Deprecated: The each() function is deprecated. This message will be suppressed on further calls in /home/zhenxiangba/zhenxiangba.com/public_html/phproxy-improved-master/index.php on line 456 Paper page - CineBrain: A Large-Scale Multi-Modal Brain Dataset During Naturalistic
Audiovisual Narrative Processing
https://jianxgao.github.io/CineBrain.\n","updatedAt":"2025-03-12T12:54:57.980Z","author":{"_id":"643815c4961bb61e463c5896","avatarUrl":"/avatars/3b44592472f16c56105bff8c314d9939.svg","fullname":"Jianxiong Gao","name":"Jianxiong","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":4,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8141717314720154},"editors":["Jianxiong"],"editorAvatarUrls":["/avatars/3b44592472f16c56105bff8c314d9939.svg"],"reactions":[],"isReport":false}},{"id":"67d2362ec5c8af10496b6433","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":318,"isUserFollowing":false},"createdAt":"2025-03-13T01:34:38.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [A Survey of fMRI to Image Reconstruction](https://huggingface.co/papers/2502.16861) (2025)\n* [A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond](https://huggingface.co/papers/2502.12048) (2025)\n* [Large Cognition Model: Towards Pretrained EEG Foundation Model](https://huggingface.co/papers/2502.17464) (2025)\n* [MagicInfinite: Generating Infinite Talking Videos with Your Words and Voice](https://huggingface.co/papers/2503.05978) (2025)\n* [MindSimulator: Exploring Brain Concept Localization via Synthetic FMRI](https://huggingface.co/papers/2503.02351) (2025)\n* [BP-GPT: Auditory Neural Decoding Using fMRI-prompted LLM](https://huggingface.co/papers/2502.15172) (2025)\n* [BrainGuard: Privacy-Preserving Multisubject Image Reconstructions from Brain Activities](https://huggingface.co/papers/2501.14309) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"
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Recognizing the complementary strengths of EEG's high temporal\nresolution and fMRI's deep-brain spatial coverage, CineBrain provides\napproximately six hours of narrative-driven content from the popular television\nseries The Big Bang Theory for each of six participants. Building upon this\nunique dataset, we propose CineSync, an innovative multimodal decoding\nframework integrates a Multi-Modal Fusion Encoder with a diffusion-based Neural\nLatent Decoder. Our approach effectively fuses EEG and fMRI signals,\nsignificantly improving the reconstruction quality of complex audiovisual\nstimuli. To facilitate rigorous evaluation, we introduce Cine-Benchmark, a\ncomprehensive evaluation protocol that assesses reconstructions across semantic\nand perceptual dimensions. Experimental results demonstrate that CineSync\nachieves state-of-the-art video reconstruction performance and highlight our\ninitial success in combining fMRI and EEG for reconstructing both video and\naudio stimuli. 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CineSync, a multimodal decoding framework using reinforcement learning with a diffusion-based neural latent decoder, achieves state-of-the-art performance in reconstructing complex audiovisual stimuli from EEG and fMRI data.
AI-generated summary
In this paper, we introduce CineBrain, the first large-scale dataset
featuring simultaneous EEG and fMRI recordings during dynamic audiovisual
stimulation. Recognizing the complementary strengths of EEG's high temporal
resolution and fMRI's deep-brain spatial coverage, CineBrain provides
approximately six hours of narrative-driven content from the popular television
series The Big Bang Theory for each of six participants. Building upon this
unique dataset, we propose CineSync, an innovative multimodal decoding
framework integrates a Multi-Modal Fusion Encoder with a diffusion-based Neural
Latent Decoder. Our approach effectively fuses EEG and fMRI signals,
significantly improving the reconstruction quality of complex audiovisual
stimuli. To facilitate rigorous evaluation, we introduce Cine-Benchmark, a
comprehensive evaluation protocol that assesses reconstructions across semantic
and perceptual dimensions. Experimental results demonstrate that CineSync
achieves state-of-the-art video reconstruction performance and highlight our
initial success in combining fMRI and EEG for reconstructing both video and
audio stimuli. Project Page: https://jianxgao.github.io/CineBrain.
In this paper, we introduce CineBrain, the first large-scale dataset featuring simultaneous EEG and fMRI recordings during dynamic audiovisual stimulation. Recognizing the complementary strengths of EEG's high temporal resolution and fMRI's deep-brain spatial coverage, CineBrain provides approximately six hours of narrative-driven content from the popular television series The Big Bang Theory for each of six participants. Building upon this unique dataset, we propose CineSync, an innovative multimodal decoding framework integrates a Multi-Modal Fusion Encoder with a diffusion-based Neural Latent Decoder. Our approach effectively fuses EEG and fMRI signals, significantly improving the reconstruction quality of complex audiovisual stimuli. To facilitate rigorous evaluation, we introduce Cine-Benchmark, a comprehensive evaluation protocol that assesses reconstructions across semantic and perceptual dimensions. Experimental results demonstrate that CineSync achieves state-of-the-art video reconstruction performance and highlight our initial success in combining fMRI and EEG for reconstructing both video and audio stimuli. Project Page: https://jianxgao.github.io/CineBrain.