Title: Distinct adoption of k-nearest neighbour and support vector machine in classifying EEG signals of mental tasks
Authors: Kusuma Mohanchandra; Snehanshu Saha; K. Srikanta Murthy; G.M. Lingaraju
Department of Computer Science and Engineering, Medical Imaging Research Centre, Dayananda Sagar College of Engineering, Bangalore – 560078, India
Department of Computer Science and Engineering, CBMMIC, PESIT South Campus, Bangalore – 560100, India
Department of Computer Science and Engineering, PESIT South Campus, Bangalore – 560100, India
Department of Information Science and Engineering, MSRIT, Bangalore – 560054, India
Abstract: In this paper, an attempt is made to apply few conventional methods of EEG feature extraction and classification methods and compare their performance for a specific task. Two different feature extraction and classification methods are implemented to classify the mental tasks of EEG signals from a known dataset. For this purpose, the auto regression model and the wavelet transform is used as feature extraction. A combined EEG feature vector is also evaluated on the classification accuracy. The features extracted from these methods are applied to the k-nearest neighbour and support vector machine classifiers separately. Each subject has ten trials of each mental task, in which five trials of each task is used for training the system. The remaining five tasks are used for testing the system. Four different trial combinations of each task are made. The results are evaluated using the confusion matrix. Experimental results specify that each method has specific advantages and disadvantages and is suitable for EEG signal analysis for a specific application.
Keywords: brain computer interface; BCI; electroencephalography; EEG signals; auto regression; wavelet transform; k-nearest neighbour; k-NN; support vector machines; SVM; mental tasks; signal classification; feature extraction.
Int. J. of Intelligent Engineering Informatics, 2015 Vol.3, No.4, pp.313 - 329
Submission date: 17 Nov 2014
Date of acceptance: 02 Feb 2015
Available online: 13 Nov 2015