QEEGNet is a hybrid neural network integrating quantum computing and the classical EEGNet architecture to enhance the encoding and analysis of EEG signals. By incorporating variational quantum circuits (VQC), QEEGNet captures more intricate patterns within EEG data, offering improved performance and robustness compared to traditional models.
This repository contains the implementation and experimental results for QEEGNet, evaluated on the BCI Competition IV 2a dataset.
- Hybrid Architecture: Combines the EEGNet convolutional framework with quantum encoding layers for advanced feature extraction.
- Quantum Layer Integration: Leverages the unique properties of quantum mechanics, such as superposition and entanglement, for richer data representation.
- Improved Robustness: Demonstrates enhanced accuracy and resilience to noise in EEG signal classification tasks.
- Generalizability: Consistently outperforms EEGNet across most subjects in benchmark datasets.
QEEGNet consists of:
- Classical EEGNet Layers: Initial convolutional layers process EEG signals to extract temporal and spatial features.
- Quantum Encoding Layer: Encodes classical features into quantum states using a parameterized quantum circuit.
- Fully Connected Layers: Converts quantum outputs into final classifications.
The BCI Competition IV 2a dataset was used for evaluation, featuring EEG signals from motor-imagery tasks.
- Subjects: 9
- Classes: Right hand, left hand, feet, tongue
- Preprocessing: Downsampled to 128 Hz, band-pass filtered (4-38 Hz).
For more details, refer to the dataset documentation.
Coming soon!
Hope this idea is helpful. I would appreciate you citing us in your paper, and the github.
@article{chen2024qeegnet,
title={Qeegnet: Quantum machine learning for enhanced electroencephalography encoding},
author={Chen, Chi-Sheng and Chen, Samuel Yen-Chi and Tsai, Aidan Hung-Wen and Wei, Chun-Shu},
journal={arXiv preprint arXiv:2407.19214},
year={2024}
}