Classification of migraineurs using functional near infrared spectroscopy data

Sayıta, Yusuf
Classification of migraineur and healthy subjects using statistical pattern classifiers on functional Near Infrared Spectroscopy (NIRS) data is the main purpose of this study. Also a statistical comparison between trials that have different type of classifiers, classifier settings and feature sets is done. Features are extracted from raw light measurement data acquired with NIRS device, namely Niroxcope, during two separate previous studies, using Modified Beer-Lambert Law. After feature extraction, Naïve Bayes classifier and k Nearest Neighbor classifier are utilized with and with-out Principal Component Analysis in separate trials. Results obtained are compared within each other using statistical hypothesis tests namely Mc Nemar and Cochran Q.


An investigation of the neural correlates of purchase behavior through fNIRS
Çakır, Murat Perit; Girisken, Yener; Yurdakul, Dicle (2018-01-01)
Purpose This study aims to explore the plausibility of the functional near-infrared spectroscopy (fNIRS) methodology for neuromarketing applications and develop a neurophysiologically-informed model of purchasing behavior based on fNIRS measurements.
Classification of fMRI Data by Using Clustering
Moğultay, Hazal; Yarman Vural, Fatoş Tunay (2015-05-19)
Recognition of the the cognitive states by using functional Magnetic Rezonans Imaging (fMRI) data is a challenging problem that has been a focus of scientific research for a long time. In this study the effectiveness of clustering and the ensemble learning techniques on fMRI dataset is investigated and different paramaters are compared. Moreover, the performance of these techniques are tested on both raw voxel intensity values and meshes formed by multiple voxels. Clusters are compared to the functional bra...
Classification via ensembles of basic thresholding classifiers
TOKSÖZ, Mehmet Altan; Ulusoy, İlkay (Institution of Engineering and Technology (IET), 2016-08-01)
The authors present a sparsity-based algorithm, basic thresholding classifier (BTC), for classification applications which is capable of identifying test samples extremely rapidly and performing high classification accuracy. They introduce a sufficient identification condition (SIC) under which BTC can identify any test sample in the range space of a given dictionary. By using SIC, they develop a procedure which provides a guidance for the selection of threshold parameter. By exploiting rapid classification...
Classification of hyperspectral images based on weighted DMPs
Ulusoy, İlkay; MURA, Mauro Dalla (2012-07-27)
This paper presents a classification method for hyperspectral images utilizing Differential Morphological Profiles (DMPs) which permit to include in the analysis spatial information since they can provide an estimate of the size and contrast characteristics of the structures in an image. Due to the wide variety of objects present in a scene, the pixels belonging to the same semantic structure may not have homogeneous spatial and spectral features. In addition, instead of a single peak (which can be related ...
Eken, Aykut (2014-04-25)
Functional Near Infrared Spectroscopy is used in neuroimaging studies to observe the oxyhemoglobin (HB02) and deoxyhemoglobin (HB) changes. Blood oxygen level dependency (BOLD) signal that is collected by using this system shows response in related region in brain against an applied stimulus. Therefore in these signals, signal to noise ratio (SNR) is quite important to decide the behavior of brain in related region In this study, fNIRS data was filtered by using eigenvalue based methods such as Principal Co...
Citation Formats
Y. Sayıta, “Classification of migraineurs using functional near infrared spectroscopy data,” M.S. - Master of Science, Middle East Technical University, 2012.