Discrimination and Identification Method of Human Activity Level based on Information Processing of EEG Signals

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In this research project, we have performed studies on the development of a discrimination and identification method of human activity level (sleep level and arousal level) based on information processing of electroencephalogram (EEG) signals and obtained following research results.1.Automatic determination system of human EEG sleep stagesWe have developed an automatic stage determination system with graphical user interface based on the wave-shape recognition method. By this system, we obtained about 80% accuracy compared with clinician's scoring.2.Feature extraction of sleep stages based on EEG generating modelsThe EEG generating mechanism was modeled by a damped system excited by impulse input process which subjects to Poisson process whose amplitude has a transition property. Furthermore, a Maximum A Posteriori (MAP) method was modified to be applicable to the model. We could then extract useful information directly related to sleep stages by using our method.3.Information processi … More ng of EEG based on artificial neural network (ANN)ANN was applied to EEG signals as the predictor for alpha-waves, high voltage delta-waves and low voltage fast waves. Classification of unknown waves among the above mentioned class were possible by evaluating the infinity norm between ANN output and original signals.4.Investigation of event related potential (ERP) under visual cognitive taskThe measurement system was developed using 2 computers. One is used for visual stimulation, the other is used for data acquisition. Investigating EEG signals under the oddball task (2 kinds of color or 2 kinds of character pattern), it was confirmed that wave patterns were changed by stimulation only at the low arousal level. We also tried to classify the single trial ERPs by using statistical pattern recognition method. This was not easy, because P300 related to vanishing time of stimulation exists in all data (target and non-target stimulus).5.Quantitative evaluation method of stationarity of EEG signals as the features of human activity levelWe have explored the possibilities of the quantitative evaluation based on wavelet transform. It has been confirmed that EEG waves whose duration is 0.5 [sec] could be classified by using wavelet transform and statistical pattern recognition method. These results suggest the possibilities of segmentation for stationary period. Therefore, we will further investigate to find out quantitative evaluation methods related to the segmentations. Less