EEG data from 6 subjects recorded while viewing 2,000 images across 40 object classes sourced from ImageNet. The dataset was created by author luigi-s for the paper 'Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion Models' and was last updated on Hugging Face in October 2024. It is designed for use in ControlNet scenarios for linking brain activity to visual stimuli.
Use Cases
- Training EEG-to-image generation models based on the described latent diffusion framework
- Researching visual classification from brain signals based on the 40 object categories
- Developing ControlNet architectures for conditional generation based on EEG data
- Analyzing subject-specific neural responses to visual stimuli across 6 individuals
Strengths
- Includes EEG data from 6 distinct subjects, allowing for individual variability analysis
- Stimuli consist of 2,000 images spanning 40 object classes from the established ImageNet dataset
- Dataset is directly linked to a published arXiv paper and associated code repository
Limitations
- Column-level documentation is absent; field semantics must be inferred after download
- Row count, file formats, and license information are unknown, which may limit suitability assessment
Provenance
- Source
- Hugging Face dataset uploaded by author luigi-s.
- Collection Method
- EEG recordings from subjects viewing ImageNet images, as described in the associated arXiv paper.
- Time Range
- null
- Freshness
- Last updated 2024-10-30 12:27:20; freshness should be verified
- Geography
- null