Fig. 4. Design considerations for speech BCIs.

a, Word error rate as a function of language model vocabulary size, obtained by reprocessing the 50-word-set RNN outputs with language models of increasingly large vocabulary size. Word error rates were aggregated over the 250 available trials (50 for each of the five evaluation days). The shaded region indicates 95% CI (computed by bootstrap resampling across trials, n = 10,000 resamplings). b, Word error rate as a function of the number of electrodes included in an offline decoding analysis (each filled circle represents the average word error rate of RNNs trained with that number of electrodes, and each thin line shows s.d. across ten RNNs). There appears to be a log-linear relationship between the number of electrodes and performance, such that doubling the electrode count cuts word error rate by nearly half (factor of 0.57; dashed line represents the log-linear relationship fit with least squares). c, Evaluation data from the five vocalized speech-evaluation days were reprocessed offline using RNNs trained in the same way, but with fewer (or no) training sentences taken from the day on which performance was evaluated. Word error rates averaged across ten RNN seeds (blue line) are reasonable even when no training sentences are used from evaluation day (that is, when training on previous days’ data only). The shaded region shows 95% CI across the ten RNN seeds (bootstrap resampling method, n = 10,000 resamplings). The dashed line represents online performance for reference (23.8% word error rate). d, The correlation (Pearson r) in neural activity patterns representing a diagnostic set of words is plotted for each pair of days, showing high correlations for nearby days.