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Multi-pooling 3D Convolutional Neural Network for fMRI Classification of Visual Brain States

Authors :
Zhang, Zhen
Takeda, Masaki
Iwata, Makoto
Publication Year :
2023

Abstract

Neural decoding of visual object classification via functional magnetic resonance imaging (fMRI) data is challenging and is vital to understand underlying brain mechanisms. This paper proposed a multi-pooling 3D convolutional neural network (MP3DCNN) to improve fMRI classification accuracy. MP3DCNN is mainly composed of a three-layer 3DCNN, where the first and second layers of 3D convolutions each have a branch of pooling connection. The results showed that this model can improve the classification accuracy for categorical (face vs. object), face sub-categorical (male face vs. female face), and object sub-categorical (natural object vs. artificial object) classifications from 1.684% to 14.918% over the previous study in decoding brain mechanisms.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2303.14391
Document Type :
Working Paper
Full Text :
https://doi.org/10.1109/CAI54212.2023.00057