A brain-computer interface (BCI) provides a direct communication pathway between a human brain and an external device. Using appropriate sensors and data processing algorithms, a BCI maps patterns of brain activity associated with a volitional thought onto signals suitable for communication and control. In most of BCI systems, brain signals are measured by electroencephalogram (EEG), due to its non-invasiveness, relatively low cost and high temporal resolution.
The BCI technology holds great promise as a basis for assisting people with severe communication and motor disabilities. Moreover, it can be applied for nonmedical applications such as gaming. Despite the impressive expansion in the recent years, none of the BCI systems described in the literature are sufficiently mature for the daily use out of the laboratory. In order to make BCI the next generation of intuitive interfaces, improvements are required in several areas, such as usability, signal acquisition techniques, hardware development, machine learning and signal processing, and system integration.
We are interested in working on different areas to improve the BCI technology. These areas include (but are not limited to):
- Advanced algorithms to minimize or suppress the calibration time
- Robust BCI to deal with inter-subject and inter-session variabilities
- Asynchronous and continuous operation of BCI
- Prediction of BCI performance, BCI deficiency and countermeasures
- Teaching the BCI skill: feedback and human training approaches
- Brain/neuronal computer games interfaces and interaction
- Using BCI for monitoring and improving cognitive performance
The prospective student will gain experience across different disciplines including engineering, neuro-computation and psychology. The projects involve designing and conducting experimental research as well as data analysis and algorithm development.
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