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NECOMIMI: Neural-Cognitive Multimodal EEG-informed Image Generation with Diffusion Models

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arxiv 2410.00712 v2 pith:PDXJRTMZ submitted 2024-10-01 q-bio.NC cs.LG

Chi-Sheng Chen This is my paper · ORCID
classification q-bio.NCcs.LG
keywords generationdiffusionimageimagesmodelsnecomimichallengesclassification
verification ladder T0 review T1 audit T2 compute T3 formal
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NECOMIMI (NEural-COgnitive MultImodal EEG-Informed Image Generation with Diffusion Models) introduces a novel framework for generating images directly from EEG signals using advanced diffusion models. Unlike previous works that focused solely on EEG-image classification through contrastive learning, NECOMIMI extends this task to image generation. The proposed NERV EEG encoder demonstrates state-of-the-art (SoTA) performance across multiple zero-shot classification tasks, including 2-way, 4-way, and 200-way, and achieves top results in our newly proposed Category-based Assessment Table (CAT) Score, which evaluates the quality of EEG-generated images based on semantic concepts. A key discovery of this work is that the model tends to generate abstract or generalized images, such as landscapes, rather than specific objects, highlighting the inherent challenges of translating noisy and low-resolution EEG data into detailed visual outputs. Additionally, we introduce the CAT Score as a new metric tailored for EEG-to-image evaluation and establish a benchmark on the ThingsEEG dataset. This study underscores the potential of EEG-to-image generation while revealing the complexities and challenges that remain in bridging neural activity with visual representation.

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Cited by 5 Pith papers

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    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.

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    A DWT-gated transformer EEG encoder with CLIP alignment and category-aware clustering loss generates semantic images via a pre-trained diffusion model, achieving 43% max single-subject top-1 classification accuracy an...

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  4. Large Cognition Model: Towards Pretrained EEG Foundation Model

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

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

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    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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