arXiv Machine Learning

Fast Whole-Brain, Geometry-Aware Functional Alignment for Cross-Subject Decoding

arXiv:2607. 10931v1 Announce Type: cross Abstract: Decoding brain activity is useful for characterizing brain processes and understanding the functional architecture underlying cognition.

arXiv AI

Jun 16

Task-guided cross-subject latent alignment: a multi-encoder-decoder VAE

arXiv:2606. 15989v1 Announce Type: cross Abstract: Aligning neural activity across subjects offers the promise of discovering shared computational principles and generalizable decoders.

By Angeliki Papathanasiou, Jascha Achterberg, Thomas E. Nichols, Rui Ponte Costa

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arXiv AI

Jun 16

Learning aligned EEG representations with subject-specific encoders

arXiv:2606. 16462v1 Announce Type: cross Abstract: Cross-subject EEG decoding promises more training data, but it also exposes neural networks to strong inter-subject distribution shifts.

By Bruna J. Lopes, Gabriel Schwartz, Sylvain Chevallier, Raphael Y. de Camargo, Bruno Aristimunha

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arXiv AI

Jun 9

NeuroAlign: Hierarchical Multimodal Fusion of Dynamic and Structural Neuroimaging for MCI Analysis

arXiv:2606. 07635v1 Announce Type: cross Abstract: Multimodal neuroimaging fusion of functional MRI (fMRI) and diffusion tensor imaging (DTI) provides complementary information for cognitive impairment analysis, but remains challenged by heterogeneous feature spaces and misaligned representations.

By Xiongri Shen, Zhenxi Song, Jiaqi wang, Yi Zhong, Leilei Zhao, Chenqi Xu, Linling Li, Yichen Wei, Lingyan Liang, Demao Deng, Luping Song, Ping Luan, Ahmed M. Anter, Shuqiang Wang, Baiying Lei, Zhiguo Zhang

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arXiv Machine Learning

Jul 23

Geometry-Guided Generative Representation for Functional Brain Graphs

arXiv:2511. 04539v2 Announce Type: replace-cross Abstract: In network neuroscience, functional brain systems are often characterized using separate yet related graph-theoretic or spectral descriptors, overlooking how these properties covary and partially overlap across individuals and conditions.

By Subati Abulikemu, Tiago Azevedo, Michail Mamalakis, John Suckling

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arXiv Computer Vision

Aug 24

Deep Models, Shallow Alignment: Uncovering the Granularity Mismatch in Neural Decoding

arXiv:2601.21948v2 Announce Type: replace Abstract: Neural visual decoding is a central problem in brain-computer interface research, aiming to reconstruct human visual perception and to elucidate th...

By Yang Du, Siyuan Dai, Yonghao Song, Paul M. Thompson, Haoteng Tang, Liang Zhan

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arXiv Machine Learning

Jul 10

Geometry-Aware Deep Congruence Networks for Manifold Learning in Cross-Subject Motor Imagery

arXiv:2511. 18940v3 Announce Type: replace Abstract: Cross-subject motor imagery decoding remains a fundamental challenge in EEG-based brain-computer interfaces due to substantial inter-subject variability.

By Sanjeev Manivannan, Chandra Shekar Lakshminarayan

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arXiv AI

Jul 1

Resolving superposition in AI for interpretability and cross-modal alignment in patient-neuronal images

arXiv:2606. 31394v1 Announce Type: cross Abstract: Artificial intelligence is transforming our capability to solve biological challenges.

By Jisung Park, Seohyeon Kang, Daeun Yoo, Eunsu Lee, Seoin Cho, Wooyeop Choi, Ian Choi, James R. Evan, Daesoo Kim, Sonia Gandhi, Minee L. Choi

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arXiv Computation and Language

Sep 11

Cross-lingual brain-language model alignment is robust but challenges hierarchical and computational accounts

The study examined whether brain-language model alignment reflects shared computational mechanisms or merely stable lexical‑semantic correspondences. Using whole‑brain encoding across Mandarin, English, and French, transformer representations predicted activity in a distributed network that overlapped across languages and remained stable across layers. Contextual embeddings and measures of prediction or compression did not outperform static lexical embeddings, suggesting that alignment is robust but not informative about shared computational processes.

By Ni Yang, Rui He, Philipp Homan, Iris Sommer, Davide Staub, Wolfram Hinzen

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arXiv Machine Learning

Sep 1

MEL: Coordinate-Preserving EEG Tokenization for fMRI Translation

arXiv:2608.29304v1 Announce Type: new Abstract: Translating electroencephalography (EEG) into functional magnetic resonance imaging (fMRI) is important for medical neuroimaging, clinical brain-state...

By Xiangyu Liu, Zeting Yan, Zhitong Yin, Boyang Li, Xi Zhang

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arXiv Machine Learning

Jun 30

BrainRiem: Riemannian Prototype Learning for Source-Free Cross-Site Brain Network Diagnosis

arXiv:2606. 29200v1 Announce Type: new Abstract: Multi-site functional MRI (fMRI) studies are essential for robust neuropsychiatric diagnosis yet suffer severe domain shifts from scanner heterogeneity, demographics, and site-specific acquisition protocols.

By Kunyu Zhang, Tianxiang Xu

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arXiv Machine Learning

Aug 4

STEAM:ASpatio-TEmporal Alignment Mixture-of-Experts Model with Hierarchical Pre-training for EEG Decoding

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

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arXiv AI

Jul 23

Cross-Subject Semantic Decoding with Shared-Space Alignment for Generalized Neural Representation Learning

arXiv:2607. 19394v1 Announce Type: cross Abstract: Generalizing across subjects remains challenging in invasive neural recordings because electrode configurations, anatomical structures, and neural signal patterns vary substantially across individuals.

By Ji-Hoon Heo, Aleksandra Joanna Wisniewska, Seo-Hyun Lee, Seong-Whan Lee

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