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

• URL: http://arxiv.org/abs/2303.14391v1
• Date: Sat, 25 Mar 2023 07:54:51 GMT
• Title: Multi-pooling 3D Convolutional Neural Network for fMRI Classification of Visual Brain States
• Authors: Zhen Zhang, Masaki Takeda and Makoto Iwata
• Abstract summary: 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.
• Score: 3.19429184376611
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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