Abstract
This paper describes an experiment involving visual object fMRI brain data and the NeuCube [1] architecture. fMRI spatio- and spectro- temporal data (SSTD), apart from EEG, audio and video data, comprises both space and time information, that requires a specific and specialized architecture to process, interpret and visualize the data for better understanding and interpretation of the information it may carries. At the same time, any patterns can be better recognized and thus new knowledge that may be embedded within the pattern can be extracted. From the experiment with the case study of Haxby fMRI data, NeuCubeB has accomplished better accuracy in recognizing the brain patterns compared with the standard machine learning techniques (i.e. SVM and MLP). In addition, the NeuCube method assists deep learning of the SSTD and deeper analysis of the spatio-temporal characteristics and patterns in the fMRI SSTD.
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Acknowledgements
This experiment is part of a project supported by the EU FP7 Marie Curie project EvoSpike PIIF-GA-2010-272006, supported by the Institute for Neuroinformatics at ETH/UZH Zurich (http://ncs.ethz.ch/projects/evospike), as well as by the Knowledge Engineering and Discovery Research Institute (KEDRI, http://www.kedri.info) of the Auckland University of Technology and the New Zealand Ministry of Science and Innovation. This work also is supported by TIER 1 No. H100 as Graduate Research Assistant (GRA) of University Tun Hussein Onn Malaysia (UTHM).
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Murli, N., Kasabov, N., Paham, N.A. (2020). eSNN for Spatio-Temporal fMRI Brain Pattern Recognition with a Graphical Object Recognition Case Study. In: Ghazali, R., Nawi, N., Deris, M., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2020. Advances in Intelligent Systems and Computing, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-030-36056-6_44
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DOI: https://doi.org/10.1007/978-3-030-36056-6_44
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