💭Imagine relying on a brain-computer interface (BCI) for your only means of communication, but it's inconsistent and needs frequent recalibration. For those who can't move or speak, this isn't just frustrating; it's a huge barrier. 🌟Our #NeurIPS2023 paper introduces a promising solution: Continual Online Recalibration with Pseudo-labels (CORP). Over an entire year (403 days), CORP demonstrated remarkable stability in an online handwriting BCI task. With a 6.16% word error rate, it significantly outperforms existing recalibration methods. 🥇To the best of our knowledge, this is the longest demonstration of intracortical BCI plug-and-play stability to date. ⚙️Technical Insight: 1⃣CORP leverages large language models to auto-correct BCI outputs. The outputs are accurate enough to be used as pseudo training labels, enabling unsupervised recalibration without user interruption. 2⃣We also implemented a replay buffer and data augmentation strategies to tackle the challenges of continual learning. 🤝We encourage the research community to build on our findings. Our data and code are open for further research and collaboration. Paper: arxiv.org/abs/2311.03611 Code: github.com/cffan/CORP Data: doi.org/10.5061/dryad.hqbzkh… This work was the result of amazing collaboration with @WillettNeuro, @xoxo_meme_queen, Nick Hahn, Foram Kamdar, Donald Avansino, Leigh Hochberg, Krishna Shenoy, Jaimie Henderson, and our clinical-trial participant T5!