Multiple Kernel Based Region Importance Learning for Neural Classification of Gait States from EEG Signals

• Published in: Frontiers in neuroscience Vol. 11; p. 170
• Main Authors: Zhang, Yuhang, Prasad, Saurabh, Kilicarslan, Atilla, Contreras-Vidal, Jose L.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 03.04.2017; Frontiers Media S.A
• Subjects: Adaptation; Artificial intelligence; Brain research; Cell cycle; Classification; Computer engineering; Cortex; EEG; Exoskeleton; Fitness equipment; Gait; Kinematics; Machine learning; Neuroplasticity; Neuroscience; Paralysis; Pattern recognition; Rehabilitation; Spinal cord injuries; Stroke; Walking; United States > US; electroencephalography (EEG); neural classification; brain machine interface (BMI); multiple kernel learning; machine learning

With the development of Brain Machine Interface (BMI) systems, people with motor disabilities are able to control external devices to help them restore movement abilities. Longitudinal validation of these systems is critical not only to assess long-term performance reliability but also to investigat...