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Classification of Cognitive States from fMRI data using Fisher Discriminant Ratio and Regions of Interest
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 Title & Authors
Classification of Cognitive States from fMRI data using Fisher Discriminant Ratio and Regions of Interest
Do, Luu Ngoc; Yang, Hyung Jeong;
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In recent decades, analyzing the activities of human brain achieved some accomplishments by using the functional Magnetic Resonance Imaging (fMRI) technique. fMRI data provide a sequence of three-dimensional images related to human brain`s activity which can be used to detect instantaneous cognitive states by applying machine learning methods. In this paper, we propose a new approach for distinguishing human`s cognitive states such as "observing a picture" versus "reading a sentence" and "reading an affirmative sentence" versus "reading a negative sentence". Since fMRI data are high dimensional (about 100,000 features in each sample), extremely sparse and noisy, feature selection is a very important step for increasing classification accuracy and reducing processing time. We used the Fisher Discriminant Ratio to select the most powerful discriminative features from some Regions of Interest (ROIs). The experimental results showed that our approach achieved the best performance compared to other feature extraction methods with the average accuracy approximately 95.83% for the first study and 99.5% for the second study.
functional Magnetic Resonance Imaging;Regions of Interest;feature selection;Fisher Discriminant Ratio;
 Cited by
Classifying Instantaneous Cognitive States from fMRI using Discriminant based Feature Selection and Adaboost,;;;;

스마트미디어저널, 2016. vol.5. 1, pp.30-37
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