UniBCI is a unified pretrained model for invasive neural spike data that uses CST tokenization, IAA attention, and self-supervised masked reconstruction to achieve SOTA downstream performance with better generalization and efficiency.
Neural encoding and decoding at scale.arXiv preprint arXiv:2504.08201
4 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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representative citing papers
A cross-species pretrained neural encoder combined with end-to-end training and audio LLMs reduces word error rate in neural speech decoding from 24.69% to 10.22% while aligning attempted and imagined speech.
A dual-axis autoregressive transformer pretrained on tokenized calcium traces transfers competitively to population forecasting and better than supervised models to behavior decoding.
Dual-stream EEG decoder separates identity and orientation to support 3D reconstruction from neural signals via circular regression and conditioned diffusion.
citing papers explorer
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UniBCI: Towards a Unified Pretrained Model for Invasive Brain-Computer Interfaces
UniBCI is a unified pretrained model for invasive neural spike data that uses CST tokenization, IAA attention, and self-supervised masked reconstruction to achieve SOTA downstream performance with better generalization and efficiency.
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A cross-species neural foundation model for end-to-end speech decoding
A cross-species pretrained neural encoder combined with end-to-end training and audio LLMs reduces word error rate in neural speech decoding from 24.69% to 10.22% while aligning attempted and imagined speech.
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CalM: A Self-Supervised Foundation Model for Population Dynamics in Calcium Imaging Data
A dual-axis autoregressive transformer pretrained on tokenized calcium traces transfers competitively to population forecasting and better than supervised models to behavior decoding.
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Dual-Stream EEG Decoding for 3D Visual Perception
Dual-stream EEG decoder separates identity and orientation to support 3D reconstruction from neural signals via circular regression and conditioned diffusion.