Toward Next generation of Brain Computer Interfaces

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. One of the main challenges when dealing with BCI is non-stationarity inherent in EEG signals. There are huge variations in properties of EEG signals over time, days and across users. Thus, the trained model may not work properly over next sessions or across different users. The aim of this project is to advance BCI technology toward a technology that works perfectly well 24 hours a day and 7 days a week. To achieve this aim, different approaches can be proposed including advanced signal processing and machine learning algorithms, better human training approaches and new mental tasks to run BCI.

The prospective student will gain experience across different disciplines including engineering, neuro-computation and psychology. The project involves designing and conducting experimental research as well as data analysis and algorithm development.

Students with good degrees in engineering, mathematics, neuroscience, computer science or subjects where signal processing may be applied are encouraged to apply. If you are interested in research on BCI, and are unsure about whether you have the right background, please get in touch. The project can be adapted based on the student’s interest and experiences.