# Synchronized brain dynamics and eye movement trajectory for objective evaluation of robot-assisted surgical skills

> **NIH NIH R01** · ROSWELL PARK CANCER INSTITUTE CORP · 2022 · $359,997

## Abstract

Abstract The number of people living with Alzheimer’s disease (AD) is expected to increase from 46.8 million
worldwide (5.2 million in the USA) at 2014 to 140 million worldwide (13.8 million in the USA) by 2050. The AD
is caused by decline in cognitive function; hence, this frightening rate demonstrates the importance of
assessing cognitive learning rate (the speed of cognitive performance improvement; change of performance
score divided by practice time) as a diagnostic tool to prevent serious cognitive deterioration. Several
subjective evaluation measures have been offered, but unrelated factors to cognitive state such as age,
education, tester bias, and patient discomfort, may have an impact on individual ratings. Furthermore, multiple
electroencephalogram (EEG) studies have shown that dementia patients have different brain functioning
parameters than healthy people. However, inconsistent findings have been reported, and no universally
accepted screening technique for early diagnosis of cognitive impairment exists. The goals of this study are
to develop models that use high-density EEG recordings from 120 areas of the brain to objectively
evaluate cognitive learning rate and cognitive performance status in healthy participants. The
hypothesis is that functional brain network of individual areas of the brain changes during cognitive learning
(learning how to perform cognitive tasks properly). To validate this hypothesis, the EEG signals of thirty
mentally healthy participants (age: 36±12.7; 10 females and 20 males) performing three cognitive tasks will be
used. These tasks have been created and implemented in a robot simulator framework to help surgical
trainees enhance their cognitive skills. Perception, understanding, thinking, decision making, and task/time
management are required for these tasks, as well as spatial cognitive capacity, which allows for a quick,
correct knowledge of the position, orientation, size, and form of the object on which surgery is being performed.
At the end of each task, the robot simulator delivers a performance score. Participants are told to repeat these
tasks until they get a passing score. Then, computational network neuroscience algorithms will be developed
to extract important features characterizing the whole-brain information processing efficiency. Multivariate
analysis will be used to determine the correlations between the participants' cognitive learning rate and
retrieved features. Using EEG signals, a deep neural network model will be developed to classify performance
of completing cognitive tasks into three categories: failed (score <71), passed (score:71-80), and excellent
(score: 81-100). Although the models in this work were constructed using EEG data from healthy people, they
may be tweaked to fit data from AD patients completing more common cognitive activities. Once validated in
the area of AD, the developed models could be used to monitor cognitive learning rate and cognitive
performance to dis...

## Key facts

- **NIH application ID:** 10499022
- **Project number:** 3R01EB029398-03S1
- **Recipient organization:** ROSWELL PARK CANCER INSTITUTE CORP
- **Principal Investigator:** Somayeh Besharat Shafiei
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $359,997
- **Award type:** 3
- **Project period:** 2020-04-01 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10499022

## Citation

> US National Institutes of Health, RePORTER application 10499022, Synchronized brain dynamics and eye movement trajectory for objective evaluation of robot-assisted surgical skills (3R01EB029398-03S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10499022. Licensed CC0.

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