Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns.
Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy-EEG), which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used...
Main Authors: | Camille Jeunet, Bernard N'Kaoua, Sriram Subramanian, Martin Hachet, Fabien Lotte |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2015-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC4666487?pdf=render |
Similar Items
-
Why we should systematically assess, control and report somatosensory impairments in BCI-based motor rehabilitation after stroke studies
by: Léa Pillette, et al.
Published: (2020-01-01) -
Multi-Session Influence of Two Modalities of Feedback and Their Order of Presentation on MI-BCI User Training
by: Léa Pillette, et al.
Published: (2021-03-01) -
Understanding & Improving Mental-Imagery Based Brain-Computer Interface (Mi-Bci) User-Training : towards A New Generation Of Reliable, Efficient & Accessible Brain- Computer Interfaces
by: Jeunet, Camille
Published: (2016) -
Estimation of Distances in 3D by Orthodontists Using Digital Models
by: Masrour Makaremi, et al.
Published: (2021-09-01) -
Long-Term BCI Training of a Tetraplegic User: Adaptive Riemannian Classifiers and User Training
by: Camille Benaroch, et al.
Published: (2021-03-01)