Compositional decoding of neural activity enhances generalization in handwriting BCIs

Recent brain-computer interfaces (BCIs) have achieved state-of-the-art performance in decoding behavior from neural activity. These models are typically trained on a con-strained set of behaviors, which limits their ability to generalize to real-world settings where behavior is variable, complex, and context-dependent. However, many complex behaviors can be decomposed into a set of reusable behavioral motifs, indicating a compositional organization. Here, we analyze human intracortical neural activity underlying attempted handwriting and find signatures of neural compositionality at a finer resolution than individual letters. We further introduce a compositional temporal decoding model, MOtif-based Temporal Inference Framework (MOTIF), that jointly predicts the fine-scale behavioral motifs (e.g., strokes, phonemes) and the longer-timescale behavior class (e.g., characters, words). We show that the compositional structure leveraged by MOTIF enables improved generalization in few-shot learning. Our results demonstrate that explicitly incorporating compositionality into neural decoders can enhance generalization and sample efficiency, while providing a principled approach to designing more scalable, robust, and interpretable BCIs.