Motor prediction in Brain-Computer Interfaces for controlling mobile robots.
EEG-based Brain-Computer Interface (BCI) can be regarded as a new channel for motor control except that it does not involve muscles. Normal neuromuscular motor control has two fundamental components: (1) to control the body, and (2) to predict the consequences of the control command, which is called motor prediction. In this study, after training with a specially designed BCI paradigm based on motor imagery, two subjects learnt to predict the time course of some features of the EEG signals. It is shown that, with this newly-obtained motor prediction skill, subjects can use motor imagery of feet to directly control a mobile robot to avoid obstacles and reach a small target in a time-critical scenario.
Geng
Tao
T
BCI Group, Department of Computing and Electronic System, University of Essex, Colchester C04 3SQ, UK. tgeng@essex.ac.uk
Gan
John Q
JQ
D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008328:Man-Machine Systems; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D012371:Robotics / Q000379:methods*; D014584:User-Computer Interface