EMS: A Large-Scale Eye Movement Dataset, Benchmark, and New Model for Schizophrenia Recognition

Yingjie Song , Zhi Liu , Gongyang Li

IEEE Transactions on Neural Networks and Learning Systems +

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August 23, 2024

View abstract on PubMed

Summary

This study introduces a large eye movement dataset for schizophrenia recognition and a novel MSNet model. The dataset and benchmark advance research in using eye-tracking for diagnosing schizophrenia.

Area Of Science

Neuroscience
Computer Science
Psychiatry

Background

Schizophrenia (SZ) is a prevalent mental illness often accompanied by cognitive deficits.
Eye-tracking technology offers a cost-effective method for characterizing cognitive impairments.

Purpose Of The Study

To address the lack of large-scale, publicly available eye movement datasets and benchmarks for schizophrenia recognition.
To introduce a novel deep learning model for improved eye movement-based schizophrenia detection.

Main Methods

A large-scale Eye Movement dataset for SZ recognition (EMS) was created, including data from 104 SZ patients and 104 healthy controls (HCs).
A comprehensive benchmark comparing 13 existing psychosis recognition methods was conducted.
A novel mean-shift-based network (MSNet) was proposed, integrating mean shift algorithm with convolution for feature extraction.

Main Results

The proposed MSNet demonstrated superior performance compared to existing methods in eye movement-based SZ recognition.
The EMS dataset and benchmark results provide a valuable resource for the research community.
MSNet effectively extracts subject features using a stimulus feature branch and a cluster center branch.

Conclusions

The EMS dataset and benchmark establish a foundation for future research in eye movement analysis for schizophrenia.
The MSNet model offers a powerful baseline for advancing the study of eye movement-based schizophrenia recognition.
Publicly releasing the dataset, benchmark results, and code facilitates further investigation and development in this field.