Dr. Sumeet Dua

Max P. & Robbie L. Watson Eminent Scholar Chair

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Priti Srinivasan (2008)

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Wavelet Based Approach for Detecting Cognitive States in fMRI Images; MS-Biomedical Engineering, Student: Priti Srinivasan (2008).

The functional Magnetic Resonance Imaging has evolved as a major tool for analyzing brain activity. Over the past few decades this has been used for detecting cognitive states of human subjects. Our main aim is to develop an algorithm that provides automated assessments of a patient’s cognitive state and provides decision support to clinicians in treatment planning for patients with various brain disorders. The fMRI data is high dimensional in nature and hence dimensionality reduction and feature extraction are two important steps for the representation of cognitive states for decision support analysis. The set of cognitive states that we are interested in classifying in this paper are ‘a person reading a sentence’ and ‘a person reading a picture.’ In this paper, we describe a unique slice based approach in which feature extraction is done by converting the data into frequency domain using Discrete Wavelet Transform (DWT) for getting novel features to represent a cognitive state. We believe that the frequency based approach captures the distinct trend associated with a particular cognitive state. Dimensionality reduction in frequency domain is done using Principal Component Analysis (PCA). The feature vector thus constructed is tested using different machine learning classifiers. Our results show good performance for multi-subject classification with much reduced dimensionality compared to most of the voxel based approaches.

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