A self-paced brain-computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training.
Due to the non-stationarity of EEG signals, online training and adaptation are essential to EEG based brain-computer interface (BCI) systems. Self-paced BCIs offer more natural human-machine interaction than synchronous BCIs, but it is a great challenge to train and adapt a self-paced BCI online because the user's control intention and timing are usually unknown. This paper proposes a novel motor imagery based self-paced BCI paradigm for controlling a simulated robot in a specifically designed environment which is able to provide user's control intention and timing during online experiments, so that online training and adaptation of the motor imagery based self-paced BCI can be effectively investigated. We demonstrate the usefulness of the proposed paradigm with an extended Kalman filter based method to adapt the BCI classifier parameters, with experimental results of online self-paced BCI training with four subjects.
Tsui
Chun Sing Louis
CS
School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK. csltsu@essex.ac.uk
Gan
John Q
JQ
Roberts
Stephen J
SJ
D000465:Algorithms; D001921:Brain / Q000502:physiology*; D003143:Communication Devices for People with Disabilities; D004569:Electroencephalography / Q000379:methods; D006801:Humans; D007092:Imagination / Q000502:physiology; D009862:Online Systems; D011214:Practice, Psychological; D012371:Robotics; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface