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Lightweight Transformer for EEG Classification via Balanced Signed Graph Algorithm Unrolling
Junyi Yao, Parham Eftekhar, Gene Cheung, Xujin Chris Liu, Yao Wang, Wei Hu
https://arxiv.org/abs/2510.03027 …

Lightweight Transformer for EEG Classification via Balanced Signed Graph Algorithm Unrolling
Samples of brain signals collected by EEG sensors have inherent anti-correlations that are well modeled by negative edges in a finite graph. To differentiate epilepsy patients from healthy subjects using collected EEG signals, we build lightweight and interpretable transformer-like neural nets by unrolling a spectral denoising algorithm for signals on a balanced signed graph -- graph with no cycles of odd number of negative edges. A balanced signed graph has well-defined frequencies that map to…

Replaced article(s) found for eess.IV. https://arxiv.org/list/eess.IV/new
[1/1]:
- Robust MRI Reconstruction by Smoothed Unrolling (SMUG)
Shijun Liang, Van Hoang Minh Nguyen, Jinghan Jia, Ismail Alkhouri, Sijia Liu, Saiprasad Ravishankar

UTOPY: Unrolling Algorithm Learning via Fidelity Homotopy for Inverse Problems
Roman Jacome, Romario Gualdr\'on-Hurtado, Leon Suarez-Rodriguez, Henry Arguello
https://arxiv.org/abs/2509.14394

UTOPY: Unrolling Algorithm Learning via Fidelity Homotopy for Inverse Problems
Imaging Inverse problems aim to reconstruct an underlying image from undersampled, coded, and noisy observations. Within the wide range of reconstruction frameworks, the unrolling algorithm is one of the most popular due to the synergistic integration of traditional model-based reconstruction methods and modern neural networks, providing an interpretable and highly accurate reconstruction. However, when the sensing operator is highly ill-posed, gradient steps on the data-fidelity term can hinde…

Physics-Informed Deep Unrolled Network for Portable MR Image Reconstruction
Efe Il{\i}cak, Chinmay Rao, Chlo\'e Najac, Beatrice Lena, Baris Imre, Fernando Galve, Joseba Alonso, Andrew Webb, Marius Staring
https://arxiv.org/abs/2509.11790

Physics-Informed Deep Unrolled Network for Portable MR Image Reconstruction
Magnetic resonance imaging (MRI) is the gold standard imaging modality for numerous diagnostic tasks, yet its usefulness is tempered due to its high cost and infrastructural requirements. Low-cost very-low-field portable scanners offer new opportunities, while enabling imaging outside conventional MRI suites. However, achieving diagnostic-quality images in clinically acceptable scan times remains challenging with these systems. Therefore methods for improving the image quality while reducing th…