NeuroPhysNet: A FitzHugh-Nagumo-Based Physics-Informed Neural Network Framework for Electroencephalograph (EEG) Analysis and Motor Imagery Classification

TL;DR

NeuroPhysNet is a physics-informed neural network framework that leverages the FitzHugh-Nagumo model to improve EEG analysis and motor imagery classification, addressing noise, variability, and interpretability issues in clinical BCI applications.

Contribution

This paper introduces NeuroPhysNet, a novel PINN framework integrating biophysical neurodynamical models for enhanced EEG analysis and motor imagery classification.

Findings

Achieved higher accuracy than conventional methods on BCIC-IV-2a dataset.

Demonstrated robustness in data-limited and cross-subject scenarios.

Enhanced interpretability through biophysical model integration.

Abstract

Electroencephalography (EEG) is extensively employed in medical diagnostics and brain-computer interface (BCI) applications due to its non-invasive nature and high temporal resolution. However, EEG analysis faces significant challenges, including noise, nonstationarity, and inter-subject variability, which hinder its clinical utility. Traditional neural networks often lack integration with biophysical knowledge, limiting their interpretability, robustness, and potential for medical translation. To address these limitations, this study introduces NeuroPhysNet, a novel Physics-Informed Neural Network (PINN) framework tailored for EEG signal analysis and motor imagery classification in medical contexts. NeuroPhysNet incorporates the FitzHugh-Nagumo model, embedding neurodynamical principles to constrain predictions and enhance model robustness. Evaluated on the BCIC-IV-2a dataset, the framework achieved superior accuracy and generalization compared to conventional methods, especially in data-limited and cross-subject scenarios, which are common in clinical settings. By effectively integrating biophysical insights with data-driven techniques, NeuroPhysNet not only advances BCI applications but also holds significant promise for enhancing the precision and reliability of clinical diagnostics, such as motor disorder assessments and neurorehabilitation planning.