Discrete motor imageries can be used to allow a faster detection

Sébastien Rimbert (1) , Oleksii Avilov (1) , Laurent Bougrain (1)

1 NEUROSYS - Analysis and modeling of neural systems by a system neuroscience approach

Motor imagery (MI) modifies the neural activity within the primary sensorimotor areas of the cortex and can be measured through the analysis of elec-troencephalographic (EEG) recordings. It is particularly interesting for Brain-Computer Interface (BCI) applications. In most MI-based BCI experimental paradigms, subjects realize continuous motor imagery (CMI), i.e. a repetitive and prolonged intention of movement, for a few seconds. The system detects the movement based on the event-related desynchronization and the event-related synchronization features in electroencephalographic signal. Currently, improving efficiency such as detecting faster a motor imagery is an important issue in BCI to avoid fatigue and boredom. The purpose of this study is to show the difference, in term of classification, between a discrete motor imagery, i.e. a single short MI, and a CMI. The results of experiments involving 16 healthy subjects show that a BCI based on DMI is as effective as a BCI based on CMI and could be used to allow a faster detection.

Domaines: Imagerie médicale; Intelligence artificielle [cs.AI]; Sciences cognitives

Sébastien Rimbert, Oleksii Avilov, Laurent Bougrain. Discrete motor imageries can be used to allow a faster detection. 7th Graz Brain-Computer Interface Conference 2017, Sep 2017, Graz, Austria. ⟨hal-01512407⟩