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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

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Zero-Shot Neural Decoding with Semi-Supervised Multi-View Embedding.

Yusuke Akamatsu¹, Keisuke Maeda², Takahiro Ogawa²

¹Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Hokkaido, Japan.

Sensors (Basel, Switzerland)

|August 12, 2023

Summary

This study introduces a novel semi-supervised multi-view embedding method for zero-shot neural decoding from fMRI data. The approach improves decoding accuracy for untrained image categories by addressing the projection domain shift problem.

Keywords:

Bayesian inference functional magnetic resonance imaging (fMRI)generative model multi-view learning neural decoding probabilistic model semi-supervised learning zero-shot learning

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Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Area of Science:

Neuroscience
Machine Learning
Computer Vision

Background:

Zero-shot neural decoding aims to identify image categories from brain activity (fMRI) without prior training data.
Insufficient fMRI data leads to poor model generalization and the projection domain shift problem for novel categories.

Purpose of the Study:

To propose a novel zero-shot neural decoding approach using semi-supervised multi-view embedding.
To address the projection domain shift problem and enhance generalization capability.

Main Methods:

Utilized a semi-supervised approach incorporating additional related images without fMRI data.
Projected fMRI activity patterns into a multi-view embedding space (visual and semantic features).

Main Results:

The proposed method effectively rectifies the projection domain shift problem.
Experimental results show superior performance compared to existing zero-shot neural decoding methods.

Conclusions:

Semi-supervised multi-view embedding is a promising strategy for improving zero-shot neural decoding.
This approach enhances the ability to decode novel image categories from fMRI data.