evelabs
/

morphism

@@ -62,15 +62,6 @@ You will see a real-time retrieval of documents from your index that the model t
 ## How was this created?
-```
-raw EEG (16ch, 1kHz)
-  → windowed segments (~0.6s)
-    → convolutional autoencoder (64-dim bottleneck)
-      → semantic VAE (→ 1024-dim text embedding space)
-        → FAISS nearest-neighbor search
-          → matching text from your corpus
-```
 This model works in two stages. The first stage is an autoencoder which represents the neural data in a latent space. The second stage is a semantic mapper, which guesses a semantic vector from the neural vector. This relatively simple architecture is surprisingly effective and lays the groundwork for future developments of this technology.
 The underlying dataset represents a large collection of paired neural measurements and text stimuli, collected by Eve Labs over a period of 20 months on approximately forty subjects. Training data was gathered naturalistically; subjects chatted with LLMs while wearing the headset, with the conversation text serving as paired stimuli.

 ## How was this created?
 This model works in two stages. The first stage is an autoencoder which represents the neural data in a latent space. The second stage is a semantic mapper, which guesses a semantic vector from the neural vector. This relatively simple architecture is surprisingly effective and lays the groundwork for future developments of this technology.
 The underlying dataset represents a large collection of paired neural measurements and text stimuli, collected by Eve Labs over a period of 20 months on approximately forty subjects. Training data was gathered naturalistically; subjects chatted with LLMs while wearing the headset, with the conversation text serving as paired stimuli.