Classifying Instantaneous Cognitive States from fMRI using Discriminant based Feature Selection and Adaboost
0
조회
0
다운로드
3
인용
0
참고문헌
키워드
초록
In recent decades, the study of human brain function has dramatically increased thanks to the advent of Functional Magnetic Resonance Imaging. This is a powerful tool which provides a deep view of the activities of the brain. From fMRI data, the neuroscientists analyze which parts of the brain have responsibility for a particular action and finding the common pattern representing each state involved in these tasks. This is one of the most challenges in neuroscience area because of noisy, sparsity of data as well as the differences of anatomical brain structure of each person. In this paper, we propose the use of appropriate discriminant methods, such as Fisher Discriminant Ratio and hypothesis testing, together with strong boosting ability of Adaboost classifier. We prove that discriminant methods are effective in classifying cognitive states. The experiment results show significant better accuracy than previous works. We also show that it is possible to train a successful classifier without prior anatomical knowledge and use only a small number of features
Abstract
In recent decades, the study of human brain function has dramatically increased thanks to the advent of Functional Magnetic Resonance Imaging. This is a powerful tool which provides a deep view of the activities of the brain. From fMRI data, the neuroscientists analyze which parts of the brain have responsibility for a particular action and finding the common pattern representing each state involved in these tasks. This is one of the most challenges in neuroscience area because of noisy, sparsity of data as well as the differences of anatomical brain structure of each person. In this paper, we propose the use of appropriate discriminant methods, such as Fisher Discriminant Ratio and hypothesis testing, together with strong boosting ability of Adaboost classifier. We prove that discriminant methods are effective in classifying cognitive states. The experiment results show significant better accuracy than previous works. We also show that it is possible to train a successful classifier without prior anatomical knowledge and use only a small number of features
참고문헌 (13)
- T. Mitchell, "Classifying Instantaneous Cognitive States from fMRI Data", American Medical Informatics Association Symposium (2003)
- 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)
- Hojen-Sorensen, "Bayesian modeling of fMRI time series", Proc. Conf. Advances in Neural Information Processing Systems, pp.754~760 (1999), NIPS
- Cox, D.D., "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)
- Wagner, A. D., "Building memories: Remembering and forgetting of verbal experiences as predicted by brain activity", Science, 281, pp.1188~1191 (1998)
- T. M. Mitchell, "Learning to Decode Cognitive States from Brain Images", Machine Learning, 57(1-2), pp.145~175 (2004)
- M.T.T.Hoang, "Cognitive States Detection in fMRI Data Analysis using incremental PCA", ICCSA, pp.335~341 (2007)
- Luu Ngoc Do, "Classification of Cognitive States from fMRI data using Fisher Discriminant Ratio and Regions of Interest", International Journal of Contents, 8(4), pp.55~62 (2012), 한국콘텐츠학회[DOI]
- Michael Collins, "Logistic regression, AdaBoost and Bregman distances", Machine Learning, 48(1), pp.253~265 (2002)
- Freund, Y., "A Decision -Theoretic Generalization of On-Line Learning and an Application to Boosting", Journal of Computer and System Sciences, 55, pp.119~139 (1997)
- Rademacher, J., "Human celebral cortex: localization, parcellation, and morphometry with magnetic resonance imaging", J. Cogn. Neurosci, 4, pp.352~374 (1992)
- Etzel, J.A., "An introduction to anatomical ROI-based fMRI classification analysis", Brain Res, 1282, pp.114~125 (2009)
- Sergios Theodoridis, "Konstantinos Koutroumbas: Pattern Recognition", 273~274 (2009)