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Abstract <p>Parkinson’s disease (PD) is a prevalent neurodegenerative disorder worldwide, often progressing to mild cognitive impairment (MCI) and dementia. Clinical diagnosis of PD mainly depends on characteristic motor symptoms, which can lead to misdiagnosis, underscoring the need for reliable biomarkers. Early detection of PD and effective monitoring of disease progression are crucial for enhancing patient outcomes. Electroencephalogram (EEG) signals, as non-invasive neural recordings, show great promise as diagnostic biomarkers. In this study, we present a novel approach for PD diagnosis through the analysis of EEG signals from distinct brain regions. We used two publicly available EEG datasets and constructed three-dimensional (3D) time-frequency spectrograms for each brain region using the continuous wavelet transform (CWT). To improve feature representation, these spectrograms were encoded in the red-green-blue (RGB) color space. A ResNet18 model was trained separately on the spectrograms of each brain region, and its performance was assessed using the leave-one-subject-out cross-validation (LOSOCV) method. The proposed method achieved classification accuracies of 92.86% and 90.32% on the two datasets, respectively. The experimental results confirm the efficacy of our approach, highlighting its potential as a valuable tool to aid clinical diagnosis of PD.</p> Graphical abstract <p></p>

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Brain region localization: a rapid Parkinson’s disease detection method based on EEG signals

  • 期刊论文

  • 发表日期：

    2025年7月9日

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  • Mingliang Zhang,
  • Hang Liu,
  • Zhenghao Guo,
  • Cui Wang,
  • Timo Hamalainen,
  • Fengyu Cong

摘要

Abstract

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder worldwide, often progressing to mild cognitive impairment (MCI) and dementia. Clinical diagnosis of PD mainly depends on characteristic motor symptoms, which can lead to misdiagnosis, underscoring the need for reliable biomarkers. Early detection of PD and effective monitoring of disease progression are crucial for enhancing patient outcomes. Electroencephalogram (EEG) signals, as non-invasive neural recordings, show great promise as diagnostic biomarkers. In this study, we present a novel approach for PD diagnosis through the analysis of EEG signals from distinct brain regions. We used two publicly available EEG datasets and constructed three-dimensional (3D) time-frequency spectrograms for each brain region using the continuous wavelet transform (CWT). To improve feature representation, these spectrograms were encoded in the red-green-blue (RGB) color space. A ResNet18 model was trained separately on the spectrograms of each brain region, and its performance was assessed using the leave-one-subject-out cross-validation (LOSOCV) method. The proposed method achieved classification accuracies of 92.86% and 90.32% on the two datasets, respectively. The experimental results confirm the efficacy of our approach, highlighting its potential as a valuable tool to aid clinical diagnosis of PD.

Graphical abstract