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<p>The purpose of this study is to develop a model utilizing resting-state EEG data acquired through dry electrodes to classify individuals with and without mild psychopathological symptoms for triage purposes. The proposed methodology uses a Random Forest model. Coherence connectivity was identified as the most predictive feature, achieving an accuracy of 73% in distinguishing between epochs. The classifier is applied to sequences of epoch-level predictions for each file, demonstrating file-level classification and achieving an accuracy of 86% on the test files. These findings highlight the potential of non-invasive, dry electrode EEG and Machine Learning approaches as scalable tools for mental health disorder classification and clinical triage.</p>

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Non-Invasive Classification of Mental Health Disorders Using Resting-State EEG with Dry Electrodes for Scalable Triage

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月1日

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  • Damian Jan,
  • Emma Rico,
  • Agustina Birba,
  • Yennifer Ravelo,
  • Melany León-Mendez,
  • Ksenia Travina,
  • Iván Padrón,
  • Manuel de Vega,
  • Hipólito Marrero

摘要

The purpose of this study is to develop a model utilizing resting-state EEG data acquired through dry electrodes to classify individuals with and without mild psychopathological symptoms for triage purposes. The proposed methodology uses a Random Forest model. Coherence connectivity was identified as the most predictive feature, achieving an accuracy of 73% in distinguishing between epochs. The classifier is applied to sequences of epoch-level predictions for each file, demonstrating file-level classification and achieving an accuracy of 86% on the test files. These findings highlight the potential of non-invasive, dry electrode EEG and Machine Learning approaches as scalable tools for mental health disorder classification and clinical triage.