Abstract
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.
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Notes
According to our experience, all subjects prefer to perform the next mental task before the end of the refractory period, while they see the robot (see Sect. 2.4) is about to reach the next node.
In that study, nine subjects participated in an offline BCI experiment. Five were invited for further online experiment, but only three agreed and only two of them were able to control the online self-paced BCI system at a reasonably satisfactory level.
For this experiment, the initial setting for threshold is 0.5, and 1.5 s for dwell time. The first author is the only experiment conductor.
In our experience, subjects usually found themselves easier to control one class over another class, so it is reasonable to assign threshold and dwell time differently.
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Acknowledgment
This work was partly supported by the UK EPSRC under grant EP-D030552-1. The first author is supported by the UK Overseas Research Studentship (ORS) and University of Essex studentship, who would also like to thank Matthew Dyson and Tao Geng for their advice and help in BCI experiments.
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Tsui, C.S.L., Gan, J.Q. & Roberts, S.J. 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. Med Biol Eng Comput 47, 257–265 (2009). https://doi.org/10.1007/s11517-009-0459-7
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DOI: https://doi.org/10.1007/s11517-009-0459-7