Abstract
Finding human cognitive state is very useful for medical cares in our daily life. EEG eye state classification is a common time series problem for detecting human cognitive state. A cognitive state can be defined as the human thought processes and state of mind. This paper mainly focuses on the prediction and classification of EEG eye state dataset determining various parameters such as Mean absolute error, Relative absolute error, True positive rate, False positive etc. ROC area has also been graphically observed for various conditions. Six types of classifiers such as Naive Bayes multinomial, Logistic, PART, K nearest neighbor classifier, Decision table, and SVM have been applied in this work to represent a comparative analysis. Experimental result shows that Partial Decision Tree (PART) and K nearest neighbor classifier can correctly identify larger percentage of instances compared to the Naive Bayes multinomial, Logistic regression, SVM, Decision table. Weka Data mining tool has been used for simulation purpose.
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