PROJECT SUMMARY Paralysis due to spinal cord injury, stroke, or amyotrophic lateral sclerosis (ALS) can lead to debilitating communication deficits. Implanted brain-computer interfaces (BCIs) are a promising approach to treat these patients. BCIs leverage neural activity to create a desired computer output, such as text. To characterize the relationship between the neural data and the computer output, a decoder is trained on data from many repeated trials. Unfortunately, this training trial burden limits the practical utility of communication BCIs in many patients. There are several contributing factors to this training burden. First, there is an incomplete understanding of the neural codes which underlie complex, skilled behaviors in humans such as handwriting or speech. Second, standard decoders rely on neural network architectures which are extremely flexible but require a substantial amount of training data to achieve acceptable predictive accuracy. Communication BCIs are often based solely on intentions of motion, which creates an additional technical challenge. Due to unobservable variability in the timing of patients’ intentions from trial-to-trial, data-driven methods for aligning neural activity across trials can substantially aid in the analysis of these datasets. I have developed Bayesian time warping for this purpose, a neural activity alignment approach which learns a probability distribution over possible alignments for each trial and response profiles for each neuron based on the observed data. In this project, I propose that the uncertainty estimates generated by this method will provide insights into approaches that can improve the data-efficiency of communication BCIs. To determine if these insights can be generalized across distinct BCI strategies, I will analyze data from two different communication BCI approaches: one which decodes characters from attempted handwriting, and another which decodes phonemes from attempted speech. In Aim 1, I will u