The Impact of Loss of Control on Movement BCIs

Brain-computer interfaces (BCIs) are known to suffer from spontaneous changes in the brain activity. If changes in the mental state of the user are reflected in the brain signals used for control, the behavior of a BCI is directly influenced by these states. We investigate the influence of a state o...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering 2011-12, Vol.19 (6), p.628-637
Main Authors: Reuderink, Boris, Poel, Mannes, Nijholt, Anton
Format: Article
Language:English
Subjects:
Adult
Algorithms
Area Under Curve
Behavior - physiology
Brain
Brain - physiology
Brain modeling
Brain-computer interface (BCI)
common spatial patterns
Computer interfaces
Contingent Negative Variation - physiology
Electrodes
Electroencephalography
electroencephalography (EEG)
Electroencephalography - classification
Electromyography
Electrooculography
Emotions
Equipment Failure
event-related de synchronization (ERD)
Evoked Potentials - physiology
Eye Movements - physiology
Female
Fingers - physiology
Humans
Internal-External Control
lateralized readiness potential
loss of control
Male
mental states
Movement - physiology
nonstationary signals
Psychomotor Performance - physiology
Signal Processing, Computer-Assisted
Software
Synchronization
User-Computer Interface
Video Games
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Summary:Brain-computer interfaces (BCIs) are known to suffer from spontaneous changes in the brain activity. If changes in the mental state of the user are reflected in the brain signals used for control, the behavior of a BCI is directly influenced by these states. We investigate the influence of a state of loss of control in a variant of Pacman on the performance of BCIs based on motor control. To study the effect a temporal loss of control has on the BCI performance, BCI classifiers were trained on electroencephalography (EEG) recorded during the normal control condition, and the classification performance on segments of EEG from the normal and loss of control condition was compared. Classifiers based on event-related desynchronization unexpectedly performed significantly better during the loss of control condition; for the event-related potential classifiers there was no significant difference in performance.
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2011.2166562