Abstract
Mental arithmetic is a basic cognitive function of human brain, mental arithmetic is an important cognitive function of brain, which is also considered as the core of human logical thinking. fMRI provides convenience for mental arithmetic cognitive function research because of its non-invasiveness and convenience, More and more experiments are devoted to the clear understanding of mental arithmetic, and the classification of cognitive tasks will contribute to a more comprehensive understanding of the behavior of organisms and the decoding of neural signals. We recruited 21 subjects and took block design of fMRI in the experiment, In this paper, seeds are extracted and characterized by partial correlation connection, build functional brain network (FBN). At present, the deep learning technology represented by CNN is increasingly applied in the analysis and classification of neural image data, CNN was used to classify the brain functional connectivity network, at the same time, with several other machine learning methods classifying mental arithmetic comparison effects and analyzed. The classification results show that in the data-driven classification, CNN-based method have the best classification effect, reaching 98%, which is more obvious than the traditional machine learning method.
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Liu, R., Zhong, N., Zhang, X., Yang, Y., Huang, J. (2019). Classification of Mental Arithmetic Cognitive States Based on CNN and FBN. In: Liang, P., Goel, V., Shan, C. (eds) Brain Informatics. BI 2019. Lecture Notes in Computer Science(), vol 11976. Springer, Cham. https://doi.org/10.1007/978-3-030-37078-7_22
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DOI: https://doi.org/10.1007/978-3-030-37078-7_22
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