Crossmark

Objective Classification of mTBI Using Machine Learning on a Combination of Frontopolar Electroencephalography Measurements and Self-reported Symptoms

Crossref DOI link: https://doi.org/10.1186/s40798-019-0187-y

Published Online: 2019-04-18

Published Print: 2019-12

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

McNerney, M. Windy https://orcid.org/0000-0002-6536-5671
Hobday, Thomas

Cole, Betsy

Ganong, Rick

Winans, Nina

Matthews, Dennis

Hood, Jim

Lane, Stephen
License Information

Text and Data Mining valid from 2019-04-18
More Information

Article History

Received: 23 October 2018

Accepted: 28 March 2019

First Online: 18 April 2019

Ethics Approval and Consent to Participate

: All protocols and procedures were approved by an Institutional Review Board (IRB) under the guidelines of Aspire IRB (Santee, CA). Each participant provided informed consent to participate in the study. Signed consent forms included <i>Authorization to use your private health information, Informed Consent to Participate in Clinical Research Study</i>, and <i>Experimental Subject’s Bill of Rights.</i>

: Not applicable

: M. Windy McNerney and Stephen Lane have received financial compensation as consultants from the Tahoe Institute for Rural Health Research for this study. Thomas Hobday, Betsy, Rick Ganong, Jim Hood, Nina Winans, and Dennis Matthews declare they have no competing interests.

: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Document is current

Any future updates will be listed below