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      HD-EEG Based Classification of Motor-Imagery Related Activity in Patients With Spinal Cord Injury

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          Abstract

          Brain computer interfaces (BCIs) are thought to revolutionize rehabilitation after SCI, e.g., by controlling neuroprostheses, exoskeletons, functional electrical stimulation, or a combination of these components. However, most BCI research was performed in healthy volunteers and it is unknown whether these results can be translated to patients with spinal cord injury because of neuroplasticity. We sought to examine whether high-density EEG (HD-EEG) could improve the performance of motor-imagery classification in patients with SCI. We recorded HD-EEG with 256 channels in 22 healthy controls and 7 patients with 14 recordings (4 patients had more than one recording) in an event related design. Participants were instructed acoustically to either imagine, execute, or observe foot and hand movements, or to rest. We calculated Fast Fourier Transform (FFT) and full frequency directed transfer function (ffDTF) for each condition and classified conditions pairwise with support vector machines when using only 2 channels over the sensorimotor area, full 10-20 montage, high-density montage of the sensorimotor cortex, and full HD-montage. Classification accuracies were comparable between patients and controls, with an advantage for controls for classifications that involved the foot movement condition. Full montages led to better results for both groups ( p < 0.001), and classification accuracies were higher for FFT than for ffDTF ( p < 0.001), for which the feature vector might be too long. However, full-montage 10–20 montage was comparable to high-density configurations. Motor-imagery driven control of neuroprostheses or BCI systems may perform as well in patients as in healthy volunteers with adequate technical configuration. We suggest the use of a whole-head montage and analysis of a broad frequency range.

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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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            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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              Scalp electrode impedance, infection risk, and EEG data quality.

              Breaking the skin when applying scalp electroencephalographic (EEG) electrodes creates the risk of infection from blood-born pathogens such as HIV, Hepatitis-C, and Creutzfeldt-Jacob Disease. Modern engineering principles suggest that excellent EEG signals can be collected with high scalp impedance ( approximately 40 kOmega) without scalp abrasion. The present study was designed to evaluate the effect of electrode-scalp impedance on EEG data quality. The first section of the paper reviews electrophysiological recording with modern high input-impedance differential amplifiers and subject isolation, and explains how scalp-electrode impedance influences EEG signal amplitude and power line noise. The second section of the paper presents an experimental study of EEG data quality as a function of scalp-electrode impedance for the standard frequency bands in EEG and event-related potential (ERP) recordings and for 60 Hz noise. There was no significant amplitude change in any EEG frequency bands as scalp-electrode impedance increased from less than 10 kOmega (abraded skin) to 40 kOmega (intact skin). 60 Hz was nearly independent of impedance mismatch, suggesting that capacitively coupled noise appearing differentially across mismatched electrode impedances did not contribute substantially to the observed 60 Hz noise levels. With modern high input-impedance amplifiers and accurate digital filters for power line noise, high-quality EEG can be recorded without skin abrasion.
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                Author and article information

                Contributors
                Journal
                Front Neurol
                Front Neurol
                Front. Neurol.
                Frontiers in Neurology
                Frontiers Media S.A.
                1664-2295
                19 November 2018
                2018
                : 9
                : 955
                Affiliations
                [1] 1Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of Salzburg , Salzburg, Austria
                [2] 2Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University of Salzburg , Salzburg, Austria
                [3] 3Department of Computer Sciences, Paris-Lodron University of Salzburg , Salzburg, Austria
                [4] 4Department of Mathematics, Paris-Lodron University of Salzburg , Salzburg, Austria
                [5] 5Department of Neurology, Franz Tappeiner Hospital , Merano, Italy
                Author notes

                Edited by: Mikhail Lebedev, Duke University, United States

                Reviewed by: Lohitash Karumbaiah, University of Georgia, United States; Marco Aurelio M. Freire, University of the State of Rio Grande do Norte, Brazil

                *Correspondence: Stefan Leis s.leis@ 123456salk.at

                This article was submitted to Neuroprosthetics, a section of the journal Frontiers in Neurology

                Article
                10.3389/fneur.2018.00955
                6252382
                30510537
                cf4dc43b-1218-42d9-ac39-9aa485958bea
                Copyright © 2018 Höller, Thomschewski, Uhl, Bathke, Nardone, Leis, Trinka and Höller.

                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
                : 20 October 2017
                : 24 October 2018
                Page count
                Figures: 9, Tables: 6, Equations: 1, References: 94, Pages: 21, Words: 15475
                Categories
                Neurology
                Original Research

                Neurology
                spinal cord injury,hd-eeg,connectivity,motor imagery,bci,ffdtf,fft
                Neurology
                spinal cord injury, hd-eeg, connectivity, motor imagery, bci, ffdtf, fft

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