Literature DB >> 27590964

Advances in user-training for mental-imagery-based BCI control: Psychological and cognitive factors and their neural correlates.

C Jeunet1, B N'Kaoua2, F Lotte3.   

Abstract

While being very promising for a wide range of applications, mental-imagery-based brain-computer interfaces (MI-BCIs) remain barely used outside laboratories, notably due to the difficulties users encounter when attempting to control them. Indeed, 10-30% of users are unable to control MI-BCIs (so-called BCI illiteracy) while only a small proportion reach acceptable control abilities. This huge interuser variability has led the community to investigate potential predictors of performance related to users' personality and cognitive profile. Based on a literature review, we propose a classification of these MI-BCI performance predictors into three categories representing high-level cognitive concepts: (1) users' relationship with the technology (including the notions of computer anxiety and sense of agency), (2) attention, and (3) spatial abilities. We detail these concepts and their neural correlates in order to better understand their relationship with MI-BCI user-training. Consequently, we propose, by way of future prospects, some guidelines to improve MI-BCI user-training.
© 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Attention; Brain–computer interfaces; Computer anxiety; Improving training protocols; Interuser variability; Neural correlates; Predictors of performance; Sense of agency; Spatial abilities; User-training

Mesh:

Year:  2016        PMID: 27590964     DOI: 10.1016/bs.pbr.2016.04.002

Source DB:  PubMed          Journal:  Prog Brain Res        ISSN: 0079-6123            Impact factor:   2.453


  25 in total

1.  Heading for new shores! Overcoming pitfalls in BCI design.

Authors:  Ricardo Chavarriaga; Melanie Fried-Oken; Sonja Kleih; Fabien Lotte; Reinhold Scherer
Journal:  Brain Comput Interfaces (Abingdon)       Date:  2016-12-30

2.  Neural mechanisms of training an auditory event-related potential task in a brain-computer interface context.

Authors:  Sebastian Halder; Teresa Leinfelder; Stefan M Schulz; Andrea Kübler
Journal:  Hum Brain Mapp       Date:  2019-01-28       Impact factor: 5.038

3.  Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior.

Authors:  Jennifer Stiso; Marie-Constance Corsi; Jean M Vettel; Javier Garcia; Fabio Pasqualetti; Fabrizio De Vico Fallani; Timothy H Lucas; Danielle S Bassett
Journal:  J Neural Eng       Date:  2020-07-24       Impact factor: 5.379

4.  Workshops of the Seventh International Brain-Computer Interface Meeting: Not Getting Lost in Translation.

Authors:  Jane E Huggins; Christoph Guger; Erik Aarnoutse; Brendan Allison; Charles W Anderson; Steven Bedrick; Walter Besio; Ricardo Chavarriaga; Jennifer L Collinger; An H Do; Christian Herff; Matthias Hohmann; Michelle Kinsella; Kyuhwa Lee; Fabien Lotte; Gernot Müller-Putz; Anton Nijholt; Elmar Pels; Betts Peters; Felix Putze; Rüdiger Rupp; Gerwin Schalk; Stephanie Scott; Michael Tangermann; Paul Tubig; Thorsten Zander
Journal:  Brain Comput Interfaces (Abingdon)       Date:  2019-12-10

5.  Behind the Scenes of Noninvasive Brain-Computer Interfaces: A Review of Electroencephalography Signals, How They Are Recorded, and Why They Matter.

Authors:  Kevin M Pitt; Jonathan S Brumberg; Jeremy D Burnison; Jyutika Mehta; Juhi Kidwai
Journal:  Perspect ASHA Spec Interest Groups       Date:  2019-11-09

6.  Neurofeedback Training of Alpha Relative Power Improves the Performance of Motor Imagery Brain-Computer Interface.

Authors:  Qing Zhou; Ruidong Cheng; Lin Yao; Xiangming Ye; Kedi Xu
Journal:  Front Hum Neurosci       Date:  2022-04-08       Impact factor: 3.473

7.  Virtual and Actual Humanoid Robot Control with Four-Class Motor-Imagery-Based Optical Brain-Computer Interface.

Authors:  Alyssa M Batula; Youngmoo E Kim; Hasan Ayaz
Journal:  Biomed Res Int       Date:  2017-07-18       Impact factor: 3.411

8.  Comparison of Brain Activation during Motor Imagery and Motor Movement Using fNIRS.

Authors:  Alyssa M Batula; Jesse A Mark; Youngmoo E Kim; Hasan Ayaz
Journal:  Comput Intell Neurosci       Date:  2017-05-04

9.  Benchmarking Brain-Computer Interfaces Outside the Laboratory: The Cybathlon 2016.

Authors:  Domen Novak; Roland Sigrist; Nicolas J Gerig; Dario Wyss; René Bauer; Ulrich Götz; Robert Riener
Journal:  Front Neurosci       Date:  2018-01-11       Impact factor: 4.677

10.  Classification of Movement and Inhibition Using a Hybrid BCI.

Authors:  Jennifer Chmura; Joshua Rosing; Steven Collazos; Shikha J Goodwin
Journal:  Front Neurorobot       Date:  2017-08-15       Impact factor: 2.650

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