In EMBS, 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), September 1-4,, Shanghai, China (Accepted), September 2005 (inproceedings) Accepted
AbstractUse of EEG signals as a channel of communication between men and machines represents one of the current challenges in signal theory research. The principal element of
such a communication system, known as a Brain-Computer Interface, is the interpretation of the EEG signals related to the characteristic parameters of brain electrical activity. Our goal in this work was extracting quantitative changes in the
EEG due to movement imagination. Subject&lsquo;s EEG was recorded while he performed left or right hand movement imagination. Different feature sets extracted from EEG were
used as inputs into linear, Neural Network and HMM classifiers for the purpose of imagery movement mental task classification. The results indicate that applying linear classifier to 5 frequency features of asymmetry signal produced from
channel C3 and C4 can provide a very high classification accuracy percentage as a simple classifier with small number of features comparing to other feature sets.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems