Conserved Kinematic Representations enable Zero-Shot Decoding in Handwriting BCIs

TL;DR

This paper presents a neural decoding framework that leverages conserved kinematic representations to enable zero-shot decoding of unseen characters in handwriting brain-computer interfaces, facilitating open-vocabulary communication.

Contribution

It introduces a novel computational approach that aligns neural activity to imagined kinematics, allowing zero-shot decoding and reducing calibration needs for logographic languages.

Findings

Achieved 64% hits@3 retrieval on unseen characters

Neural representations of kinematic strokes are conserved across characters

Framework enables open-vocabulary decoding with minimal recalibration

Abstract

While intracortical Brain-Computer Interfaces (iBCIs) that decode imagined handwriting have achieved high communication rates for Latin scripts, they rely on observing every character in the alphabet during training. This poses a challenge in scaling to logographic languages (e.g., Chinese, Japanese), where the character set exceeds thousands of classes. The limitation highlights a fundamental question in motor neuroscience: does the motor cortex represent handwriting through the composition of shared kinematic primitives, that can be exploited by decoders? We introduce a computational framework for aligning neural activity to imagined kinematics in large datasets, enabling the training of a zero-shot capable machine learning algorithm for decoding unseen characters. Our model achieves 64% hits@3 retrieval on unseen letters, suggesting that neural representations of kinematic strokes are robustly conserved across different character contexts. This study provides a framework for dissecting conserved neural dynamics in large-scale intracortical datasets and offers strong evidence for a compositional basis of complex motor control. It also establishes a new paradigm for open-vocabulary iBCI communication with minimal recalibration burden on the user, crucial to increasing adoption of neuroprosthetics in logographic languages.