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      Estimation of Neuromuscular Primitives from EEG Slow Cortical Potentials in Incomplete Spinal Cord Injury Individuals for a New Class of Brain-Machine Interfaces

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          Abstract

          One of the current challenges in human motor rehabilitation is the robust application of Brain-Machine Interfaces to assistive technologies such as powered lower limb exoskeletons. Reliable decoding of motor intentions and accurate timing of the robotic device actuation is fundamental to optimally enhance the patient's functional improvement. Several studies show that it may be possible to extract motor intentions from electroencephalographic (EEG) signals. These findings, although notable, suggests that current techniques are still far from being systematically applied to an accurate real-time control of rehabilitation or assistive devices. Here we propose the estimation of spinal primitives of multi-muscle control from EEG, using electromyography (EMG) dimensionality reduction as a solution to increase the robustness of the method. We successfully apply this methodology, both to healthy and incomplete spinal cord injury (SCI) patients, to identify muscle contraction during periodical knee extension from the EEG. We then introduce a novel performance metric, which accurately evaluates muscle primitive activations.

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          What is the Bereitschaftspotential?

          Since discovery of the slow negative electroencephalographic (EEG) activity preceding self-initiated movement by Kornhuber and Deecke [Kornhuber HH, Deecke L. Hirnpotentialänderungen bei Willkurbewegungen und passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale. Pflugers Archiv 1965;284:1-17], various source localization techniques in normal subjects and epicortical recording in epilepsy patients have disclosed the generator mechanisms of each identifiable component of movement-related cortical potentials (MRCPs) to some extent. The initial slow segment of BP, called 'early BP' in this article, begins about 2 s before the movement onset in the pre-supplementary motor area (pre-SMA) with no site-specificity and in the SMA proper according to the somatotopic organization, and shortly thereafter in the lateral premotor cortex bilaterally with relatively clear somatotopy. About 400 ms before the movement onset, the steeper negative slope, called 'late BP' in this article (also referred to as NS'), occurs in the contralateral primary motor cortex (M1) and lateral premotor cortex with precise somatotopy. These two phases of BP are differentially influenced by various factors, especially by complexity of the movement which enhances only the late BP. Event-related desynchronization (ERD) of beta frequency EEG band before self-initiated movements shows a different temporospatial pattern from that of the BP, suggesting different neuronal mechanisms for the two. BP has been applied for investigating pathophysiology of various movement disorders. Volitional motor inhibition or muscle relaxation is preceded by BP quite similar to that preceding voluntary muscle contraction. Since BP of typical waveforms and temporospatial pattern does not occur before organic involuntary movements, BP is used for detecting the participation of the 'voluntary motor system' in the generation of apparently involuntary movements in patients with psychogenic movement disorders. In view of Libet et al.'s report [Libet B, Gleason CA, Wright EW, Pearl DK. Time of conscious intention to act in relation to onset of cerebral activity (readiness-potential). The unconscious initiation of a freely voluntary act. Brain 1983;106:623-642] that the awareness of intention to move occurred much later than the onset of BP, the early BP might reflect, physiologically, slowly increasing cortical excitability and, behaviorally, subconscious readiness for the forthcoming movement. Whether the late BP reflects conscious preparation for intended movement or not remains to be clarified.
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            Reconstructing three-dimensional hand movements from noninvasive electroencephalographic signals.

            It is generally thought that the signal-to-noise ratio, the bandwidth, and the information content of neural data acquired via noninvasive scalp electroencephalography (EEG) are insufficient to extract detailed information about natural, multijoint movements of the upper limb. Here, we challenge this assumption by continuously decoding three-dimensional (3D) hand velocity from neural data acquired from the scalp with 55-channel EEG during a 3D center-out reaching task. To preserve ecological validity, five subjects self-initiated reaches and self-selected targets. Eye movements were controlled so they would not confound the interpretation of the results. With only 34 sensors, the correlation between measured and reconstructed velocity profiles compared reasonably well to that reported by studies that decoded hand kinematics from neural activity acquired intracranially. We subsequently examined the individual contributions of EEG sensors to decoding to find substantial involvement of scalp areas over the sensorimotor cortex contralateral to the reaching hand. Using standardized low-resolution brain electromagnetic tomography (sLORETA), we identified distributed current density sources related to hand velocity in the contralateral precentral gyrus, postcentral gyrus, and inferior parietal lobule. Furthermore, we discovered that movement variability negatively correlated with decoding accuracy, a finding to consider during the development of brain-computer interface systems. Overall, the ability to continuously decode 3D hand velocity from EEG during natural, center-out reaching holds promise for the furtherance of noninvasive neuromotor prostheses for movement-impaired individuals.
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              Precise temporal association between cortical potentials evoked by motor imagination and afference induces cortical plasticity.

