# QEEGNet: Quantum Machine Learning for Enhanced Electroencephalography Encoding

arXiv: https://arxiv.org/abs/2407.19214 · HTML: https://mlsanity.com/paper/2407.19214/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2024.

## Built on by (most cited first, 13 of 13)

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- [Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning](https://mlsanity.com/paper/2605.06734/) (2026)
- [Exploring the Potential of QEEGNet for Cross-Task and Cross-Dataset Electroencephalography Encoding with Quantum Machine Learning](https://mlsanity.com/paper/2503.00080/) (2025)
- [Hybrid Quantum Deep Learning Model for Emotion Detection using raw EEG Signal Analysis](https://mlsanity.com/paper/2411.17715/) (2024)
- [Vision-QRWKV: Exploring Quantum-Enhanced RWKV Models for Image Classification](https://mlsanity.com/paper/2506.06633/) (2025)
- [Conditional Quantum Flow Matching for Data-Scarce Physiological Signal Augmentation](https://mlsanity.com/paper/2609.14019/) (2026)

## Abstract (arXiv)

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.