EEG-Based Mental Task Classification: Linear and Nonlinear Classification of Movement Imagery.

Use 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'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.

EEG-Based Mental Task Classification in Hypnotized and Normal Subjects.

EEG-Based mental task classification is an approach to understand the processes in our brain which lead to our thoughts and behavior. Different mental tasks have been used for this purpose and we have chosen relaxation and imagination for our study. As well as normal conscious state, we have considered mental tasks performed in hypnosis which is defined as a state of consciousness with high concentration. To assess nonlinear dynamics, we have considered fractal dimension in addition to frequency features. HMM classifiers have been used for classification. Results show the most important features in EEG signal related to mentioned mental tasks as well as differences between normal and hypnotic states of the brain.