International audienceA 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 ...
AbstractTime domain features of slow potentials ≤2Hz) during periodic movement and motor imagery at ...
Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into acti...
Motor imagery classification using electroencephalography is based on feature extraction over a leng...
International audienceMotor imagery (MI) modifies the neural activity within the primary sensorimoto...
International audienceIn most Brain-Computer Interfaces (BCI) experimental paradigms based on Motor ...
Motor imagery modifies the neural activity within the primary sensorimotor areas of the cortex in a ...
International audience— Imaginary motor tasks cause brain oscillations that can be detected through ...
International audienceNon-invasive Brain-Computer Interfaces (BCIs) based on motor imagery (MI) task...
International audienceLimb movement execution or imagination induce sensorimotor rhythms that can be...
International audienceDespite current research, the relationship between the variability of Event-Re...
In this paper, the accuracy in classifying Motor Imagery (MI) tasks for a Brain-Computer Interface (...
Motor imagery (MI) brain-computer interfaces (BCIs) translate a subject’s motor intention to a comma...
We present the results from three motor imagery-based brain-computer interface experiments. Brain si...
International audienceIn this article, we study how combined motor imageries can be detected to deli...
Phase synchronisation between different neural groups is considered an important source of informati...
AbstractTime domain features of slow potentials ≤2Hz) during periodic movement and motor imagery at ...
Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into acti...
Motor imagery classification using electroencephalography is based on feature extraction over a leng...
International audienceMotor imagery (MI) modifies the neural activity within the primary sensorimoto...
International audienceIn most Brain-Computer Interfaces (BCI) experimental paradigms based on Motor ...
Motor imagery modifies the neural activity within the primary sensorimotor areas of the cortex in a ...
International audience— Imaginary motor tasks cause brain oscillations that can be detected through ...
International audienceNon-invasive Brain-Computer Interfaces (BCIs) based on motor imagery (MI) task...
International audienceLimb movement execution or imagination induce sensorimotor rhythms that can be...
International audienceDespite current research, the relationship between the variability of Event-Re...
In this paper, the accuracy in classifying Motor Imagery (MI) tasks for a Brain-Computer Interface (...
Motor imagery (MI) brain-computer interfaces (BCIs) translate a subject’s motor intention to a comma...
We present the results from three motor imagery-based brain-computer interface experiments. Brain si...
International audienceIn this article, we study how combined motor imageries can be detected to deli...
Phase synchronisation between different neural groups is considered an important source of informati...
AbstractTime domain features of slow potentials ≤2Hz) during periodic movement and motor imagery at ...
Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into acti...
Motor imagery classification using electroencephalography is based on feature extraction over a leng...