Can a Subjective Questionnaire Be Used as Brain-Computer Interface Performance Predictor?
Predicting a subject's ability to use a Brain Computer Interface (BCI) is one of the major issues in the BCI domain. Relevant applications of forecasting BCI performance include the ability to adapt the BCI to the needs and expectations of the user, assessing the efficiency of BCI use in stroke...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2019-01-01
|
Series: | Frontiers in Human Neuroscience |
Subjects: | |
Online Access: | https://www.frontiersin.org/article/10.3389/fnhum.2018.00529/full |
id |
doaj-68739bd3873f4d519e8f276573417819 |
---|---|
record_format |
Article |
spelling |
doaj-68739bd3873f4d519e8f2765734178192020-11-25T02:40:24ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612019-01-011210.3389/fnhum.2018.00529393734Can a Subjective Questionnaire Be Used as Brain-Computer Interface Performance Predictor?Sébastien Rimbert0Nathalie Gayraud1Laurent Bougrain2Maureen Clerc3Stéphanie Fleck4Université de Lorraine, Inria, LORIA, Neurosys Team, Nancy, FranceUniversité Côte d'Azur, Inria, Sophia-Antipolis Mditerrannée, Athena Team, Valbonne, FranceUniversité de Lorraine, Inria, LORIA, Neurosys Team, Nancy, FranceUniversité Côte d'Azur, Inria, Sophia-Antipolis Mditerrannée, Athena Team, Valbonne, FranceUniversité de Lorraine, Perseus, Metz, FrancePredicting a subject's ability to use a Brain Computer Interface (BCI) is one of the major issues in the BCI domain. Relevant applications of forecasting BCI performance include the ability to adapt the BCI to the needs and expectations of the user, assessing the efficiency of BCI use in stroke rehabilitation, and finally, homogenizing a research population. A limited number of recent studies have proposed the use of subjective questionnaires, such as the Motor Imagery Questionnaire Revised-Second Edition (MIQ-RS). However, further research is necessary to confirm the effectiveness of this type of subjective questionnaire as a BCI performance estimation tool. In this study we aim to answer the following questions: can the MIQ-RS be used to estimate the performance of an MI-based BCI? If not, can we identify different markers that could be used as performance estimators? To answer these questions, we recorded EEG signals from 35 healthy volunteers during BCI use. The subjects had previously completed the MIQ-RS questionnaire. We conducted an offline analysis to assess the correlation between the questionnaire scores related to Kinesthetic and Motor imagery tasks and the performances of four classification methods. Our results showed no significant correlation between BCI performance and the MIQ-RS scores. However, we reveal that BCI performance is correlated to habits and frequency of practicing manual activities.https://www.frontiersin.org/article/10.3389/fnhum.2018.00529/fullbrain-computer interfacekinesthetic motor imagerymotor imagery questionnaireBCI-illiterateprediction of accuracy |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sébastien Rimbert Nathalie Gayraud Laurent Bougrain Maureen Clerc Stéphanie Fleck |
spellingShingle |
Sébastien Rimbert Nathalie Gayraud Laurent Bougrain Maureen Clerc Stéphanie Fleck Can a Subjective Questionnaire Be Used as Brain-Computer Interface Performance Predictor? Frontiers in Human Neuroscience brain-computer interface kinesthetic motor imagery motor imagery questionnaire BCI-illiterate prediction of accuracy |
author_facet |
Sébastien Rimbert Nathalie Gayraud Laurent Bougrain Maureen Clerc Stéphanie Fleck |
author_sort |
Sébastien Rimbert |
title |
Can a Subjective Questionnaire Be Used as Brain-Computer Interface Performance Predictor? |
title_short |
Can a Subjective Questionnaire Be Used as Brain-Computer Interface Performance Predictor? |
title_full |
Can a Subjective Questionnaire Be Used as Brain-Computer Interface Performance Predictor? |
title_fullStr |
Can a Subjective Questionnaire Be Used as Brain-Computer Interface Performance Predictor? |
title_full_unstemmed |
Can a Subjective Questionnaire Be Used as Brain-Computer Interface Performance Predictor? |
title_sort |
can a subjective questionnaire be used as brain-computer interface performance predictor? |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Human Neuroscience |
issn |
1662-5161 |
publishDate |
2019-01-01 |
description |
Predicting a subject's ability to use a Brain Computer Interface (BCI) is one of the major issues in the BCI domain. Relevant applications of forecasting BCI performance include the ability to adapt the BCI to the needs and expectations of the user, assessing the efficiency of BCI use in stroke rehabilitation, and finally, homogenizing a research population. A limited number of recent studies have proposed the use of subjective questionnaires, such as the Motor Imagery Questionnaire Revised-Second Edition (MIQ-RS). However, further research is necessary to confirm the effectiveness of this type of subjective questionnaire as a BCI performance estimation tool. In this study we aim to answer the following questions: can the MIQ-RS be used to estimate the performance of an MI-based BCI? If not, can we identify different markers that could be used as performance estimators? To answer these questions, we recorded EEG signals from 35 healthy volunteers during BCI use. The subjects had previously completed the MIQ-RS questionnaire. We conducted an offline analysis to assess the correlation between the questionnaire scores related to Kinesthetic and Motor imagery tasks and the performances of four classification methods. Our results showed no significant correlation between BCI performance and the MIQ-RS scores. However, we reveal that BCI performance is correlated to habits and frequency of practicing manual activities. |
topic |
brain-computer interface kinesthetic motor imagery motor imagery questionnaire BCI-illiterate prediction of accuracy |
url |
https://www.frontiersin.org/article/10.3389/fnhum.2018.00529/full |
work_keys_str_mv |
AT sebastienrimbert canasubjectivequestionnairebeusedasbraincomputerinterfaceperformancepredictor AT nathaliegayraud canasubjectivequestionnairebeusedasbraincomputerinterfaceperformancepredictor AT laurentbougrain canasubjectivequestionnairebeusedasbraincomputerinterfaceperformancepredictor AT maureenclerc canasubjectivequestionnairebeusedasbraincomputerinterfaceperformancepredictor AT stephaniefleck canasubjectivequestionnairebeusedasbraincomputerinterfaceperformancepredictor |
_version_ |
1724781924597104640 |