Abstract—Electroencephalography (EEG) measure human
brain activity for different applications. In this paper
performance of Instance Based classifier is compared with well
known classifier, such as AdaBoost, J48, IB1, etc. As the
experimental result shows that the Random forest and Instance
Based Classifier like IB1& IBK are performing well as
compared to other classifiers for predicting eye state (open or
closed) using EEG signals from electrode data.
Index Terms—Electroencephalography (EEG), emotive
devices, instance based classifier.
Mridu Sahu is with the Department of IT, NIT, Raipur, India (e-mail:
mrisahu.it@nitrr.ac.in).
Saransh Shirke is with the Department of Electrical Engineering, NIT
Raipur, India (e-mail: saransh.shirke@gmail.com).
K. Nagwani is with the Department of CSE, NIT, Raipur, India (e-mail:
nknagwani.cs@nitrr.ac.in).
Shrish Verma is with the Department of E&TC, NIT, Raipur, India
(e-mail: shrishverma@nitrr.ac.in).
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Cite: Mridu Sahu, N. K. Nagwani, Shrish Verma, and Saransh Shirke, "Performance Evaluation of Different Classifier for Eye State Prediction Using EEG Signal," International Journal of Knowledge Engineering vol. 1, no. 2, pp. 141-145, 2015.
