Reading the Inner Voice Off the Scalp
A new arXiv paper decodes which words a participant is silently reading from 19-channel dry-electrode EEG, training a CNN–transformer with a CLIP-style objective against LLM embeddings. Performance scales log-linearly with data and shows no sign of saturation — suggesting the bottleneck is data volume, not architecture.
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