arXiv Machine Learning July 14, 2026 By Pierre-Louis Barbarant, Florent Meyniel, Bertrand Thirion

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

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

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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.

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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

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arXiv AI

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Flattening the Connectome Spectrum: A Spectral Filter for FC Induces a Pretraining Target for fMRI Encoders

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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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