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

• Published in: Medical & biological engineering & computing Vol. 47; no. 3; pp. 257 - 265
• Main Authors: Tsui, Chun Sing Louis, Gan, John Q, Roberts, Stephen J
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Berlin/Heidelberg : Springer-Verlag 01.03.2009; Springer-Verlag; Springer Nature B.V
• Subjects: Adaptation; Algorithms; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Brain; Brain - physiology; Communication Aids for Disabled; Computer Applications; Design; Discriminant analysis; Electroencephalography - methods; Experiments; Human Physiology; Humans; Imagination - physiology; Imaging; Kalman filters; Labeling; Online instruction; Online Systems; Original Article; Practice (Psychology); Radiology; Robotics; Robots; Signal Processing, Computer-Assisted; Studies; User-Computer Interface; Online training and adaptation; Asynchronous; Self-paced BCI paradigm; Extended Kalman filter; Brain–computer interface
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-009-0459-7

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.