Asynchronous BCI Control of a Robot Simulator with Supervised Online Training

Tsui, Chun Sing Louis; Gan, John Q.

doi:10.1007/978-3-540-77226-2_14

Cited by 27 publications

(16 citation statements)

References 13 publications

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“…In [24], a method to automatically refine the preset threshold and dwell settings during online training was developed. In this paper, we adopt an alternative approach as we focus on adapting the parameters of the LDA classifiers instead of adapting the threshold and dwell settings.…”

Section: Online Self-paced Event Detectionmentioning

confidence: 99%

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

Tsui

Gan

Roberts

2009

Med Biol Eng Comput

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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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Section: Online Self-paced Event Detectionmentioning

confidence: 99%

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

Tsui

Gan

Roberts

2009

Med Biol Eng Comput

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“…The same simple classification (both hands MI) has been used in [29] to move through a grid of hexagons. After each turn command, the system forced an advance in the pointed direction.…”

Section: 2mentioning

confidence: 99%

BCI-Based Navigation in Virtual and Real Environments

Velasco-Álvarez

Ron-Angevín

López-Gordo

2013

Advances in Computational Intelligence

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Abstract. A Brain-Computer Interface (BCI) is a system that enables people to control an external device with their brain activity, without the need of any muscular activity. Researchers in the BCI field aim to develop applications to improve the quality of life of severely disabled patients, for whom a BCI can be a useful channel for interaction with their environment. Some of these systems are intended to control a mobile device (e. g. a wheelchair). Virtual Reality is a powerful tool that can provide the subjects with an opportunity to train and to test different applications in a safe environment. This technical review will focus on systems aimed at navigation, both in virtual and real environments.

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“…Normally, the simulated device in the virtual environment is in charge of two actions http://dx.doi.org/10.1016/j.eswa.2015.01.031 0957-4174/Ó 2015 Elsevier Ltd. All rights reserved. in response to left-or right-hand MI task (Pfurtscheller, Neuper, Schlogl, & Lugger, 1998;Tsui & Gan, 2007). It is proved that the virtual environment improves the performance of BCI system (Ron-Angevin & Díaz-Estrella, 2009).…”

Section: Introductionmentioning

confidence: 96%