# Decoding mental concept identities using electrocorticography

> **NIH NIH R21** · MEDICAL COLLEGE OF WISCONSIN · 2024 · $195,000

## Abstract

PROJECT SUMMARY/ABSTRACT
Aphasia is a common and disabling outcome following stroke. Although some treatments are available in the
acute phase, people with chronic, severe deficits rarely have meaningful recovery. Frequently, these patients
have phonological or articulatory planning deficits, while their semantic functions are preserved. Because of
this, a novel treatment modality in these patients is a speech brain-computer interface (BCI) designed to
decode semantic activity. In this project we are developing a machine learning model to decode brain activity
to concept identities, to be used in such a device. We will first develop the model in patients with no language
deficits using invasive electrical recordings. During awake brain surgeries, we will place high-density
electrocorticography (ECoG) grids on prespecified brain locations corresponding to high-level semantic areas.
Patients will perform a semantic decision task, and the neural network model will be trained to predict concept
identities from the recorded ECoG activity using a semantic model developed by our lab. We will then
demonstrate the application of this model to people with aphasia by performing the same task using the
noninvasive magnetoencephalography (MEG) in people with severe aphasia. Demonstrating that this model
can be used to decode concept identities from brain activity, and that it is applicable to people with severe
aphasia, will open up a new avenue of treatment for this population.

## Key facts

- **NIH application ID:** 10837090
- **Project number:** 5R21DC021013-02
- **Recipient organization:** MEDICAL COLLEGE OF WISCONSIN
- **Principal Investigator:** William L. Gross
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $195,000
- **Award type:** 5
- **Project period:** 2023-05-05 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10837090

## Citation

> US National Institutes of Health, RePORTER application 10837090, Decoding mental concept identities using electrocorticography (5R21DC021013-02). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10837090. Licensed CC0.

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