Transferability of the EEG-based speech decoder to new participants

Determine whether the EEG-based speech decoding system trained via CLIP alignment between EEG and pre-trained audio embeddings using 175 hours of overt speech data from a single healthy participant can be transferred to other participants, and quantify the amount of participant-specific data required to enable such transferability through fine-tuning or adaptation.

Background

The study reports unprecedented zero-shot classification performance for EEG-based speech decoding by training an EEG encoder to align with pre-trained audio encoders using 175 hours of data from one participant. Despite these results, the dataset involves only a single individual, and the authors explicitly note uncertainty about generalizing the system to others.

They suggest that models pre-trained on large datasets often transfer with modest fine-tuning and hypothesize the same might hold for speech decoding, emphasizing the need to measure how much data is required for adaptation across individuals.

References

As such, it is unclear whether this system can be transferred to other participants.

— Scaling Law in Neural Data: Non-Invasive Speech Decoding with 175 Hours of EEG Data  (2407.07595 - Sato et al., 2024) in Section 6 Limitations

We suspect joint training on Subject 0 and Subjects 1--32 will improve generalisation on the latter subjects further, but leave this and other avenues of exploration to competition participants.

— The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100  (2609.03231 - Mantegna et al., 3 Sep 2026) in Section “Baselines, code, and material provided”