Plug and play stability for intracortical brain-computer interfaces: A one-year demonstration of seamless brain-to-text communication

Intracortical brain-computer interfaces (iBCIs) have shown promise for
restoring rapid communication to people with neurological disorders such
as amyotrophic lateral sclerosis (ALS). However, to maintain high
performance over time, iBCIs typically need frequent recalibration to
combat changes in the neural recordings that accrue over days. In this
study, we propose a method: Continual Online Recalibration with
Pseudo-labels (CORP), that enables self-recalibration of communication
iBCIs without interrupting the user. We evaluated CORP with one clinical
trial participant. CORP achieved a stable decoding accuracy of 93.84% in
an online handwriting iBCI task, significantly outperforming other
baseline methods. This dataset contains 21 sessions of recorded neural
activities used for the evaluation. It has been formatted for developing
and evaluating machine learning models. There 5 more sessions heldout for
a planned iBCI stability competition. They will be released in the future.
We also provide a pretrained RNN seed model and a laugnage model to
preproduce the results in our paper.