EIT-1M: One Million EEG-Image-Text Pairs for Human Visual-textual Recognition and More
<|reference_start|>EIT-1M: One Million EEG-Image-Text Pairs for Human Visual-textual Recognition and More: Recently, electroencephalography (EEG) signals have been actively incorporated to decode brain activity to visual or textual stimuli and achieve object recognition in multi-modal AI. Accordingly, endeavors have been focused on building EEG-based datasets from visual or textual single-modal stimuli. However, these datasets offer limited EEG epochs per category, and the complex semantics of stimuli presented to participants compromise their quality and fidelity in capturing precise brain activity. The study in neuroscience unveils that the relationship between visual and textual stimulus in EEG recordings provides valuable insights into the brain's ability to process and integrate multi-modal information simultaneously. Inspired by this, we propose a novel large-scale multi-modal dataset, named EIT-1M, with over 1 million EEG-image-text pairs. Our dataset is superior in its capacity of reflecting brain activities in simultaneously processing multi-modal information. To achieve this, we collected data pairs while participants viewed alternating sequences of visual-textual stimuli from 60K natural images and category-specific texts. Common semantic categories are also included to elicit better reactions from participants' brains. Meanwhile, response-based stimulus timing and repetition across blocks and sessions are included to ensure data diversity. To verify the effectiveness of EIT-1M, we provide an in-depth analysis of EEG data captured from multi-modal stimuli across different categories and participants, along with data quality scores for transparency. We demonstrate its validity on two tasks: 1) EEG recognition from visual or textual stimuli or both and 2) EEG-to-visual generation.<|reference_end|>
@article{zheng2024eit-1m:,
    title={EIT-1M: One Million EEG-Image-Text Pairs for Human Visual-textual
  Recognition and More},
    author={Xu Zheng, Ling Wang, Kanghao Chen, Yuanhuiyi Lyu, Jiazhou Zhou, Lin
  Wang},
    journal={arXiv preprint arXiv:2407.01884},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01884},
    primaryClass={cs.CV cs.HC}
}