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Mind's Eye: Image Recognition by EEG via Multimodal Similarity-Keeping Contrastive Learning

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arxiv 2406.16910 v1 pith:QCCKR5MA submitted 2024-06-05 eess.SP cs.AIcs.HCcs.LGq-bio.NC

Chi-Sheng Chen , Chun-Shu Wei This is my paper · ORCID
classification eess.SPcs.AIcs.HCcs.LGq-bio.NC
keywords contrastiveimagevisualaccuracybrainclassificationhumanlearning
verification ladder T0 review T1 audit T2 compute T3 formal
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Decoding images from non-invasive electroencephalographic (EEG) signals has been a grand challenge in understanding how the human brain process visual information in real-world scenarios. To cope with the issues of signal-to-noise ratio and nonstationarity, this paper introduces a MUltimodal Similarity-keeping contrastivE learning (MUSE) framework for zero-shot EEG-based image classification. We develop a series of multivariate time-series encoders tailored for EEG signals and assess the efficacy of regularized contrastive EEG-Image pretraining using an extensive visual EEG dataset. Our method achieves state-of-the-art performance, with a top-1 accuracy of 19.3% and a top-5 accuracy of 48.8% in 200-way zero-shot image classification. Furthermore, we visualize neural patterns via model interpretation, shedding light on the visual processing dynamics in the human brain. The code repository for this work is available at: https://github.com/ChiShengChen/MUSE_EEG.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. EEG2TEXT-CN: An Exploratory Study of Open-Vocabulary Chinese Text-EEG Alignment via Large Language Model and Contrastive Learning on ChineseEEG

    cs.CL 2025-06 reject novelty 6.0 of 10

    A Chinese EEG-to-text system that aligns 128-channel EEG with per-character text embeddings achieves BLEU-1 6.38% on a held-out subject, claimed as the first open-vocabulary EEG-to-Chinese decoder.

  2. Decoding Visual Neural Representations by Multimodal with Dynamic Balancing

    cs.CV 2025-09 conditional novelty 4.0 of 10

    A multimodal EEG-image-text contrastive framework with dynamic gradient balancing and stochastic noise improves zero-shot object recognition from EEG on ThingsEEG, raising top-1 accuracy from 13.8% to 15.8%.

  3. Large Cognition Model: Towards Pretrained EEG Foundation Model

    eess.SP 2025-02 conditional novelty 4.0 of 10

    LCM, a transformer EEG model combining contrastive alignment and masked reconstruction, reports state-of-the-art balanced accuracy on BCIC-2A and BCIC-2B.

  4. Quantum-Enhanced Natural Language Generation: A Multi-Model Framework with Hybrid Quantum-Classical Architectures

    quant-ph 2025-08 reject novelty 3.0 of 10

    A benchmark of QASA, QRWKV, and QKSAN against Transformer and MLP on five tiny datasets, with results that contradict the paper's own tables.

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