Multiclass Prediction and Assisted Diagnosis of Mental Disorders Based on Eye Movement and Other Physiological Signals

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 448

Special Issue Editor


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Guest Editor
Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China
Interests: brain-computer interaction and neuroscience; convolutional neural networks; classification

Special Issue Information

Dear Colleagues,

This Special Issue explores the multiclass prediction and diagnosis of mental disorders via eye movement and other physiological signals. It involves leveraging technology to analyze eye movement, heart rate, skin conductance, and brain activity data to enhance the accuracy of diagnosing a variety of mental health conditions. By employing machine learning and artificial intelligence techniques, this research aims to differentiate between different mental disorders, such as depression, anxiety, schizophrenia, and more. This work may revolutionize mental healthcare by providing more precise and timely diagnoses, reducing the likelihood of misdiagnosis, and improving treatment planning. However, there are ethical and privacy concerns related to the collection and protection of sensitive physiological data. In summary, this represents an innovative approach that may make mental health assessment and diagnosis more effective and personalized, while simultaneously necessitating responsible data handling practices.

Dr. Quan Wang
Guest Editor

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Keywords

  • physiological signals
  • mental disorders
  • eye movement

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Published Papers (1 paper)

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Research

18 pages, 2703 KiB  
Article
Altered Effective Connectivity of the Attentional Network in Temporal Lobe Epilepsy with EEG Data
by Xiaojie Wei, Haojun Yang, Ruochen Dang, Bingliang Hu, Li Feng, Yuanyuan Xie and Quan Wang
Bioengineering 2025, 12(4), 387; https://doi.org/10.3390/bioengineering12040387 - 4 Apr 2025
Viewed by 83
Abstract
Existing studies have shown that the attentional function of epilepsy is prone to be impaired. However, the characterization of brain connectivity behind this impairment remains uncertain. This study investigates attention-related brain connectivity in 92 patients with temporal lobe epilepsy and 78 healthy controls [...] Read more.
Existing studies have shown that the attentional function of epilepsy is prone to be impaired. However, the characterization of brain connectivity behind this impairment remains uncertain. This study investigates attention-related brain connectivity in 92 patients with temporal lobe epilepsy and 78 healthy controls using a 32-channel EEG monitor during an attention network test. Compared to controls, patients showed reduced temporal–occipital connectivity in the alerting and orienting networks, but increased frontal–occipital connectivity in the executive network. Additionally, this study showed that patients and healthy individuals exhibited similar network topologies in the alerting and orienting networks, but the executive networks in patients showed altered topology properties, with a larger clustering coefficient in the theta band and a longer characteristic path length in the delta and theta bands. These findings reveal distinct characteristics of attention network connectivity in patients with temporal lobe epilepsy, offering valuable insights into the underlying mechanisms of epilepsy and providing clinical guidance for long-term monitoring and intervention. Full article
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