms of classification performance and, hence, possible communication rate, these results were significantly better (p
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2003.821372