Hankel Subspace Self‑Supervised Reconstruction (HSSRecon) is a scan‑specific framework for parallel MRI that partitions data by acquisition units before Hankel lifting and normalizes repeated Hankel copies. Instead of predicting missing k‑space directly, the network learns a compact complex‑valued Hankel subspace operator, and reconstruction is performed with a conjugate‑gradient solver enforcing hard data consistency. The method provides theoretical guarantees on uniqueness and error bounds, and demonstrates competitive performance on fastMRI brain data across multiple contrasts and sampling masks.
By Mingyu Hu, Siquan Zhu, Xijun Zhong, Qiegen Liu
BrainWideBench is a benchmark that evaluates across‑animal transfer on multi‑region neural recordings from 139 mice, covering 276 brain regions. It comprises three task suites—behavior decoding, neural activity prediction, and anatomical organization recovery—to test whether learned representations support diverse downstream objectives. The benchmark shows that while pretraining improves performance over single‑session baselines, current methods vary in transfer ability and none perform uniformly well across all suites, highlighting the challenge of developing general‑purpose neural representations.
By Alexandre Andre, Shivashriganesh P. Mahato, Vinam Arora, Keshav Balaji, Divyansha Lachi, Nanda H. Krishna, Jingyun Xiao, Yizi Zhang, Ximeng Mao, Wenrui Ma, Han Yu, International Brain Laboratory, Daniel Birman, Niccol\`o Bonacchi, Gaelle A. Chapuis, Joana A. Catarino, Felicia Davatolhagh, Mayo Faulkner, Laura Freitas-Silva, Fei Hu, Julia M. Huntenburg, Anup Khanal, In\^es Laranjeira, Petrina Lau, Guido T. Meijer, Nathaniel J. Miska, Jean-Paul Noel, Alejandro Pan-Vazquez, Georg Raiser, Cyrille Rossant, Karolina Z. Socha, Anne E. Urai, Miles J. Wells, Steven J. West, Olivier Winter, Blake Richards, Guillaume Lajoie, Cole Hurwitz, Mehdi Azabou, Matthew R. Whiteway, Liam Paninski, Eva L. Dyer
Hankel Subspace Self-Supervised Reconstruction (HSSRecon) is a scan‑specific framework for parallel MRI that partitions data by acquisition units before Hankel lifting and normalizes repeated Hankel copies. Instead of directly predicting missing k‑space data, the network learns a compact complex‑valued Hankel subspace operator, and reconstruction is performed with a conjugate‑gradient solver enforcing hard data consistency. The method is theoretically justified with proofs of positive definiteness, uniqueness, and error bounds, and demonstrates competitive performance on fastMRI brain data across multiple contrasts and sampling masks.
arXiv:2603. 19100v2 Announce Type: replace Abstract: Electroencephalography (EEG) enables non-invasive monitoring of brain activity across clinical and neurotechnology applications, yet building foundation models for EEG remains challenging due to differing electrode topologies and computational scalability, as Transformer architectures incur quadratic sequence complexity.
By Dana\'e Broustail, Anna Tegon, Thorir Mar Ingolfsson, Yawei Li, Luca Benini
arXiv:2608. 02070v1 Announce Type: cross Abstract: Brain-computer interfaces (BCIs) have been widely used in motor rehabilitation, disease diagnosis, and other neural engineering scenarios.
By Zhu Chen, Dingkun Liu, Yuheng Chen, Dongrui Wu