Summary of Cognitioncapturer: Decoding Visual Stimuli From Human Eeg Signal with Multimodal Information, by Kaifan Zhang et al.
CognitionCapturer: Decoding Visual Stimuli From Human EEG Signal With Multimodal Information
by Kaifan Zhang, Lihuo He, Xin Jiang, Wen Lu, Di Wang, Xinbo Gao
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed CognitionCapturer framework is a unified approach that fully leverages multimodal data to represent EEG signals, which have been largely overlooked in recent studies. The framework trains Modality Expert Encoders for each modality to extract cross-modal information from the EEG modality, and then uses a diffusion prior to map the EEG embedding space to the CLIP embedding space. This allows for the reconstruction of visual stimuli with high semantic and structural fidelity, without requiring any fine-tuning of generative models or incorporating additional modalities. Through extensive experiments, CognitionCapturer outperforms state-of-the-art methods both qualitatively and quantitatively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EEG signals can reveal important information about what we’re thinking and feeling. But most studies only look at how EEG signals relate to visual images. The proposed framework, called CognitionCapturer, tries to capture more information from EEG by looking at multiple types of data together. It uses special “encoders” for each type of data to find connections between them. This helps the framework create realistic pictures that match what’s going on in our brains. The results are really promising and show that this approach can do better than other methods. |
Keywords
» Artificial intelligence » Diffusion » Embedding space » Fine tuning
