Conserved Kinematic Representations enable Zero-Shot Decoding in Handwriting BCIs
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| Format: | Preprint |
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2026
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| author | Ravishankar, Srinivas de Sa, Virginia |
| author_facet | Ravishankar, Srinivas de Sa, Virginia |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_19048 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Conserved Kinematic Representations enable Zero-Shot Decoding in Handwriting BCIs Ravishankar, Srinivas de Sa, Virginia Neurons and Cognition 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. |
| title | Conserved Kinematic Representations enable Zero-Shot Decoding in Handwriting BCIs |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2605.19048 |