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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References
Ahmad RF, Malik AS, Kamel N, Reza F (2015) Object categories specific brain activity classification with simultaneous EEG-fMRI. IEEE, Piscataway
Mitchell et al (2004) Learning to decode cognitive states from brain images. Kluwer Academic Publishers, Dordrecht
Tom M, Mitchell et al (2008) Predicting human brain activity associated with the meanings of nouns. Science 320:1191. https://doi.org/10.1126/science.1152876
Rieger et al (2008) Predicting the recognition of natural scenes from single trial MEG recordings of brain activity.
Taghizadeh-Sarabi M, Daliri MR, Niksirat KS (2014) Decoding objects of basic categories from electroencephalographic signals using wavelet transform and support vector machines. Brain topography, pp 1–14
Miyapuram KP, Schultz W, Tobler PN (2013) Predicting the imagined contents using brain activation. In: Fourth national conference on computer vision pattern recognition image processing and graphics (NCVPRIPG) 2013, pp 1–3
Haxby JV, Gobbini MI, Furey ML, Ishai A, Schouten JL, Pietrini P (2001) Distributad and overlapping representations of faces and objects in ventral temporal cortex. Science 293:2425–2430
Cox DD, Savoy RL (2003) Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage 19:261–270
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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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Publisher Name: Springer, Singapore
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Online ISBN: 978-981-13-0544-3
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