Compositional decoding of neural activity enhances generalization in handwriting BCIs

Abstract 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. Competing Interest Statement VG holds shares in Neuralink Corp. and is Chief Scientific Officer and an options holder at Paradromics, Inc. Footnotes smnarasimha{at}ucsd.edu, jih201{at}ucsd.edu, rsristi{at}ucsd.edu, vgilja{at}ucsd.edu, gmishne{at}ucsd.edu This work was supported by NSF award EFRI 2223822.