NavChair: An Assistive Wheelchair Navigation System with Automatic Adaptation
Assistive Technology and Artificial Intelligence, Applications in Robotics, User Interfaces and Natural Language Processing
Shared user-computer control of a robotic wheelchair system
Shared user-computer control of a robotic wheelchair system
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Artificial Intelligence
IEEE Transactions on Robotics - Special issue on rehabilitation robotics
Synchronous EEG brain-actuated wheelchair with automated navigation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Non-invasive brain-actuated interaction
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Clustering of EEG data using maximum entropy method and LVQ
ISTASC'10 Proceedings of the 10th WSEAS international conference on Systems theory and scientific computation
Backwards maneuvering powered wheelchairs with haptic guidance
EuroHaptics'12 Proceedings of the 2012 international conference on Haptics: perception, devices, mobility, and communication - Volume Part I
Assisted navigation for a brain-actuated intelligent wheelchair
Robotics and Autonomous Systems
Did I Do That? Brain---Computer Interfacing and the Sense of Agency
Minds and Machines
BCI-based navigation in virtual and real environments
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased. This paper introduces a shared control system that helps the subject in driving an intelligent wheelchair with a noninvasive brain interface. The subject's steering intentions are estimated from electroencephalogram (EEG) signals and passed through to the shared control system before being sent to the wheelchair motors. Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it. These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.