| EPSRC Reference: |
EP/D030552/1 |
| Title: |
AABAC: Adaptive Asynchronous Brain-Actuated Control |
| Principal Investigator: |
Dr JQ Gan |
| Other Investigators: |
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| Researcher Co-investigators: |
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| Project Partners: |
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| Department: |
Computer Sci and Electronic Engineering |
| Organisation: |
University of Essex |
| Scheme: |
Standard Research |
| Starts: |
01 April 2006 |
Ends: |
30 September 2009 |
Value (£): |
261,939
|
| EPSRC Research Topic Classifications: |
| Information and communication technologies: Human-Computer Interactions |
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| EPSRC Industrial Sector Classifications: |
| Healthcare |
Creative Industries |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
|
12 Sep 2005
|
People & Interactivity Panel (Technology)
|
Announced
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Summary |
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.
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| Final Report Summary |
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 other devices such as mobile robots.
The project has resulted in over 30 publications.
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| Further Information: |
http://dces.essex.ac.uk/Research/BCIs/AABAC.html |
| Organisation Website: |
http://www.sx.ac.uk |