title: A fuzzy integral method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification across multiple subjects.
abstract: The huge number of voxels in fMRI over time poses a major challenge to for effective analysis. Fast, accurate, and reliable classifiers are required for estimating the decoding accuracy of brain activities. Although machine-learning classifiers seem promising, individual classifiers have their own limitations. To address this limitation, the present paper proposes a method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification for application across multiple subjects. Similarly, the fuzzy integral (FI) approach has been employed as an efficient tool for combining different classifiers. The FI approach led to the development of a classifiers ensemble technique that performs better than any of the single classifier by reducing the misclassification, the bias, and the variance. The proposed method successfully classified the different cognitive states for multiple subjects with high accuracy of classification. Comparison of the performance improvement, while applying ensemble neural networks method, vs. that of the individual neural network strongly points toward the usefulness of the proposed method.
authors.affiliation: 1 Laser Center, Faculty of Science, Ibnu Sina Institute for Scientific Industrial Research (ISI-SIR), Universiti Teknologi, Malaysia.
authors.forename: L A
authors.initials: LA
authors.lastname: Cacha
authors.affiliation: 2 Carrier Software and Core Network Department, Huawei Technologies India Pvt Ltd Near EPIP Industrial Area, Whitefield Bangalore - 560 066, Karnataka, India.
authors.forename: S
authors.initials: S
authors.lastname: Parida
authors.affiliation: 3 Department of Systems Engineering, Ajou University, San 5, Woncheon-dong, Yeongtong-gu, Suwon 443-749, South Korea.
authors.forename: S
authors.initials: S
authors.lastname: Dehuri
authors.affiliation: 4 Department of Computer Science, Yonsei University Seoul, South Korea.
authors.forename: S-B
authors.initials: SB
authors.lastname: Cho
authors.affiliation: 5 Department of Clinical Sciences, Faculty of Biosciences and Medical Engineering, Universiti Teknologi, Malaysia 81310 Johor Bahru, Johor, Malaysia.
authors.forename: R R
authors.initials: RR
authors.lastname: Poznanski
mesh_terms: D001921:Brain / Q000502:physiology*; D003071:Cognition / Q000502:physiology*; D066264:Datasets as Topic; D017143:Fuzzy Logic; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016571:Neural Networks, Computer; D009483:Neuropsychological Tests; D014796:Visual Perception / Q000502:physiology
keywords: FMRI; artificial neural network; classifier ensemble; cognitive states; fuzzy integral; machine learning