AABAC: Adaptive Asynchronous Brain-Actuated Control
Lead Research Organisation:
University of Essex
Department Name: Computer Sci and Electronic Engineering
Abstract
This proposed project aims to develop a novel adaptive and asynchronous brain-computer interface (BCI) system for brain-actuated control of intelligent systems and robots. Recent advances in science and technology have shed light on the possibility of fusing human's brain with intelligent machines to carry out challenging tasks that the state of the art autonomous machines cannot undertake. BCI is one of the key technologies to make this possible. A BCI system detects and analyses brain waves, e.g., electroencephalography (EEG) signals, in order to understand a user's mental states, and then translates the mental states into commands for communicating with and controlling computers, robots, and other systems. Almost all the current EEG-based BCI systems of high accuracy use synchronous protocols and recognise two mental states only. Their disadvantages include low information transfer rate and unnatural user interface, which impose severe limitations on BCI systems for real-world applications. Based on our previous research in BCI and related areas, we believe that it is now very timely to develop adaptive and asynchronous BCI systems that not only have the advantages of using asynchronous protocols, such as high information transfer rate and natural operation mode, but also benefit from adaptive learning so as to improve the system's accuracy and robustness. Apart from adaptive learning, in order to achieve high accuracy and robustness, this proposed programme will investigate novel effective indicators for onset detection and optimal timing schemes for asynchronous mental state classification, discover or invent new feature spaces on which it would be easier to classify EEG patterns, and develop new methods for increasing the number of control commands mapped from a limited number of mental states. The methods developed hereby will be assessed through extensive experimentation with real-time brain-actuated control of an intelligent wheelchair and a robotic arm.
Organisations
Publications
Awwad Shiekh Hasan B
(2010)
Unsupervised movement onset detection from EEG recorded during self-paced real hand movement.
in Medical & biological engineering & computing
Dyson M
(2010)
Localisation of cognitive tasks used in EEG-based BCIs.
in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Geng T
(2008)
A novel design of 4-class BCI using two binary classifiers and parallel mental tasks.
in Computational intelligence and neuroscience
Geng T
(2010)
A self-paced online BCI for mobile robot control
in International Journal of Advanced Mechatronic Systems
Hasan BA
(2011)
Temporal modeling of EEG during self-paced hand movement and its application in onset detection.
in Journal of neural engineering
Hasan BA
(2012)
Hangman BCI: an unsupervised adaptive self-paced Brain-Computer Interface for playing games.
in Computers in biology and medicine
Hasan BA
(2011)
Conditional random fields as classifiers for three-class motor-imagery brain-computer interfaces.
in Journal of neural engineering
Tsui CS
(2009)
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.
in Medical & biological engineering & computing
Tsui CS
(2011)
A self-paced motor imagery based brain-computer interface for robotic wheelchair control.
in Clinical EEG and neuroscience
Yoon JW
(2009)
Adaptive classification for Brain Computer Interface systems using Sequential Monte Carlo sampling.
in Neural networks : the official journal of the International Neural Network Society