Hybrid BCI Coupling EEG and EMG for Severe Motor Disabilities

Abstract : In this paper, we are studying hybrid Brain-Computer Interfaces (BCI) coupling joystick data, electroencephalogram (EEG – electrical activity of the brain) and electromyogram (EMG – electrical activity of muscles) activities for severe motor disabilities. We are focusing our study on muscular activity as a control modality to interact with an application. We present our data processing and classification technique to detect right and left hand movements. EMG modality is well adapted for DMD patients, because less strength is needed to detect movements in contrast to conventional interfaces like joysticks. Across virtual reality tools, we believe that users will be more able to understand how to interact with such kind of interactive systems. This first part of our study report some very good results concerning the detection of hand movements, according to muscular channel, on healthy subjects.
Document type :
Conference papers
Liste complète des métadonnées

Cited literature [19 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01361926
Contributor : Alban Duprès <>
Submitted on : Monday, September 12, 2016 - 12:30:05 PM
Last modification on : Saturday, March 23, 2019 - 1:24:43 AM

Links full text

Identifiers

Collections

Citation

José Rouillard, Alban Duprès, François Cabestaing, Stéphanie Leclercq, Marie-Hélène Bekaert, et al.. Hybrid BCI Coupling EEG and EMG for Severe Motor Disabilities. AHFE 2015, Jul 2015, Las Vegas, United States. ⟨10.1016/j.promfg.2015.07.104⟩. ⟨hal-01361926⟩

Share

Metrics

Record views

212