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.
@article{arxiv.2303.14391,
title = {Multi-pooling 3D Convolutional Neural Network for fMRI Classification of Visual Brain States},
author = {Zhen Zhang and Masaki Takeda and Makoto Iwata},
journal= {arXiv preprint arXiv:2303.14391},
year = {2023}
}