# Bayesian time warping for data-efficient communication brain-computer interfaces

> **NIH DC F30** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2026 · $55,114

## Abstract

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

## Key facts

- **NIH application ID:** 11389297
- **Project number:** 1F30DC023803-01
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Argha  Bandyopadhyay
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** DC
- **Fiscal year:** 2026
- **Award amount:** $55,114
- **Award type:** 1
- **Project period:** 2026-05-01T00:00:00 → 2030-04-30T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11389297

## Citation

> US National Institutes of Health, RePORTER application 11389297, Bayesian time warping for data-efficient communication brain-computer interfaces (1F30DC023803-01). Retrieved via AI Analytics 2026-07-05 from https://api.ai-analytics.org/grant/nih/11389297. Licensed CC0.

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