Brain Signal Analysis for the Classification of Mental States

Electroencephalograms (EEG) can be used for implicit communication. It allows paralyzed patients to express their thoughts and a person to control devices using only his/her mind. Potential uses of EEGs are to detect individuals with negative intentions such as hijacking or committing crimes and to assist authorities in investigation. However, in such applications signal noise is unavoidable. This study is to explore the possibility of employing EEG signals to use where the presence of signal noise is more prominent than the information content using Independent Component Analysis and Multi-Layer Perceptron Neural Network for feature extraction and classification.