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Towards Neural Foundation Models for Vision: Aligning EEG, MEG, and fMRI Representations for Decoding, Encoding, and Modality Conversion

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arxiv 2411.09723 v1 pith:YRBW57AF submitted 2024-11-14 cs.CV cs.AI

Matteo Ferrante , Tommaso Boccato , Grigorii Rashkov , Nicola Toschi This is my paper · ORCID
classification cs.CVcs.AI
keywords neuraldatadecodingencodingacrossaligningbrainconversion
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper presents a novel approach towards creating a foundational model for aligning neural data and visual stimuli across multimodal representationsof brain activity by leveraging contrastive learning. We used electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) data. Our framework's capabilities are demonstrated through three key experiments: decoding visual information from neural data, encoding images into neural representations, and converting between neural modalities. The results highlight the model's ability to accurately capture semantic information across different brain imaging techniques, illustrating its potential in decoding, encoding, and modality conversion tasks.

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Cited by 3 Pith papers

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    Fine-tuning CLIP with human behavioral embeddings and dynamic MEG responses yields models that better predict human similarity judgments and track individual neural dynamics over time.

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  3. Foundation Models for Cross-Domain EEG Analysis Application: A Survey

    cs.HC 2025-08 conditional novelty 4.0 of 10

    A survey that organizes EEG foundation-model research into five output-modality categories: native EEG, text, vision, audio, and multimodal fusion, with a claim to be the first such comprehensive taxonomy.

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