              In monkeys, the repeated activation of somatosensory afferents projecting onto the motor cortex (M1) has a pivotal role in motor skill learning. Here we investigate if sensory feedback that is artificially generated at specific times during imagination of a dorsiflexion task leads to reorganization of the human M1. The common peroneal nerve was stimulated to generate an afferent volley timed to arrive during specific phases of the cortical potential generated when a movement was imagined (50 repetitions). The change in the output of M1 was quantified by applying single transcranial magnetic stimuli to the area of M1 controlling the tibialis anterior muscle. The results demonstrated that the concomitance between the cognitive process of movement (motor imagination) and the ascending volley due to the peripheral nerve stimulation can lead to significant increases in cortical excitability. These increases were critically dependent on the timing between the peripherally generated afferent volley and the cortical potential generated during the imagined movement. Only if the afferent volley arrived during the peak negative deflection of the potential, were significant alterations in motor cortical output attained. These results demonstrate that an artificially generated signal (the peripheral afferent volley) can interact with a physiologically generated signal in humans leading to plastic changes within the M1, the final output stage for movement generation within the human brain. The results presented may have implications in systems for artificially inducing cortical plasticity in patients with motor impairments (neuromodulation).
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                Author and article information

                Contributors
                Journal
                Front Comput Neurosci
                Front Comput Neurosci
                Front. Comput. Neurosci.
                Frontiers in Computational Neuroscience
                Frontiers Media S.A.
                1662-5188
                25 January 2018
                2018
                : 12
                : 3
                Affiliations
                [1] 1AUROVA Group, Department of Physics, Systems Engineering and Signal Theory, University of Alicante , San Vicente del Raspeig, Spain
                [2] 2Brain-Machine Interface Systems Lab, Miguel Hernández University , Elche, Spain
                [3] 3Chair in Neurorehabilitation Engineering, Department of Bioengineering, Imperial College , London, United Kingdom
                [4] 4Department of Biomechanical Engineering, University of Twente , Enschede, Netherlands
                Author notes

                Edited by: Florentin Wörgötter, University of Göttingen, Germany

                Reviewed by: Robert H. Lee, Emory University, United States; Jan-Matthias Braun, University of Southern Denmark Odense, Denmark

                *Correspondence: Massimo Sartori m.sartori@ 123456utwente.nl
                Article
                10.3389/fncom.2018.00003
                5788900
                29422842
                15c621c3-64c9-49a1-83c8-83593a4b6928
                Copyright © 2018 Úbeda, Azorín, Farina and Sartori.

                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) or licensor 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
                : 28 February 2017
                : 04 January 2018
                Page count
                Figures: 6, Tables: 2, Equations: 2, References: 29, Pages: 11, Words: 7344
                Funding
                Funded by: Generalitat Valenciana 10.13039/501100003359
                Award ID: APOSTD/2015/104
                Funded by: European Research Council 10.13039/501100000781
                Award ID: 267888
                Funded by: Ministerio de Economía y Competitividad 10.13039/501100003329
                Award ID: DPI2014-58431-C4-2-R
                Funded by: Seventh Framework Programme 10.13039/501100004963
                Award ID: 611695
                Categories
                Neuroscience
                Original Research

                Neurosciences
                brain-machine interface,muscle primitives,corticospinal mapping,linear decoders,gait rehabilitation,lower-limb exoskeletons

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