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      Feel Your Reach: An EEG-Based Framework to Continuously Detect Goal-Directed Movements and Error Processing to Gate Kinesthetic Feedback Informed Artificial Arm Control

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          Abstract

          Establishing the basic knowledge, methodology, and technology for a framework for the continuous decoding of hand/arm movement intention was the aim of the ERC-funded project “Feel Your Reach”. In this work, we review the studies and methods we performed and implemented in the last 6 years, which build the basis for enabling severely paralyzed people to non-invasively control a robotic arm in real-time from electroencephalogram (EEG). In detail, we investigated goal-directed movement detection, decoding of executed and attempted movement trajectories, grasping correlates, error processing, and kinesthetic feedback. Although we have tested some of our approaches already with the target populations, we still need to transfer the “Feel Your Reach” framework to people with cervical spinal cord injury and evaluate the decoders’ performance while participants attempt to perform upper-limb movements. While on the one hand, we made major progress towards this ambitious goal, we also critically discuss current limitations.

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          Most cited references130

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          Event-related EEG/MEG synchronization and desynchronization: basic principles.

          An internally or externally paced event results not only in the generation of an event-related potential (ERP) but also in a change in the ongoing EEG/MEG in form of an event-related desynchronization (ERD) or event-related synchronization (ERS). The ERP on the one side and the ERD/ERS on the other side are different responses of neuronal structures in the brain. While the former is phase-locked, the latter is not phase-locked to the event. The most important difference between both phenomena is that the ERD/ERS is highly frequency band-specific, whereby either the same or different locations on the scalp can display ERD and ERS simultaneously. Quantification of ERD/ERS in time and space is demonstrated on data from a number of movement experiments.
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            Brainstorm: A User-Friendly Application for MEG/EEG Analysis

            Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI).
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              High-performance neuroprosthetic control by an individual with tetraplegia.

              Paralysis or amputation of an arm results in the loss of the ability to orient the hand and grasp, manipulate, and carry objects, functions that are essential for activities of daily living. Brain-machine interfaces could provide a solution to restoring many of these lost functions. We therefore tested whether an individual with tetraplegia could rapidly achieve neurological control of a high-performance prosthetic limb using this type of an interface. We implanted two 96-channel intracortical microelectrodes in the motor cortex of a 52-year-old individual with tetraplegia. Brain-machine-interface training was done for 13 weeks with the goal of controlling an anthropomorphic prosthetic limb with seven degrees of freedom (three-dimensional translation, three-dimensional orientation, one-dimensional grasping). The participant's ability to control the prosthetic limb was assessed with clinical measures of upper limb function. This study is registered with ClinicalTrials.gov, NCT01364480. The participant was able to move the prosthetic limb freely in the three-dimensional workspace on the second day of training. After 13 weeks, robust seven-dimensional movements were performed routinely. Mean success rate on target-based reaching tasks was 91·6% (SD 4·4) versus median chance level 6·2% (95% CI 2·0-15·3). Improvements were seen in completion time (decreased from a mean of 148 s [SD 60] to 112 s [6]) and path efficiency (increased from 0·30 [0·04] to 0·38 [0·02]). The participant was also able to use the prosthetic limb to do skilful and coordinated reach and grasp movements that resulted in clinically significant gains in tests of upper limb function. No adverse events were reported. With continued development of neuroprosthetic limbs, individuals with long-term paralysis could recover the natural and intuitive command signals for hand placement, orientation, and reaching, allowing them to perform activities of daily living. Defense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute. Copyright © 2013 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                11 March 2022
                2022
                : 16
                : 841312
                Affiliations
                [1] 1Institute of Neural Engineering, Graz University of Technology , Graz, Austria
                [2] 2BioTechMed , Graz, Austria
                [3] 3RIKEN Center for Advanced Intelligence Project , Kyoto, Japan
                [4] 4Brain-State Decoding Lab, Albert-Ludwigs-Universität Freiburg , Freiburg, Germany
                [5] 5Stereotaxy and Functional Neurosurgery Department, Uniklinik Freiburg , Freiburg, Germany
                [6] 6Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, University of Valladolid , Valladolid, Spain
                [7] 7Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN) , Valladolid, Spain
                [8] 8Faculty of Engineering, Department of Computer Engineering, University of Rijeka , Rijeka, Croatia
                Author notes

                Edited by: Sung Chan Jun, Gwangju Institute of Science and Technology, South Korea

                Reviewed by: Surjo R. Soekadar, Charité Universitätsmedizin Berlin, Germany; Baoguo Xu, Southeast University, China

                *Correspondence: Gernot R. Müller-Putz gernot.mueller@ 123456tugraz.at

                Specialty section: This article was submitted to Brain-Computer Interfaces, a section of the journal Frontiers in Human Neuroscience

                Article
                10.3389/fnhum.2022.841312
                8961864
                35360289
                1258c060-3e8f-49e8-9a41-97111419cdec
                Copyright © 2022 Müller-Putz, Kobler, Pereira, Lopes-Dias, Hehenberger, Mondini, Martínez-Cagigal, Srisrisawang, Pulferer, Batistić and Sburlea.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 22 December 2021
                : 16 February 2022
                Page count
                Figures: 9, Tables: 0, Equations: 0, References: 131, Pages: 21, Words: 16187
                Funding
                Funded by: European Research Council, doi 10.13039/501100000781;
                Categories
                Human Neuroscience
                Review

                Neurosciences
                electroencephalogram (eeg),brain-computer interface (bci),goal-directed movement,movement detection,trajectory decoding,error-related potential,kinesthetic feedback,spinal cord injury (sci)

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