Comparative Analysis of KNN, SVM, DT for EOG based Human Computer Interface

In this study, EOG signal based human computer interface (HCI) has been implemented. This is a communication system that permits interaction with computer using eye movement. The necessary steps of implementation of HCI are EOG signal acquisition and analysis, feature extraction and classification. EOG signal has been acquired by placing electrodes at left and right corner of eye from 12 subjects. Dual Tree Complex Wavelet Transform (DTCWT) has been employed to denoise the EOG signal and 16 features are extracted from the time domain. Three classifiers (Decision Tree (DT), k-Nearest Neighbor (KNN) algorithm, and Support Vector Machines (SVM)) have been used to identify the horizontal eye movement i.e. left and right. Analysis and comparison of their performance is made on the basis of confusion matrix, receiver operating characteristics (ROC) and performance indices i.e. sensitivity, specificity, precision, accuracy and F1 score to evaluate the most efficient classifier for their classification task. According to classification results, out of three classifiers, KNN is the best classifier for horizontal EOG signal and has shown almost 100 percent accuracy.

[1]  N. Kingsbury.  Complex Wavelets for Shift Invariant Analysis and Filtering of Signals , 2001 .

[2]  Richard Baraniuk,et al.  The Dual-tree Complex Wavelet Transform , 2007 .

[3]  Abdulhamit Subasi,et al.  Comparison of decision tree algorithms for EMG signal classification using DWT , 2015, Biomed. Signal Process. Control..

[4]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[5]  Patrick Haffner,et al.  Support vector machines for histogram-based image classification , 1999, IEEE Trans. Neural Networks.

[6]  Rimsha Iqbal,et al.  Effective Classification of EEG Signals Using K-Nearest Neighbor Algorithm , 2016, 2016 International Conference on Frontiers of Information Technology (FIT).

[7]  Gerhard Tröster,et al.  Eye Movement Analysis for Activity Recognition Using Electrooculography , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Babita,et al.  Binary movement classification of sEMG signal using linear SVM and Wavelet Packet Transform , 2016, 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES).

[9]  Guangyi Chen,et al.  Automatic EEG seizure detection using dual-tree complex wavelet-Fourier features , 2014, Expert Syst. Appl..

[10]  Touradj Ebrahimi,et al.  Support vector EEG classification in the Fourier and time-frequency correlation domains , 2003, First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings..

[11]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[12]  Onder Aydemir,et al.  A polynomial fitting and k-NN based approach for improving classification of motor imagery BCI data , 2010, Pattern Recognit. Lett..

[13]  Chun-Liang Hsu,et al.  EOG-based Human-Computer Interface system development , 2010, Expert Syst. Appl..

[14]  Brendan Z. Allison,et al.  The Hybrid BCI , 2010, Frontiers in Neuroscience.

[15]  Wyatt S. Newman,et al.  A human-robot interface based on electrooculography , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[16]  M. Yıldız,et al.  Comparison of different classification methods for the preictal stage detection in EEG signals , 2017 .

[17]  Touradj Ebrahimi,et al.  Classification of EEG signals using Dempster Shafer theory and a k-nearest neighbor classifier , 2009, 2009 4th International IEEE/EMBS Conference on Neural Engineering.

[18]  Onder Aydemir,et al.  Decision tree structure based classification of EEG signals recorded during two dimensional cursor movement imagery , 2014, Journal of Neuroscience Methods.

[19]  Manuel Mazo,et al.  Wheelchair Guidance Strategies Using EOG , 2002, J. Intell. Robotic Syst..

[20]  Y DengLawrence,et al.  EOG-based Human-Computer Interface system development , 2010 .

[21]  Mohd Nasir Taib,et al.  Comparison between KNN and ANN Classification in Brain Balancing Application via Spectrogram Image , 2012 .

[22]  G. Rashmi,et al.  Human Emotion Classification From EEG using SVM and K-NN Classifier For BCI , 2015 .