Fig. 5: Across subject neural representation stability via multi-subject transfer learning.

A Pre-articulatory zero-shot decoding performances from single and group (n = 5) subject models. B Group model trained on pre-articulatory activity from 1 to 5 subjects (n) with dense sensorimotor cortex coverage significantly improves decoding performance when transferred to a participant with frontotemporal electrodes and no sensorimotor cortex coverage. C Heatmap of %∆PER improvement across 20 subjects with variable electrode applying transfer learning with a recurrent layer trained on increasingly more participants in a specific set and order with the group-based model (n = 1:5). D Peak %∆ improvement in decoding accuracy (PER) for each subject in the cohort, along with the optimal number of subjects required in the group model to achieve this performance. ** 5A) Box plots (center/bounds/whiskers): Single Subject Models—S1: 0.52/0.50–0.54/0.46–0.57, S2: 0.51/0.49–0.53/0.44–0.59, S3: 0.52/0.50–0.54/0.46–0.60, S4: 0.53/0.48–0.56/0.40–0.58, S5: 0.53/0.51–0.55/0.44–0.60; Group Models—S1: 0.46/0.40–0.49/0.34–0.52, S2: 0.46/0.43–0.49/0.34–0.52, S3: 0.47/0.39–0.48/0.34–0.56, S4: 0.47/0.40–0.49/0.32–0.54, S5: 0.45/0.41–0.49/0.33–0.51. Outliers beyond 1.5× IQR. Statistical significance by paired comparisons, two-sided (**** p < 0.0001, *** p < 0.001, ** p < 0.01). n = 5 subjects. 5B) Box plots (center/bounds/whiskers): Within Subject 0.57/0.55–0.59/0.52–0.60; Group Models—n = 1: 0.50/0.47–0.50/0.45–0.53, n = 2: 0.47/0.46–0.48/0.44–0.49, n = 3: 0.48/0.46–0.49/0.44–0.49, n = 4: 0.45/0.43–0.47/0.41–0.48. Outliers beyond 1.5× IQR. Statistical significance by repeated measures analysis, two-sided (**** p<0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05, n.s. p > 0.05) Sample sizes n = 1 to n = 4 for group models. **Created in BioRender. Singh, A. (2025) https://BioRender.com/2brioq6.