Classifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithms-Reference-Cited by-同舟云学术

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Classifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithms

Published:2011-07-27 Issue:3 Volume:2 Page:125-134
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Boehm Omer,Hardoon David R.,Manevitz Larry M.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/content/pdf/10.1007/s13042-011-0030-3.pdf

Reference19 articles.

1. Cox D, Savoy R (2003) Functional magnetic resonance imaging (fmri) “brain reading”: detecting and classifying distributed patterns of fmri activity in human visual cortex. NeuroImage 19:261–270

2. Mitchell TM, Hutchison R, Niculescu RS, Pereira F, Wang X, Just M, Newman S (2004) Learning to decode cognitive states from brain images. Mach Learn 57:145–175

3. Mourao-Miranda J, Reynaud E, McGlone F, Calvert G, Brammer M (2006) The impact of temporal compression and space selection on svm analysis of single-subject and multi-subject fmri data. NeuroImage. doi: 10.1016/j.neuroimage.2006.08.016

4. Hardoon DR, Manevitz LM (2005) fMRI analysis via one-class machine learning techniques. In: Proceedings of the Nineteenth IJCAI, pp 1604–1605

5. Carlson TA, Schrater P, He S (2004) Patterns of activity in the categorical representations of objects. J Cog Neurosci 15(5):704–717

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