Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design
Identifieur interne : 001C00 ( Main/Merge ); précédent : 001B99; suivant : 001C01Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design
Auteurs : Fabien Lotte ; Florian Larrue ; Christian MühlSource :
- Frontiers in Human Neuroscience [ 1662-5161 ] ; 2013.
Abstract
While recent research on Brain-Computer Interfaces (BCI) has highlighted their potential for many applications, they remain barely used outside laboratories. The main reason is their lack of robustness. Indeed, with current BCI, mental state recognition is usually slow and often incorrect. Spontaneous BCI (i.e., mental imagery-based BCI) often rely on mutual learning efforts by the user and the machine, with BCI users learning to produce stable ElectroEncephaloGraphy (EEG) patterns (spontaneous BCI control being widely acknowledged as a skill) while the computer learns to automatically recognize these EEG patterns, using signal processing. Most research so far was focused on signal processing, mostly neglecting the human in the loop. However, how well the user masters the BCI skill is also a key element explaining BCI robustness. Indeed, if the user is not able to produce stable and distinct EEG patterns, then no signal processing algorithm would be able to recognize them. Unfortunately, despite the importance of BCI training protocols, they have been scarcely studied so far, and used mostly unchanged for years. In this paper, we advocate that current human training approaches for spontaneous BCI are most likely inappropriate. We notably study instructional design literature in order to identify the key requirements and guidelines for a successful training procedure that promotes a good and efficient skill learning. This literature study highlights that current spontaneous BCI user training procedures satisfy very few of these requirements and hence are likely to be suboptimal. We therefore identify the flaws in BCI training protocols according to instructional design principles, at several levels: in the instructions provided to the user, in the tasks he/she has to perform, and in the feedback provided. For each level, we propose new research directions that are theoretically expected to address some of these flaws and to help users learn the BCI skill more efficiently.
Url:
DOI: 10.3389/fnhum.2013.00568
PubMed: 24062669
PubMed Central: 3775130
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design</title><author><name sortKey="Lotte, Fabien" sort="Lotte, Fabien" uniqKey="Lotte F" first="Fabien" last="Lotte">Fabien Lotte</name></author><author><name sortKey="Larrue, Florian" sort="Larrue, Florian" uniqKey="Larrue F" first="Florian" last="Larrue">Florian Larrue</name></author><author><name sortKey="Muhl, Christian" sort="Muhl, Christian" uniqKey="Muhl C" first="Christian" last="Mühl">Christian Mühl</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">24062669</idno><idno type="pmc">3775130</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3775130</idno><idno type="RBID">PMC:3775130</idno><idno type="doi">10.3389/fnhum.2013.00568</idno><date when="2013">2013</date><idno type="wicri:Area/Pmc/Corpus">001D42</idno><idno type="wicri:Area/Pmc/Curation">001D42</idno><idno type="wicri:Area/Pmc/Checkpoint">001257</idno><idno type="wicri:Area/Ncbi/Merge">002969</idno><idno type="wicri:Area/Ncbi/Curation">002969</idno><idno type="wicri:Area/Ncbi/Checkpoint">002969</idno><idno type="wicri:Area/Main/Merge">001C00</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design</title><author><name sortKey="Lotte, Fabien" sort="Lotte, Fabien" uniqKey="Lotte F" first="Fabien" last="Lotte">Fabien Lotte</name></author><author><name sortKey="Larrue, Florian" sort="Larrue, Florian" uniqKey="Larrue F" first="Florian" last="Larrue">Florian Larrue</name></author><author><name sortKey="Muhl, Christian" sort="Muhl, Christian" uniqKey="Muhl C" first="Christian" last="Mühl">Christian Mühl</name></author></analytic><series><title level="j">Frontiers in Human Neuroscience</title><idno type="eISSN">1662-5161</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>While recent research on Brain-Computer Interfaces (BCI) has highlighted their potential for many applications, they remain barely used outside laboratories. The main reason is their lack of robustness. Indeed, with current BCI, mental state recognition is usually slow and often incorrect. Spontaneous BCI (i.e., mental imagery-based BCI) often rely on mutual learning efforts by the user and the machine, with BCI users learning to produce stable ElectroEncephaloGraphy (EEG) patterns (spontaneous BCI control being widely acknowledged as a skill) while the computer learns to automatically recognize these EEG patterns, using signal processing. Most research so far was focused on signal processing, mostly neglecting the human in the loop. However, how well the user masters the BCI skill is also a key element explaining BCI robustness. Indeed, if the user is not able to produce stable and distinct EEG patterns, then no signal processing algorithm would be able to recognize them. Unfortunately, despite the importance of BCI training protocols, they have been scarcely studied so far, and used mostly unchanged for years. In this paper, we advocate that current human training approaches for spontaneous BCI are most likely inappropriate. We notably study instructional design literature in order to identify the key requirements and guidelines for a successful training procedure that promotes a good and efficient skill learning. This literature study highlights that current spontaneous BCI user training procedures satisfy very few of these requirements and hence are likely to be suboptimal. We therefore identify the flaws in BCI training protocols according to instructional design principles, at several levels: in the instructions provided to the user, in the tasks he/she has to perform, and in the feedback provided. For each level, we propose new research directions that are theoretically expected to address some of these flaws and to help users learn the BCI skill more efficiently.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Ainsworth, S" uniqKey="Ainsworth S">S. Ainsworth</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Allison, B" uniqKey="Allison B">B. Allison</name></author><author><name sortKey="Mcfarland, D" uniqKey="Mcfarland D">D. McFarland</name></author><author><name sortKey="Schalk, G" uniqKey="Schalk G">G. Schalk</name></author><author><name sortKey="Zheng, S" uniqKey="Zheng S">S. Zheng</name></author><author><name sortKey="Jackson, M" uniqKey="Jackson M">M. Jackson</name></author><author><name sortKey="Wolpaw, J" uniqKey="Wolpaw J">J. Wolpaw</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Allison, B" uniqKey="Allison B">B. 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Neat</name></author><author><name sortKey="Forneris, C A" uniqKey="Forneris C">C. A. Forneris</name></author></analytic></biblStruct></listBibl></div1></back></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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