Alljoined is making human thought legible to AI through non-invasive BCI.

Today, we are introducing NOVA (Neural Observations of Video-evoked Affect), a large-scale dataset of emotion-evoking videos paired with EEG recordings.

Today, we are formally introducing Alljoined-1.6M, a large EEG-image dataset comprising over 1.6 million visual stimulus trials collected from 20 participants on consumer-grade hardware.

Today, we are introducing ENIGMA (EEG Neural Image Generator for Multi-subject Applications). ENIGMA is a new multi-subject decoding model that reconstructs seen images from EEG signals with state-of-the-art accuracy.

ENIGMA is a new multi-subject decoding model that reconstructs seen images from EEG signals with state-of-the-art accuracy. By learning a shared “visual language” across different brains, the model can adapt to a new user in minutes rather than hours, bringing practical Brain-Computer Interfaces (BCIs) closer to reality.

We introduce the largest public dataset for EEG-image decoding: 6+ hours of recordings per participant across 20 subjects, collected using consumer-grade hardware. We model neural scaling laws and demonstrate log-linear scaling.

MindEye2 introduces a multi-subject fMRI-to-image architecture that trains 40× faster than previous state-of-the-art while matching performance. Accepted to ICML.

The first and only dataset for complex mental imagery reconstruction from fMRI. Accepted to CVPR (Highlight paper).