Classification of Cognitive States from fMRI data using Fisher Discriminant Ratio and Regions of Interest
0
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
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.
Abstract
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.
참고문헌 (20)
- J. Ford, "Patient Classification of fMRI Activation Maps", LNCS, 2879, pp.58~65 (2003)
- M. A Lindquist, "The Statistical Analysis of fMRI Data", Statistical Science, 28, pp.439~464 (2008)
- R. A. Poldrack, "Handbook of functional MRI data analysis" (2011), Cambridge University Press
- T. M. Mitchell, "Classifying Instantaneous Cognitive States from fMRI data", American Medical Informatics Association Symposium, pp.465~469 (2003)
- T. M. Mitchell, "Learning to decode Cognitive States from Brain Images", Machine Learning, 57, pp.145~175 (2004)
- B. M. Bly, "When you have a General Linear Hammer, every fMRI time-series looks like independent identically distributed nails", Concepts and Methods in NeuroImaging Workshop (2001)
- K. J. Friston, "Statistical parametric maps in functional imaging: A general linear approach", Human Brain Mapping, 2, pp.189~210 (1995)
- P.A.d.F.R. Højen-Sørensen, "Bayesian modeling of fMRI time series", Proc. Conf. Advances in Neural Information Processing Systems, pp.754~760 (1999), NIPS
- T. Jung, "Imaging Brain dynamics using Independent Component Analysis", Proc. IEEE, 89, pp.1107~1122 (2001)
- T. Jung, "Analysis of fMRI data by blind separation into independent spatial components", Human Brain Mapping, 6, pp.160~188 (1998)
- J. V. Haxby, "Distributed and overlapping representations of faces and objects in ventral temporal cortex", Science, 293, pp.2425~2430 (2001)
- D. D. Cox, "Functional magnetic resonance imaging (fMRI) “brain reading”: Detecting and classifying distributed patterns of fMRI activity in human visual cortex", NeuroImage, 19, pp.261~270 (2003)
- M. T. T. Hoang, "Cognitive States Detection in fMRI Data Analysis using incremental PCA", ICCSA, pp.335~341 (2007)
- F. Yong, "Detecting Cognitive States from fMRI Images by Machine Learning and Multivariate Classification", Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)
- J. A. Etzel, "An introduction to anatomical ROI-based fMRI classification analysis", Brain Research, 1282, pp.114~125 (2009)
- R. S. Bapi, "Detection of Cognitive States from fMRI data using Machine Learning Techniques", IJCAI, pp.587~592 (2007)
- N. Bernard, "Generalized Sparse Classifiers for Decoding Cognitive States in fMRI", Proceedings of the First international conference on Machine learning in medical imaging, pp.108~115 (2010)
- J. Rademacher, "Human celebral cortex: Localization, parcellation, and morphometry with magnetic resonance imaging", Journal of Cognitive Neuroscience, 4, pp.352~374 (1992)
- S. Theoridis, "Introduction to Pattern Recognition – A MATLAB Approach" (2009), Academic Press
- P. Tan, "Introduction to Data Mining" (2006), Pearson Addison Wesley