Alljoined/Alljoined-1.6M

Alljoined 1.6M

• Homepage: https://alljoined.com
• Repository: https://github.com/Alljoined/Alljoined-1.6M
• Paper: https://arxiv.org/abs/2508.18571
• Point of Contact: [email protected]

We present a new large-scale electroencephalography (EEG) dataset as part of the THINGS initiative, comprising over 1.6 million visual stimulus trials collected from 20 participants, and totaling more than twice the size of the most popular current benchmark dataset, THINGS-EEG2. Crucially, our data was recorded using a 32-channel consumer-grade wet electrode system costing ~$2.2k, around 27x cheaper than research-grade EEG systems typically used in cognitive neuroscience labs. Our work is one of the first open-source, large-scale EEG resource designed to closely reflect the quality of hardware that is practical to deploy in real-world, downstream applications of brain-computer interfaces (BCIs). We aim to explore the specific question of whether deep neural network-based BCI research and semantic decoding methods can be effectively conducted with such affordable systems, filling an important gap in current literature that is extremely relevant for future research. In our analysis, we not only demonstrate that decoding of high-level semantic information from EEG of visualized images is possible at consumer-grade hardware, but also that our data can facilitate effective EEG-to-Image reconstruction even despite significantly lower signal-to-noise ratios. In addition to traditional benchmarks, we also conduct analyses of EEG-to-Image models that demonstrate log-linear decoding performance with increasing data volume on our data, and discuss the trade-offs between hardware cost, signal fidelity, and the scale of data collection efforts in increasing the size and utility of currently available datasets. Our contributions aim to pave the way for large-scale, cost-effective EEG research with widely accessible equipment, and position our dataset as a unique resource for the democratization and development of effective deep neural models of visual cognition.

Licensing Information

In exchange for permission to use the Alljoined-1.6M database (the "Database") at Alljoined, Researcher hereby agrees to the following terms and conditions:

1. Researcher shall use the Dataset only for non-commercial research and educational purposes, in accordance with Alljoined's Terms of Use (https://www.alljoined.com/terms-of-use).
2. Alljoined and Hugging Face make no representations or warranties regarding the Dataset, including but not limited to warranties of non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Dataset and shall defend and indemnify Alljoined and Hugging Face, including their employees, officers and agents, against any and all claims arising from Researcher's use of the Dataset.
4. Researcher shall comply with Alljoined's Privacy Policy (https://www.alljoined.com/privacy-policy) and ensure that any use of the Dataset respects the privacy rights of individuals whose data may be included.
5. Researcher may provide research associates and colleagues with access to the Dataset provided that they first agree to be bound by these terms and conditions.
6. Alljoined and Hugging Face reserve the right to terminate Researcher's access to the Dataset at any time.
7. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
8. The law of the State of California shall apply to all disputes under this agreement.

Citation Information

@misc{xu2025alljoined16mmilliontrialeegimagedataset,
      title={Alljoined-1.6M: A Million-Trial EEG-Image Dataset for Evaluating Affordable Brain-Computer Interfaces}, 
      author={Jonathan Xu and Ugo Bruzadin Nunes and Wangshu Jiang and Samuel Ryther and Jordan Pringle and Paul S. Scotti and Arnaud Delorme and Reese Kneeland},
      year={2025},
      eprint={2508.18571},
      archivePrefix={arXiv},
      primaryClass={q-bio.NC},
      url={https://arxiv.org/abs/2508.18571}, 
}