Generalization of patient-level performance gains to other patients

Ascertain how well the silent speech decoding performance improvements obtained in a single neurodegenerative disease patient, using models pretrained on large-scale EEG/EMG data from healthy participants, generalize to other patients, given limited availability of patient-specific data.

Background

The study reports substantial gains in word classification accuracy for one patient (from 13.2% baseline to 54.5%) by pretraining on large heterogeneous EEG/EMG datasets collected from healthy participants and then fine-tuning. However, because patient data are difficult to acquire, the authors note they could not establish whether these improvements generalize to additional patients.

This uncertainty emphasizes the need for broader patient datasets to evaluate cross-patient generalization of non-invasive silent speech decoders trained with heterogeneous electrode configurations.