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

Zhen Zhang, Masaki Takeda, Makoto Iwata
3/25/2023
eess.IVcs.CVcs.LG

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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.

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Cite this paper

@article{zhang2023multipooling,
  title  = {Multi-pooling 3D Convolutional Neural Network for fMRI Classification of Visual Brain States},
  author = {Zhen Zhang and Masaki Takeda and Makoto Iwata},
  year   = {2023},
  eprint = {2303.14391},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.14391v1}
}

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