Context-based filtering for assisted brain-actuated wheelchair driving
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Modeling dynamic scenarios for local sensor-based motion planning
Autonomous Robots
IEEE Transactions on Robotics - Special issue on rehabilitation robotics
Fuzzy classification-based control of wheelchair using EEG data to assist people with disabilities
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
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This paper describes a new non-invasive brain-actuated wheelchair that relies on a P300 neurophysiological protocol and automated navigation. In operation, the subject faces a screen with a real-time virtual reconstruction of the scenario, and concentrates on the area of the space to reach. A visual stimulation process elicits the neurological phenomenon and the EEG signal processing detects the target area. This target area represents a location that is given to the autonomous navigation system, which drives the wheelchair to the desired place while avoiding collisions with the obstacles detected by the laser scanner. The accuracy of the brain-computer interface is above 94% and the flexibility of the sensor-based motion system allows for navigation in non-prepared and populated scenarios. The prototype has been validated with five healthy subjects in three experimental sessions: screening (an analysis of three different interfaces and its implications on the performance of the users), virtual environment driving (training and instruction of the users) and driving sessions with the wheelchair (driving tests along pre-established circuits). On the basis of the results, this paper reports a technical evaluation of the device and a variability study. All the users were able to successfully use the device with relative ease showing a great adaptation.