TECH Signal 503
Open-vocabulary word decoding from non-invasive EEG during silent reading shown above chance with no saturation
A single-participant study using 19-channel dry-electrode EEG and a CLIP-style contrastive decoder demonstrated that open-vocabulary word-level information is recoverable from brain activity during silent reading, with decoding accuracy scaling log-linearly with training-data volume and showing no sign of saturation.
The result suggests that non-invasive brain-computer interfaces for decoding language from EEG are bottlenecked by data availability rather than signal quality, at least for the proxy task of silent reading. The use of dry electrodes and a contrastive learning objective aligned with LLM embeddings points toward a scalable methodology if multi-participant data can be collected. The finding that decoding is data-limited rather than saturated implies that more training data should continue to improve performance.
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A contrastive decoder trained on approximately 240,000 word presentations from one participant across 49 hours of 19-channel dry-electrode EEG retrieved open-vocabulary words above chance in top-10 retrieval against permutation baselines.
Decoding accuracy scaled log-linearly with training-data volume with no sign of saturation, indicating the approach is data-limited rather than capped by signal quality.
Removing occipital and posterior-temporal electrodes reduced the word-level decoding gain by roughly one third but left narrative context tracking unchanged, suggesting partially separable neural sources for word identity and context.
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