QEEGNet: Quantum Machine Learning for Enhanced Electroencephalography Encoding

TL;DR

QEEGNet is a hybrid quantum-classical neural network designed to improve EEG signal encoding by leveraging quantum computing, showing promising results on benchmark datasets and robustness to noise, indicating potential for future neuroscience and clinical applications.

Contribution

This paper introduces QEEGNet, a novel hybrid quantum neural network architecture that integrates quantum layers into EEGNet to enhance EEG data analysis capabilities.

Findings

QEEGNet outperforms traditional EEGNet on most subjects in the benchmark dataset.

QEEGNet demonstrates robustness to noise in EEG data.

Quantum layers enable capturing more intricate EEG patterns.

Abstract

Electroencephalography (EEG) is a critical tool in neuroscience and clinical practice for monitoring and analyzing brain activity. Traditional neural network models, such as EEGNet, have achieved considerable success in decoding EEG signals but often struggle with the complexity and high dimensionality of the data. Recent advances in quantum computing present new opportunities to enhance machine learning models through quantum machine learning (QML) techniques. In this paper, we introduce Quantum-EEGNet (QEEGNet), a novel hybrid neural network that integrates quantum computing with the classical EEGNet architecture to improve EEG encoding and analysis, as a forward-looking approach, acknowledging that the results might not always surpass traditional methods but it shows its potential. QEEGNet incorporates quantum layers within the neural network, allowing it to capture more intricate patterns in EEG data and potentially offering computational advantages. We evaluate QEEGNet on a benchmark EEG dataset, BCI Competition IV 2a, demonstrating that it consistently outperforms traditional EEGNet on most of the subjects and other robustness to noise. Our results highlight the significant potential of quantum-enhanced neural networks in EEG analysis, suggesting new directions for both research and practical applications in the field.