EEG-Based BCI Control Schemes for Lower-Limb Assistive-Robots

Tariq, M, Trivailo, P and Simic, M 2018, 'EEG-Based BCI Control Schemes for Lower-Limb Assistive-Robots', Frontiers in Human Neuroscience, vol. 12, pp. 1-20.

Document type: Journal Article
Collection: Journal Articles

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Title EEG-Based BCI Control Schemes for Lower-Limb Assistive-Robots
Author(s) Tariq, M
Trivailo, P
Simic, M
Year 2018
Journal name Frontiers in Human Neuroscience
Volume number 12
Start page 1
End page 20
Total pages 20
Publisher Frontiers Research Foundation
Abstract Over recent years, brain-computer interface (BCI) has emerged as an alternative communication system between the human brain and an output device. Deciphered intents, after detecting electrical signals from the human scalp, are translated into control commands used to operate external devices, computer displays and virtual objects in the real-time. BCI provides an augmentative communication by creating a muscle-free channel between the brain and the output devices, primarily for subjects having neuromotor disorders, or trauma to nervous system, notably spinal cord injuries (SCI), and subjects with unaffected sensorimotor functions but disarticulated or amputated residual limbs. This review identifies the potentials of electroencephalography (EEG) based BCI applications for locomotion and mobility rehabilitation. Patients could benefit from its advancements such as, wearable lower-limb (LL) exoskeletons, orthosis, prosthesis, wheelchairs, and assistive-robot devices. The EEG communication signals employed by the aforementioned applications that also provide feasibility for future development in the field are sensorimotor rhythms (SMR), event-related potentials (ERP) and visual evoked potentials (VEP). The review is an effort to progress the development of user's mental task related to LL for BCI reliability and confidence measures. As a novel contribution, the reviewed BCI control paradigms for wearable LL and assistive-robots are presented by a general control framework fitting in hierarchical layers. It reflects informatic interactions, between the user, the BCI operator, the shared controller, the robotic device and the environment. Each sub layer of the BCI operator is discussed in detail, highlighting the feature extraction, classification and execution methods employed by the various systems. All applications' key features and their interaction with the environment are reviewed for the EEG-based activity mode recognition, and presented in form of a table. It is
Subject Biomechanical Engineering
Rehabilitation Engineering
Manufacturing Robotics and Mechatronics (excl. Automotive Mechatronics)
Keyword(s) Brain-computer interface
Electroencephalography (EEG)
Spinal cord injury
Assistive-robot devices.
DOI - identifier 10.3389/fnhum.2018.00312
Copyright notice Copyright © 2018 Tariq, Trivailo and Simic. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
ISSN 1662-5161
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Citation counts: TR Web of Science Citation Count  Cited 11 times in Thomson Reuters Web of Science Article | Citations
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