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