Abstract
The human brain activities’ research is one of the emerging research areas, and it is increasing rapidly from the last decade. This rapid growth is mainly due to the functional magnetic resonance imaging (fMRI). The fMRI is rigorously using in testing the theory about activation location of various brain activities and produces three-dimensional images related to the human subjects. In this paper, we studied about different classification learning methods to the problem of classifying the cognitive state of human subject based on fMRI data observed over single-time interval. The main goal of these approaches is to reveal the information represented in voxels of the neurons and classify them in relevant classes. The trained classifiers to differentiate cognitive state like (1) Does the subject watching is a word describing buildings, people, food (2) Does the subject is reading an ambiguous or non ambiguous sentence and (3) Does the human subject is a sentence or a picture etc. This paper summarizes the different classifiers obtained for above case studies to train classifiers for human brain activities.
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Rakesh, B., Kavitha, T., Lalitha, K., Thejaswi, K., Muppalaneni, N.B. (2019). Cognitive State Classifiers for Identifying Brain Activities. In: Computational Intelligence and Big Data Analytics. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-13-0544-3_2
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DOI: https://doi.org/10.1007/978-981-13-0544-3_2
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Online ISBN: 978-981-13-0544-3
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