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Related Experiment Video

Updated: Aug 5, 2025

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

A sensor-enabled cloud-based computing platform for computational brain biomechanics.

Ritika R Menghani¹, Anil Das¹, Reuben H Kraft²

¹Department of Mechanical Engineering, The Pennsylvania State University, University Park, 16802, USA.

Computer Methods and Programs in Biomedicine

|March 23, 2023

Summary

A new cloud-based brain modeling platform uses smart mouthguard sensor data to predict head impact injuries. This validated service offers automated, transparent, and reproducible brain injury metrics for research.

Keywords:

Brain biomechanics Cloud computing Integrated mouthguard sensors Nonlinear finite element modeling

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Last Updated: Aug 5, 2025

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

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Published on: March 10, 2011

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

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Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

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Published on: July 26, 2013

Area of Science:

Computational Biomechanics
Neuroscience
Wearable Technology

Background:

Repetitive head trauma is a risk in contact sports and military activities.
Instrumented mouthguards (smart mouthguards) are being developed to measure head impacts.
Understanding brain biomechanics from impact is crucial for injury prevention.

Purpose of the Study:

To present an automated brain modeling service using sensor data from smart mouthguards.
To predict brain injury metrics in a computational, physics-based manner.
To integrate simulated injury predictions into research pipelines.

Main Methods:

Developed a scalable cloud-based platform utilizing Amazon Web Services (AWS).
Employed custom finite element modeling code for intracranial brain tissue response.
Created a web application and API for user accessibility.

Main Results:

Validated platform predictions against experimental data for brain-skull displacements, strains, and pressure.
Verified the platform's parallel processing capabilities.
Assessed the accuracy of custom head surface generation.

Conclusions:

A validated, cloud-based computational brain modeling platform is presented.
The platform uses sensor data to output quantitative brain tissue strains and injury metrics.
The service aims to provide transparent, reproducible, and traceable computational results.