Abstract
A decoding process for fMRI data is constructed based on Multi-Voxel Pattern Analysis (MVPA) using deep learning method for online training process. The constructed process with Deep Brief Network (DBN) extracts the feature for classification on each ROI of input fMRI data. The decoding experiment results for hand motion show that the decoding accuracy based on DBN is comparable to that with the conventional process with batch training and that the divided feature extraction in the first layer decreases computational time without loss of accuracy. The constructed process should be necessary for interactive decoding experiments for each subject.
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Hatakeyama, Y., Yoshida, S., Kataoka, H., Okuhara, Y. (2014). Multi-Voxel Pattern Analysis of fMRI Based on Deep Learning Methods. In: Lee, K., Park, SJ., Lee, JH. (eds) Soft Computing in Big Data Processing. Advances in Intelligent Systems and Computing, vol 271. Springer, Cham. https://doi.org/10.1007/978-3-319-05527-5_4
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DOI: https://doi.org/10.1007/978-3-319-05527-5_4
Publisher Name: Springer, Cham
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