# Optical Tomography and Decoding for Communication via Brain-Computer Interface

> **NIH NIH F31** · WASHINGTON UNIVERSITY · 2021 · $29,520

## Abstract

Project Summary: The long-term goal of this research is to develop a new, non-invasive brain-computer
interface (BCI) that will provide augmentative and alternative communication (AAC) capabilities to patients who
have lost these capabilities due to severe motor impairments, such as completely locked-in syndrome (CLIS),
amyotrophic lateral sclerosis (ALS), and severe cerebral palsy (CP). The proposed research project will work
towards a long-term goal by developing a BCI based on brain imaging signals from high-density diffuse optical
tomography (HDDOT). Some existing BCIs record electroencephalography (EEG) or electrocorticography
(ECoG) signals from patients and then decode these signals into instructions for operating some element of the
outside world, such as a cursor on a screen, a prosthetic limb, or a virtual keyboard. This functionality can enable
communication. However, BCI generally has had limited success and capabilities in CLIS patients with EEG or
has relied on invasive technology such as ECoG or intracortical recordings, which require surgical placement of
electrodes on or beneath the brain surface. Although functional MRI (fMRI) has recently achieved great success
with decoding items viewed or heard by subjects (e.g., distinguishing from among >100 viewed images), fMRI
requires bulky, expensive equipment that cannot be employed for routine BCI for patients with severe motor-
related communication deficits. In contrast, optical imaging approaches, such as near-infrared spectroscopy
(NIRS), employ portable, wearable hardware. These optical systems are non-invasive and use non-ionizing,
near-infrared light to create movies of blood oxygenation and therefore provide physiological information
comparable to the fMRI signal. NIRS has recently been applied as an alternative to EEG BCI for decoding simple
yes/no responses in CLIS patients. However, NIRS systems suffer from much-lower spatial resolution than fMRI,
which renders NIRS unlikely to match the decoding capabilities of fMRI. High-density diffuse optical tomography
(HDDOT) combines the lightweight, low-cost equipment benefits of EEG and NIRS with higher spatial resolution
closer to that of fMRI at the brain surface. Recent advances in HDDOT systems have enabled average spatial
localization errors <5 mm and spatial resolution <17-20 mm (substantially better than NIRS). Studies have
demonstrated detailed maps of both visual and language tasks. These properties make HDDOT an ideal
candidate tool for decoding brain function. The fellowship training will provide a strong foundation in optical
neuroimaging methods, machine learning, and brain-computer interface. These experiences will prepare the
applicant exceptionally well for a career in biomedical engineering research and for developing technology that
will improve these patients’ quality of life.

## Key facts

- **NIH application ID:** 10078847
- **Project number:** 5F31NS110261-02
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Zachary E Markow
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $29,520
- **Award type:** 5
- **Project period:** 2020-01-01 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10078847

## Citation

> US National Institutes of Health, RePORTER application 10078847, Optical Tomography and Decoding for Communication via Brain-Computer Interface (5F31NS110261-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10078847. Licensed CC0.

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