The analysis of EEG signals play an important role in a wide range of applications, such as psychotropic drug research, sleep studies, seizure detection and brain computer interface. Various EEG analysis methods have been proposed in the literature, and some of these methods achieved good results in specific applications. However, automated EEG analysis is still a very challenging problem due to the complexity of extracting useful information from EEG signals.
Processing EEG signals requires dealing with multi-dimensional data. As a result, there is a need to minimize the dimension of the problem (features/channels) to improve the performance. The aim of our current project is to find the optimal combination of channels/features that would achieve appropriate results with less computational cost.
Postgraduate students working in this project:
· Rami N. Khushaba