CLASSIFICATION OF FUNCTIONAL NEAR-INFRARED IMAGING BASED HEMODYNAMIC PATTERNS RECORDED AT MENTAL ARITHMETİC AND RESTING

Year 2018, Volume: 13 Issue: 1, 27 - 36, 19.01.2018

Funda Kutlu Onay Cemal Köse

Abstract

Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical
imaging technique used in brain-computer interface (BCI) systems. It is used to
measure deoxyhemoglobin and oxyhemoglobin proportions that occur during a
specific activity in the brain region (motor and visual activity, auditory
stimulus, etc.). In this study, hemodynamic patterns were recorded from 8
participants during mental arithmetic and rest activities. Features have been
extracted for this by using detrended fluctuation analysis, entropy and Hjorth
parameters methods. The distinctive feature vectors obtained after the feature
selection process have been applied to support vector machines (SVM),
multilayer artificial neural networks (MLANN) and k-nearest neighbors (k-NN)
classifiers. As a result, the best classification accuracy was 97.17% when SVM
classifier was used.

Keywords

Functional Near-Infrared Spectroscopy, Brain-Computer Interface, Detrended Fluctuation Analysis, Hjorth Parameters, Support Vector Machines

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