Alljoined1 -- A dataset for EEG-to-Image decoding
<|reference_start|>Alljoined1 -- A dataset for EEG-to-Image decoding: We present Alljoined1, a dataset built specifically for EEG-to-Image decoding. Recognizing that an extensive and unbiased sampling of neural responses to visual stimuli is crucial for image reconstruction efforts, we collected data from 8 participants looking at 10,000 natural images each. We have currently gathered 46,080 epochs of brain responses recorded with a 64-channel EEG headset. The dataset combines response-based stimulus timing, repetition between blocks and sessions, and diverse image classes with the goal of improving signal quality. For transparency, we also provide data quality scores. We publicly release the dataset and all code at https://linktr.ee/alljoined1.<|reference_end|>
@article{xu2024alljoined1,
    title={Alljoined1 -- A dataset for EEG-to-Image decoding},
    author={Jonathan Xu, Bruno Aristimunha, Max Emanuel Feucht, Emma Qian, Charles
  Liu, Tazik Shahjahan, Martyna Spyra, Steven Zifan Zhang, Nicholas Short, Jioh
  Kim, Paula Perdomo, Ricky Renfeng Mao, Yashvir Sabharwal, Michael Ahedor Moaz
  Shoura, Adrian Nestor},
    journal={arXiv preprint arXiv:2404.05553},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05553},
    primaryClass={q-bio.NC cs.AI}
}