Literature DB >> 32164866

Hearing the needs of clinical users.

Andrea Kübler1, Femke Nijboer2, Sonja Kleih1.   

Abstract

In the past 10 years, brain-computer interfaces (BCIs) for controlling assistive devices have seen tremendous progress with respect to reliability and learnability, and numerous exemplary applications were demonstrated to be controllable by a BCI. Yet, BCI-controlled applications are hardly used for patients with neurologic or neurodegenerative disease. Such patient groups are considered potential end-users of BCI, specifically for replacing or improving lost function. We argue that BCI research and development still faces a translational gap, i.e., the knowledge of how to bring BCIs from the laboratory to the field is insufficient. BCI-controlled applications lack usability and accessibility; both constitute two sides of one coin, which is the key to use in daily life and to prevent nonuse. To increase usability, we suggest rigorously adopting the user-centered design in applied BCI research and development. To provide accessibility, assistive technology (AT) experts, providers, and other stakeholders have to be included in the user-centered process. BCI experts have to ensure the transfer of knowledge to AT professionals, and listen to the needs of primary, secondary, and tertiary end-users of BCI technology. Addressing both, usability and accessibility, in applied BCI research and development will bridge the translational gap and ensure that the needs of clinical end-users are heard, understood, addressed, and fulfilled.
© 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Accessibility; Assistive technology; End-users; Stakeholders; Translation; Translational gap; Usability; User-centered design

Mesh:

Year:  2020        PMID: 32164866     DOI: 10.1016/B978-0-444-63934-9.00026-3

Source DB:  PubMed          Journal:  Handb Clin Neurol        ISSN: 0072-9752


  5 in total

1.  Automatic Selection of Control Features for Electroencephalography-Based Brain-Computer Interface Assisted Motor Rehabilitation: The GUIDER Algorithm.

Authors:  Emma Colamarino; Floriana Pichiorri; Jlenia Toppi; Donatella Mattia; Febo Cincotti
Journal:  Brain Topogr       Date:  2022-01-19       Impact factor: 3.020

2.  Evaluation of a P300-Based Brain-Machine Interface for a Robotic Hand-Orthosis Control.

Authors:  Jonathan Delijorge; Omar Mendoza-Montoya; Jose L Gordillo; Ricardo Caraza; Hector R Martinez; Javier M Antelis
Journal:  Front Neurosci       Date:  2020-11-27       Impact factor: 4.677

3.  Brain Computer Interfaces and Communication Disabilities: Ethical, Legal, and Social Aspects of Decoding Speech From the Brain.

Authors:  Jennifer A Chandler; Kiah I Van der Loos; Susan Boehnke; Jonas S Beaudry; Daniel Z Buchman; Judy Illes
Journal:  Front Hum Neurosci       Date:  2022-04-21       Impact factor: 3.473

Review 4.  Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation: A Systematic Review.

Authors:  Daniela Camargo-Vargas; Mauro Callejas-Cuervo; Stefano Mazzoleni
Journal:  Sensors (Basel)       Date:  2021-06-24       Impact factor: 3.576

5.  An Open Source-Based BCI Application for Virtual World Tour and Its Usability Evaluation.

Authors:  Sanghum Woo; Jongmin Lee; Hyunji Kim; Sungwoo Chun; Daehyung Lee; Daeun Gwon; Minkyu Ahn
Journal:  Front Hum Neurosci       Date:  2021-07-19       Impact factor: 3.169

  5 in total

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