Javascript must be enabled for the correct page display

Using a Support-Vector Machine for the analysis of fMRI images in a tactile frequency-discrimination task

Marsman, J.B.C. (2006) Using a Support-Vector Machine for the analysis of fMRI images in a tactile frequency-discrimination task. Master's Thesis / Essay, Artificial Intelligence.

[img]
Preview
Text
AI_Ma_2006_JBCMarsman.CV.pdf - Published Version

Download (1MB) | Preview

Abstract

Traditionally, fMRI analysis uses a univariate method for statistical testing, such as the General Linear Model. In the past few years multivariate approaches have been developed also taking spatial relations between voxels into account, including Support-Vector Machines (SVMs). An SVM is a binary classifier which calculates the optimal hyperplane (decision boundary) between two classes in a training set. This hyperplane is then used to predict the class of the items in a test set. The goal of this study was to find correlates between human discrimination ability and the performance of the SVM on the captured IMRI images from this discrimination task. The task was to discriminate between different vibrotactile frequencies, where it is still an open question as to whether neural firing rate or synchronicity is used. fMRI signal intensity changes as a function of firing rate, but not synchronicity, and so could inform this question. We conducted an IMRI experiment where vibrotactile stimuli were applied to the right index finger. consisting of a reference frequency and a stimulus frequency. After each pair of stimuli, the subject had to respond with either higher or lower, using a button box held in the left hand. Beside standard fMRI preprocessing and stimulus-related averaging over each session, we used either Singular Value Decomposition or feature selection methods to decrease computational time. The results led to the conclusion that frequency is not spatially represented. This still leaves rate encoding and periodicity encoding to be possible representations of frequency.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:30
Last Modified: 15 Feb 2018 07:30
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/9088

Actions (login required)

View Item View Item