Comparison Between Discrete and Continuous Motor Imageries: toward a Faster Detection
Identifieur interne : 000017 ( Main/Exploration ); précédent : 000016; suivant : 000018Comparison Between Discrete and Continuous Motor Imageries: toward a Faster Detection
Auteurs : Sébastien Rimbert [France] ; Laurent Bougrain [France]Source :
English descriptors
Abstract
A large number of Brain-Computer Interfaces (BCIs) are based on the detection of changes in sensorimotor rhythms within the electroencephalographic signal [1]. Moreover, motor imagery (MI) modifies the neural activity within the primary sensorimotor areas of the cortex in a similar way to a real movement [2]. In most MI-based BCI experimental paradigms, subjects realize a continuous MI, i.e. one that lasts for a few seconds, with the objective of facilitating the detection of event-related desynchronization (ERD) and event-related synchronization (ERS) [3]. Currently, improving efficiency such as detecting faster a MI is a major issue in BCI to avoid fatigue and boredom. In this regards, a recent article showed that a brief intention of movement corresponding to a 2s-MI, leads to more informative ERS features than continuous motor imageries [4]. Thus, in this study, we are investigating differences between continuous MIs and discrete, i.e. simple short, MIs. Material, Methods and Results: 17 healthy subjects carried out real movements, discrete and continuous MIs, in the form of an isometric flexion movement of their right hand index finger. Each subject realized first a session of real movements, and then a discrete and a continuous sessions of motor imageries in a randomized order. Each session is divided into runs for a total number of 100 trials. Beeps were used as go and stop signals. Finally we computed ERD/ERS% for 9 electroencephalographic channels (FC3, C3, CP3, FCz, Fz, CPz, FC4, C4, CP4) using the " band power method " [3] (Fig. 1), topographic and time-frequency representations. Figure 1. Grand average (n = 17) ERD/ERS% curves estimated for the real movement (blue), the discrete motor imagery (red) and the continuous motor imagery (black) within the beta band (18-25 Hz) for electrode C3.
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CS 25233 - 54052 Nancy cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-lorraine.fr/</ref></desc></org></tutelle><tutelle name="UMR7503" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><settlement type="city">Metz</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term> Continuous Motor Imagery</term><term> Discrete Motor Imagery</term><term> EEG</term><term> Motor Cortex</term><term>BCI</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"> A large number of Brain-Computer Interfaces (BCIs) are based on the detection of changes in sensorimotor rhythms within the electroencephalographic signal [1]. Moreover, motor imagery (MI) modifies the neural activity within the primary sensorimotor areas of the cortex in a similar way to a real movement [2]. In most MI-based BCI experimental paradigms, subjects realize a continuous MI, i.e. one that lasts for a few seconds, with the objective of facilitating the detection of event-related desynchronization (ERD) and event-related synchronization (ERS) [3]. Currently, improving efficiency such as detecting faster a MI is a major issue in BCI to avoid fatigue and boredom. In this regards, a recent article showed that a brief intention of movement corresponding to a 2s-MI, leads to more informative ERS features than continuous motor imageries [4]. Thus, in this study, we are investigating differences between continuous MIs and discrete, i.e. simple short, MIs. Material, Methods and Results: 17 healthy subjects carried out real movements, discrete and continuous MIs, in the form of an isometric flexion movement of their right hand index finger. Each subject realized first a session of real movements, and then a discrete and a continuous sessions of motor imageries in a randomized order. Each session is divided into runs for a total number of 100 trials. Beeps were used as go and stop signals. Finally we computed ERD/ERS% for 9 electroencephalographic channels (FC3, C3, CP3, FCz, Fz, CPz, FC4, C4, CP4) using the " band power method " [3] (Fig. 1), topographic and time-frequency representations. Figure 1. Grand average (n = 17) ERD/ERS% curves estimated for the real movement (blue), the discrete motor imagery (red) and the continuous motor imagery (black) within the beta band (18-25 Hz) for electrode C3.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Rimbert, Sebastien" sort="Rimbert, Sebastien" uniqKey="Rimbert S" first="Sébastien" last="Rimbert">Sébastien Rimbert</name></region><name sortKey="Bougrain, Laurent" sort="Bougrain, Laurent" uniqKey="Bougrain L" first="Laurent" last="Bougrain">Laurent Bougrain</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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