Literature DB >> 27172246

Why standard brain-computer interface (BCI) training protocols should be changed: an experimental study.

Camille Jeunet1, Emilie Jahanpour, Fabien Lotte.   

Abstract

OBJECTIVE: While promising, electroencephaloraphy based brain-computer interfaces (BCIs) are barely used due to their lack of reliability: 15% to 30% of users are unable to control a BCI. Standard training protocols may be partly responsible as they do not satisfy recommendations from psychology. Our main objective was to determine in practice to what extent standard training protocols impact users' motor imagery based BCI (MI-BCI) control performance. APPROACH: We performed two experiments. The first consisted in evaluating the efficiency of a standard BCI training protocol for the acquisition of non-BCI related skills in a BCI-free context, which enabled us to rule out the possible impact of BCIs on the training outcome. Thus, participants (N = 54) were asked to perform simple motor tasks. The second experiment was aimed at measuring the correlations between motor tasks and MI-BCI performance. The ten best and ten worst performers of the first study were recruited for an MI-BCI experiment during which they had to learn to perform two MI tasks. We also assessed users' spatial ability and pre-training μ rhythm amplitude, as both have been related to MI-BCI performance in the literature. MAIN
RESULTS: Around 17% of the participants were unable to learn to perform the motor tasks, which is close to the BCI illiteracy rate. This suggests that standard training protocols are suboptimal for skill teaching. No correlation was found between motor tasks and MI-BCI performance. However, spatial ability played an important role in MI-BCI performance. In addition, once the spatial ability covariable had been controlled for, using an ANCOVA, it appeared that participants who faced difficulty during the first experiment improved during the second while the others did not. SIGNIFICANCE: These studies suggest that (1) standard MI-BCI training protocols are suboptimal for skill teaching, (2) spatial ability is confirmed as impacting on MI-BCI performance, and (3) when faced with difficult pre-training, subjects seemed to explore more strategies and therefore learn better.

Entities:  

Mesh:

Year:  2016        PMID: 27172246     DOI: 10.1088/1741-2560/13/3/036024

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  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.  Predicting motor skill learning in older adults using visuospatial performance.

Authors:  Peiyuan Wang; Frank J Infurna; Sydney Y Schaefer
Journal:  J Mot Learn Dev       Date:  2020-04

3.  Considering Augmentative and Alternative Communication Research for Brain-Computer Interface Practice.

Authors:  Kevin M Pitt; Jonathan S Brumberg; Adrienne R Pitt
Journal:  Assist Technol Outcomes Benefits       Date:  2019

4.  Visuospatial function predicts one-week motor skill retention in cognitively intact older adults.

Authors:  Jennapher Lingo VanGilder; Caitlin R Hengge; Kevin Duff; Sydney Y Schaefer
Journal:  Neurosci Lett       Date:  2017-11-14       Impact factor: 3.046

5.  Evidence for associations between Rey-Osterrieth Complex Figure test and motor skill learning in older adults.

Authors:  Jennapher Lingo VanGilder; Keith R Lohse; Kevin Duff; Peiyuan Wang; Sydney Y Schaefer
Journal:  Acta Psychol (Amst)       Date:  2021-01-29

Review 6.  Enrichment of Human-Computer Interaction in Brain-Computer Interfaces via Virtual Environments.

Authors:  Alonso-Valerdi Luz María; Mercado-García Víctor Rodrigo; Luz María Alonso-Valerdi; Víctor Rodrigo Mercado-García
Journal:  Comput Intell Neurosci       Date:  2017-11-29

7.  User's Self-Prediction of Performance in Motor Imagery Brain-Computer Interface.

Authors:  Minkyu Ahn; Hohyun Cho; Sangtae Ahn; Sung C Jun
Journal:  Front Hum Neurosci       Date:  2018-02-15       Impact factor: 3.169

8.  Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface.

Authors:  Shi Qiu; Junjun Li; Mengdi Cong; Chun Wu; Yan Qin; Ting Liang
Journal:  Comput Math Methods Med       Date:  2020-06-15       Impact factor: 2.238

Review 9.  Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation.

Authors:  Colin Simon; David A E Bolton; Niamh C Kennedy; Surjo R Soekadar; Kathy L Ruddy
Journal:  Front Neurosci       Date:  2021-07-02       Impact factor: 4.677

10.  The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users.

Authors:  Serafeim Perdikis; Luca Tonin; Sareh Saeedi; Christoph Schneider; José Del R Millán
Journal:  PLoS Biol       Date:  2018-05-10       Impact factor: 8.029

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