Guidelines for Feature Matching Assessment of Brain–Computer Interfaces for Augmentative and Alternative Communication Purpose Brain–computer interfaces (BCIs) can provide access to augmentative and alternative communication (AAC) devices using neurological activity alone without voluntary movements. As with traditional AAC access methods, BCI performance may be influenced by the cognitive–sensory–motor and motor imagery profiles of those who use these devices. Therefore, we propose a person-centered, ... Clinical Focus
Newly Published
Clinical Focus  |   June 01, 2018
Guidelines for Feature Matching Assessment of Brain–Computer Interfaces for Augmentative and Alternative Communication
 
Author Affiliations & Notes
  • Kevin M. Pitt
    Department of Speech-Language-Hearing: Sciences & Disorders, The University of Kansas, Lawrence
  • Jonathan S. Brumberg
    Department of Speech-Language-Hearing: Sciences & Disorders, Neuroscience Graduate Program, The University of Kansas, Lawrence
  • Disclosure: The authors have declared that no competing interests existed at the time of publication.
    Disclosure: The authors have declared that no competing interests existed at the time of publication.×
  • Correspondence to Jonathan S. Brumberg: brumberg@ku.edu
  • Editor-in-Chief: Julie Barkmeier-Kraemer
    Editor-in-Chief: Julie Barkmeier-Kraemer×
  • Editor: Erinn Finke
    Editor: Erinn Finke×
Article Information
Hearing Disorders / Augmentative & Alternative Communication / Attention, Memory & Executive Functions / Newly Published / Clinical Focus
Clinical Focus   |   June 01, 2018
Guidelines for Feature Matching Assessment of Brain–Computer Interfaces for Augmentative and Alternative Communication
American Journal of Speech-Language Pathology, Newly Published. doi:10.1044/2018_AJSLP-17-0135
History: Received August 30, 2017 , Revised December 1, 2017 , Accepted February 27, 2018
 
American Journal of Speech-Language Pathology, Newly Published. doi:10.1044/2018_AJSLP-17-0135
History: Received August 30, 2017; Revised December 1, 2017; Accepted February 27, 2018

Purpose Brain–computer interfaces (BCIs) can provide access to augmentative and alternative communication (AAC) devices using neurological activity alone without voluntary movements. As with traditional AAC access methods, BCI performance may be influenced by the cognitive–sensory–motor and motor imagery profiles of those who use these devices. Therefore, we propose a person-centered, feature matching framework consistent with clinical AAC best practices to ensure selection of the most appropriate BCI technology to meet individuals' communication needs.

Method The proposed feature matching procedure is based on the current state of the art in BCI technology and published reports on cognitive, sensory, motor, and motor imagery factors important for successful operation of BCI devices.

Results Considerations for successful selection of BCI for accessing AAC are summarized based on interpretation from a multidisciplinary team with experience in AAC, BCI, neuromotor disorders, and cognitive assessment. The set of features that support each BCI option are discussed in a hypothetical case format to model possible transition of BCI research from the laboratory into clinical AAC applications.

Conclusions This procedure is an initial step toward consideration of feature matching assessment for the full range of BCI devices. Future investigations are needed to fully examine how person-centered factors influence BCI performance across devices.

Acknowledgments
This work was supported in part by National Institute on Deafness and Other Communication Disorders Grant R03-DC011304, awarded to J. Brumberg; the University of Kansas New Faculty Research Fund, awarded to J. Brumberg; and the American Speech-Language-Hearing Foundation New Century Scholars Research Grant, awarded to J. Brumberg. The authors would like to thank Nancy Brady, Jeremy Burnison, Alana Mantie-Kozlowski, Allison Meder, Caitlin Masterson, and Kelli Johnsen for their discussions on the development of the feature matching framework, and Anthony Pitt, for assistance with the checklist design.
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