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      Brain-computer interface enhanced by virtual reality training for controlling a lower limb exoskeleton

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          Summary

          This study explores the use of a brain-computer interface (BCI) based on motor imagery (MI) for the control of a lower limb exoskeleton to aid in motor recovery after a neural injury. The BCI was evaluated in ten able-bodied subjects and two patients with spinal cord injuries. Five able-bodied subjects underwent a virtual reality (VR) training session to accelerate training with the BCI. Results from this group were compared with a control group of five able-bodied subjects, and it was found that the employment of shorter training by VR did not reduce the effectiveness of the BCI and even improved it in some cases. Patients gave positive feedback about the system and were able to handle experimental sessions without reaching high levels of physical and mental exertion. These results are promising for the inclusion of BCI in rehabilitation programs, and future research should investigate the potential of the MI-based BCI system.

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          Highlights

          • Virtual reality training system to enhance subjects’ ability to perform motor imagery

          • Speed up brain-computer interfaces (BCIs) training

          • Closed-loop experiments for controlling a lower-limb exoskeleton by means of BCI

          • Assessment of the BCI to control an exoskeleton with patients with spinal cord injury

          Abstract

          Applied sciences; Engineering; Biomedical Engineering; Control engineering; Robotics

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          Most cited references43

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          EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

          We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
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            Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research

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              Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks.

              We studied the reactivity of EEG rhythms (mu rhythms) in association with the imagination of right hand, left hand, foot, and tongue movement with 60 EEG electrodes in nine able-bodied subjects. During hand motor imagery, the hand mu rhythm blocked or desynchronized in all subjects, whereas an enhancement of the hand area mu rhythm was observed during foot or tongue motor imagery in the majority of the subjects. The frequency of the most reactive components was 11.7 Hz +/- 0.4 (mean +/- SD). While the desynchronized components were broad banded and centered at 10.9 Hz +/- 0.9, the synchronized components were narrow banded and displayed higher frequencies at 12.0 Hz +/- 1.0. The discrimination between the four motor imagery tasks based on classification of single EEG trials improved when, in addition to event-related desynchronization (ERD), event-related synchronization (ERS) patterns were induced in at least one or two tasks. This implies that such EEG phenomena may be utilized in a multi-class brain-computer interface (BCI) operated simply by motor imagery.
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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                15 April 2023
                19 May 2023
                15 April 2023
                : 26
                : 5
                : 106675
                Affiliations
                [1 ]Brain-Machine Interface System Lab, Miguel Hernández University of Elche, Elche, Spain
                [2 ]Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, Elche, Spain
                [3 ]Valencian Graduate School and Research Network of Artificial Intelligence (valgrAI), Valencia, Spain
                [4 ]The European University of Brain and Technology (NeurotechEU)
                [5 ]Hospital Nacional de Parapléjicos de Toledo, Toledo, Spain
                Author notes
                []Corresponding author lferrero@ 123456umh.es
                [6]

                Lead contact

                Article
                S2589-0042(23)00752-6 106675
                10.1016/j.isci.2023.106675
                10214472
                37250318
                24ffe982-26df-44b8-a869-c67ac834cd42
                © 2023 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 25 September 2022
                : 6 March 2023
                : 11 April 2023
                Categories
                Article

                applied sciences,engineering,biomedical engineering,control engineering,robotics

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