Inter-Subject MEG Decoding for Visual Information with Hybrid Gated Recurrent Network

Jingcong Li,Fei Wang,Jiahui Pan,Zhuliang Yu

doi:10.3390/app11031215

Jingcong Li, Fei Wang + Show 2 more

Open Access

https://doi.org/10.3390/app11031215

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Abstract

As an effective brain signal recording technique for neuroscience, magnetoencephalography (MEG) is widely used in cognitive research. However, due to the low signal-to-noise ratio and the structural or functional variabilities of MEG signals between different subjects, conventional methods perform poorly in decoding human brain responds. Inspired by deep recurrent network for processing sequential data, we applied the gated recurrent units for MEG signals processing. In the paper, we proposed a hybrid gated recurrent network (HGRN) for inter-subject visual MEG decoding. Without the need of any information from test subjects, the HGRN effectively distinguished MEG signals evoked by different visual stimulations, face and scrambled face. In the leave-one-out cross-validation experiments on sixteen subjects, our method achieved better performance than many existing methods. For more in-depth analysis, HGRN can be utilized to extract spatial features and temporal features of MEG signals. These features conformed to the previous cognitive studies which demonstrated the practicality of our method for MEG signal processing. Consequently, the proposed model can be considered as a new tool for decoding and analyzing brain MEG signal, which is significant for visual cognitive research in neuroscience.

Highlights

Human brain has an excellent visual system that the main content of a scene can be captured in less than one second
To deal with the spatial-temporal structure of MEG signal, we proposed a hybrid gated recurrent network (HGRN) which is a modified recurrent network based on gate recurrent units (GRU)
The corresponding recall, precision and F1 scores are 71%, 72% and 71%, respectively. These results demonstrated that the HGRN model can evenly distinguish the positive samples and negative samples (i.e., MEG signals evoked by face images and scrambled face images)

Summary

Introduction

Human brain has an excellent visual system that the main content of a scene can be captured in less than one second. As an important research aspect in neuroscience, it is significant to record brain signals and decode visual information from these signals [1]. We are able to understand the high-level recognition abilities and diagnose the psychological illness. Magnetoencephalography (MEG) is an advanced technique for recording brain magnetic signals within hundreds of channels. Based on the MEG technique, neuroscientists are able to study brain function in-depth [2,3,4,5]. The sampling rate of MEG signal can be quite high that it can record brain magnetic signals in milliseconds. MEG signals can be used to study the dynamic changes of brain function [6,7,8]

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