The Problem and My Idea

The problem. Brain-to-image datasets are huge and growing, and the only way we compress them today is generic lossy compression that optimizes how the waveform looks. That throws away the part that actually matters: what the person saw. And there’s no way to search the data. A lab with decades of recordings can’t ask “find the trials where the subject saw a face.”

My idea: embeddings. Instead of storing the raw signal, store an embedding: a short vector that captures the meaning of the data, not its pixels.

The clearest example is faces. Face recognition already works this way. A face gets turned into a vector (a few hundred numbers), and identity lives in the geometry of that space: the same person’s faces land close together, different people land far apart. You never store the photo. You store the embedding, and that tiny vector is enough to recognize, match, or search the face.

Where I want to take it. My idea is that the same trick should work for the “what you saw” signal in a brain recording. If I can map an EEG epoch into a latent space that captures meaning (the kind of space face and image models already live in), then I could:

• Store it tiny. Keep the compact embedding instead of the full signal.
• Search it by meaning. Type a word, find the matching recordings, because text and these embeddings can share a space.
• Compress against meaning, so what survives is what the data is for, not the loudest part of the waveform.

This is the direction I want to take it: treat embeddings as the storage format, the way we already do for faces, and apply it to the brain.