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Cross-modal progressive modeling for neuro-visual representation learning.

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Bibliographic Details
Title:	Cross-modal progressive modeling for neuro-visual representation learning.
Authors:	Sun, Yueming¹ (AUTHOR), Pu, Jiyao¹ (AUTHOR), Sun, Kaili¹ (AUTHOR), Fu, Zeyu² (AUTHOR), Duan, Haoran³ (AUTHOR), Long, Yang¹ (AUTHOR) yang.long@durham.ac.uk
Source:	Neurocomputing. Jun2026, Vol. 683, pN.PAG-N.PAG. 1p.
Subjects:	Electroencephalography, Neurophysiology
Abstract:	Neural decoding from scalp signals requires models that respect spatial, temporal, and spectral structure while leveraging strong visual priors. In this paper, we introduce CFT-NET for disentangled neural visual representation together with a progressive visual–semantic adaptation (PVSA) framework that aligns EEG embeddings to pretrained visual backbones under a contrastive objective followed by pairwise matching. CFT-NET integrates Frequency-Separated Weights (FSW), Spatial-Context Aggregation (SCA), and Adaptive Temporal Filtering (ATF) to explicitly extract spectral, spatial, and temporal factors. PVSA consists of an instance-guided visual encoder and a visual-guided semantic decoder linked by cross attention, enabling fine-grained neuro–image interaction. On THINGS-EEG and THINGS-MEG datasets, the approach consistently outperforms state-of-the-art baselines in both subject-dependent and subject-independent zero-shot classification. By aligning model architecture with visual cognition principles and coupling it to strong visual priors, our method narrows the gap between neural activity and visual cognition, provides novel cross-modal neural decoding method that achieves competitive performance against recent state-of-the-art baselines. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Neural decoding from scalp signals requires models that respect spatial, temporal, and spectral structure while leveraging strong visual priors. In this paper, we introduce CFT-NET for disentangled neural visual representation together with a progressive visual–semantic adaptation (PVSA) framework that aligns EEG embeddings to pretrained visual backbones under a contrastive objective followed by pairwise matching. CFT-NET integrates Frequency-Separated Weights (FSW), Spatial-Context Aggregation (SCA), and Adaptive Temporal Filtering (ATF) to explicitly extract spectral, spatial, and temporal factors. PVSA consists of an instance-guided visual encoder and a visual-guided semantic decoder linked by cross attention, enabling fine-grained neuro–image interaction. On THINGS-EEG and THINGS-MEG datasets, the approach consistently outperforms state-of-the-art baselines in both subject-dependent and subject-independent zero-shot classification. By aligning model architecture with visual cognition principles and coupling it to strong visual priors, our method narrows the gap between neural activity and visual cognition, provides novel cross-modal neural decoding method that achieves competitive performance against recent state-of-the-art baselines. [ABSTRACT FROM AUTHOR]
ISSN:	09252312
DOI:	10.1016/j.neucom.2026.133450