Exploring Quantum Machine Learning-Enhanced Models for EEG Data Classification

Abstract

Electroencephalography (EEG) records brain activity linked to both executed and imagined movements, but separating true motor signals from background noise in high-dimensional EEG data remains a challenge. Reliable classifiers are therefore vital for accurately tracking patient progress over time. This work is part of a larger initiative, the Smart NeuroRehab Ecosystem, which has two primary goals: (1) to propose innovative physical-rehabilitation strategies for neurologic conditions such as stroke using emerging technologies that make therapy more accessible, and (2) to collect and analyze EEG data using machine learning (ML) models that classify movement-related brain signals.

EEG data are complex and often difficult to interpret. In this research, we explore the use of quantum machine learning as an alternative approach for EEG signal classification. Compared to classical ML strategies, quantum methods may offer a fundamentally different way of representing and processing data, potentially improving classification performance or computational efficiency. We implement and analyze a ten-qubit Variational Quantum Classifier (VQC), and compare its performance to a tuned Random Forest baseline using EEG data from a publicly available 64-channel dataset. The task involves classifying each EEG time-window as either a movement or rest condition.

Across 40 preliminary runs, the VQC achieves a macro-F₁ score of approximately 0.75, accuracy of 0.76, and AUROC of 0.83, outperforming the Random Forest (macro-F₁ ≈ 0.71, AUROC ≈ 0.79). In addition to higher macro-F₁ and AUROC scores, the VQC also demonstrated significantly better precision and recall on the movement class, based on paired statistical tests. Most experiments were conducted on a quantum simulator, with a subset tested on a cloud-based quantum processor.

These findings suggest that hybrid quantum-classical models can match or exceed the performance of tuned classical pipelines without increasing computational complexity. Within the scope of the Smart NeuroRehab project, this work demonstrates that quantum approaches may offer a practical path to continuous monitoring of EEG in clinical settings. Future improvements in quantum hardware may expand the range of practical applications in biomedical signal analysis.