A Controlled Comparison of Self-Supervised Pretraining Objectives and Corpora for Intracortical Neural Decoding

Brain-computer interfaces (BCIs) have recently made impressive progress in restoring communication by decoding intended behavior directly from intracortical neural activity. Inspired by the success of foundation models in computer vision and natural language processing, recent work has begun to explore large-scale self-supervised pretraining for neural decoding. However, it is unclear whether these approaches already provide consistent advantages at the scale of data currently available, which is still limited and highly fragmented. In this work we present the first controlled comparison of three self-supervised objective families for intracortical sequence decoding under a unified experimental framework. Using a common transformer backbone, we compare masked autoencoding (MAE), Joint-Embedding Predictive Architectures (JEPA), autoregressive (AR) pretraining, and training from scratch across corpora spanning multiple subjects, behaviors and species: up to 345 hours of intracortical recordings, combining 208 hours from 6 human participants performing attempted and imagined speech, handwriting, typing and cursor control with 137 hours of motor tasks from 19 non-human primates. Our results show that the gains pretraining provides on downstream tasks strongly depend on the amount and diversity of available data, and the resulting performance is often comparable to that of carefully tuned task-specific training. These findings establish a controlled benchmark for future research on neural foundation models.