Visual P300-based BCI to steer a wheelchair: a Bayesian approach.
This paper presents a new P300 paradigm for brain computer interface. Visual stimuli consisting of 8 arrows randomly intensified are used for direction target selection for wheelchair steering. The classification is based on a Bayesian approach that uses prior statistical knowledge of target and non-target components. Recorded brain activity from several channels is combined with a Bayesian sensor fusion and then events are grouped to improve event detection. The system has an adaptive performance that adapts to user and P300 pattern quality. The classification algorithms were obtained offline from training and then validated offline and online. The system achieved a transfer rate of 7 commands/min with 95% false positive classification accuracy.
Pires
Gabriel
G
Institute for Systems and Robotics, University of Coimbra, 3000, Portugal. gpires@isr.uc.pt
Castelo-Branco
Miguel
M
Nunes
Urbano
U
D001185:Artificial Intelligence; D001499:Bayes Theorem; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D012371:Robotics / Q000379:methods*; D013647:Task Performance and Analysis; D014584:User-Computer Interface; D014793:Visual Cortex / Q000502:physiology*; D014910:Wheelchairs