Asynchronous Brain-Computer Interface to Navigate in Virtual Environments Using One Motor Imagery
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Free virtual navigation using motor imagery through an asynchronous brain--computer interface
Presence: Teleoperators and Virtual Environments
Audio-cued SMR brain-computer interface to drive a virtual wheelchair
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Towards adaptive classification of motor imagery EEG using biomimetic pattern recognition
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
BCI-based navigation in virtual and real environments
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
Hi-index | 0.00 |
Due to the non-stationarity of EEG signals, online training and adaptation is essential to EEG based brain-computer interface (BCI) systems. Asynchronous BCI offers more natural human-machine interaction, but it is a great challenge to train and adapt an asynchronous BCI online because the user's control intention and timing are usually unknown. This paper proposes a novel motor imagery based asynchronous BCI 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 motor imagery based asynchronous BCI can be effectively investigated. This paper also proposes an online training method, attempting to automate the process of finding the optimal parameter values of the BCI system to deal with non-stationary EEG signals. Experimental results have shown that the proposed method for online training of asynchronous BCI significantly improves the performance.