The Development of an Electroencephalography (EEG)-Derived, 3D-Printed Brain-Computer Interface (BCI) NeuroProsthesis Utilizing Machine Learning for Chronic Impairment

23 Pages Posted: 21 Nov 2018

See all articles by Thriaksh Rajan

Thriaksh Rajan

Rutgers University, New Brunswick - Rutgers Cancer Institute of New Jersey; Mount Sinai Health System - Icahn School of Medicine; Biotechnology High School

Joel D. Valliath

Independent

Date Written: February 9th, 2018

Abstract

In the United States alone, there are 20 million people suffering from limb loss and 50 million with paralysis. Artificial prostheses have been manufactured to restore aesthetic appearance and locomotory function of impaired extremities. However, most prosthetic appendages are passive, incapable of restoring complete function. Hence, Brain-Computer Interfaces (BCIs) utilizing electroencephalograms (EEGs) have been explored for their uninterrupted communication between the brain and external device-output. Nonetheless, a fully functional BCI product has not been available for biomedical application thus far. Our goal is to develop a supervised machine learning algorithm, written in Python, to predict patterns in EEG signals customized for the impaired. Emotiv EPOC, a cost-effective BCI, will be used to detect motor imagery. The output signals will be integrated with Raspberry Pi 3 to control a 3D-printed transradial prosthetic. This novel, cost-effective, and facile approach will bring a comprehensive and non-invasive resolution to global impairment worldwide.

Keywords: Neurology, Biomedical Engineering, Computer Science, Machine Learning, AI, Neuroscience

JEL Classification: I12

Suggested Citation

Rajan, Thriaksh and Valliath, Joel D., The Development of an Electroencephalography (EEG)-Derived, 3D-Printed Brain-Computer Interface (BCI) NeuroProsthesis Utilizing Machine Learning for Chronic Impairment (February 9th, 2018). Available at SSRN: https://ssrn.com/abstract=3274806 or http://dx.doi.org/10.2139/ssrn.3274806

Thriaksh Rajan (Contact Author)

Rutgers University, New Brunswick - Rutgers Cancer Institute of New Jersey ( email )

195 Little Albany Street
New Brunswick, NJ 08903-2681
United States

HOME PAGE: http://cinj.org

Mount Sinai Health System - Icahn School of Medicine ( email )

One Gustave L. Levy Place
New York, NY 10029-6574
United States

HOME PAGE: http://mountsinai.org

Biotechnology High School ( email )

Freehold, NJ
United States

Joel D. Valliath

Independent ( email )

No Address Available

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