Evaluating EEG-Based Parameters for Bipolar Disorder Diagnosis Using a Synthetic Dataset

Richard Murdoch Montgomery

doi:10.20944/preprints202407.0633.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 7 July 2024 / Approved: 8 July 2024 / Online: 8 July 2024 (12:27:38 CEST)

This study explores the efficacy of using EEG-based parameters to diagnose bipolar disorder. A synthetic dataset was generated, including both correctly diagnosed and misdiagnosed cases, simulating realistic clinical conditions. EEG features such as theta-alpha mean, beta band mean, and coherence measures were used to train a multi-layer perceptron (MLP) model. The model achieved a validation accuracy of 92%, demonstrating strong potential for EEG-based diagnostics. However, challenges such as standardization of electrode configurations and addressing equipment differences are crucial for broader applicability and validity of the findings in diverse clinical settings.

Keywords: Biomarker; Bipolar Disorder; Computational Model; Multi-Layer Perceptron

Biology and Life Sciences, Biophysics

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Evaluating EEG-Based Parameters for Bipolar Disorder Diagnosis Using a Synthetic Dataset

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