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August 25, 2025Open Access

EEG Classification for Neurological Disorders Using Frequency Band Deciles

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Authors

JFJonah FernandezBIBianca InnocentiBLBeatriz López

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Overview

This analysis demonstrates improved classification of Alzheimer's and Parkinson's diseases using EEG signals, suggesting robust performance in diverse settings.

Key Points

  • The decile-based method achieved high classification accuracy in detecting neurological disorders, improving diagnostic potential.
  • Experimental evaluation showed that Random Forest and KNN models performed best, especially in classifying Alzheimer's and Parkinson's diseases.
  • The approach utilized machine learning techniques, effectively processing EEG signals for enhanced feature extraction and analysis.
  • Potential applications in wearable EEG systems highlight its utility for early diagnosis in resource-limited environments.

Cite This Study

Fernandez et al. (2025) studied this question.

synapsesocial.com/papers/68af5d63ad7bf08b1eae07e7https://doi.org/10.21203/rs.3.rs-7084929/v1
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Classification of EEG Signals Based on Pattern Recognition Approach2017 · 218 citations
  2. 2EEG classification for neurological disorders using frequency band deciles2025 · 3 citations
  3. 3Comprehensive EEG Signal Feature Extraction for Neurological Disorder Diagnosis: Focus on Alzheimer's, Parkinson's, and Seizure Disorders2024
  4. 4Classification of Neurodegenerative Diseases Using Machine Learning: An Approach Focused on Alzheimer's and Frontotemporal Dementia2026
  5. 5Features importance in seizure classification using scalp EEG reduced to single timeseries2021