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      Low Intensity Focused tDCS Over the Motor Cortex Shows Inefficacy to Improve Motor Imagery Performance

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          Abstract

          Transcranial direct current stimulation (tDCS) is a brain stimulation technique that can enhance motor activity by stimulating the motor path. Thus, tDCS has the potential of improving the performance of brain-computer interfaces during motor neurorehabilitation. tDCS effects depend on several aspects, including the current density, which usually varies between 0.02 and 0.08 mA/cm 2, and the location of the stimulation electrodes. Hence, testing tDCS montages at several current levels would allow the selection of current parameters for improving stimulation outcomes and the comparison of montages. In a previous study, we found that cortico-cerebellar tDCS shows potential of enhancing right-hand motor imagery. In this paper, we aim to evaluate the effects of the focal stimulation of the motor cortex over motor imagery. In particular, the effect of supplying tDCS with a 4 × 1 ring montage, which consists in placing an anode on the motor cortex and four cathodes around it, over motor imagery was assessed with different current densities. Electroencephalographic (EEG) classification into rest or right-hand/feet motor imagery was evaluated on five healthy subjects for two stimulation schemes: applying tDCS for 10 min on the (1) right-hand or (2) feet motor cortex before EEG recording. Accuracy differences related to the tDCS intensity, as well as μ and β band power changes, were tested for each subject and tDCS modality. In addition, a simulation of the electric field induced by the montage was used to describe its effect on the brain. Results show no improvement trends on classification for the evaluated currents, which is in accordance with the observation of variable EEG band power results despite the focused stimulation. The lack of effects is probably related to the underestimation of the current intensity required to apply a particular current density for small electrodes and the relatively short inter-electrode distance. Hence, higher current intensities should be evaluated in the future for this montage.

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          10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems.

          With the advent of multi-channel EEG hardware systems and the concurrent development of topographic and tomographic signal source localization methods, the international 10/20 system, a standard system for electrode positioning with 21 electrodes, was extended to higher density electrode settings such as 10/10 and 10/5 systems, allowing more than 300 electrode positions. However, their effectiveness as relative head-surface-based positioning systems has not been examined. We previously developed a virtual 10/20 measurement algorithm that can analyze any structural MR head and brain image. Extending this method to the virtual 10/10 and 10/5 measurement algorithms, we analyzed the MR images of 17 healthy subjects. The acquired scalp positions of the 10/10 and 10/5 systems were normalized to the Montreal Neurological Institute (MNI) stereotactic coordinates and their spatial variability was assessed. We described and examined the effects of spatial variability due to the selection of positioning systems and landmark placement strategies. As long as a detailed rule for a particular system was provided, it yielded precise landmark positions on the scalp. Moreover, we evaluated the effective spatial resolution of 329 scalp landmark positions of the 10/5 system for multi-subject studies. As long as a detailed rule for landmark setting was provided, 241 scalp positions could be set effectively when there was no overlapping of two neighboring positions. Importantly, 10/10 positions could be well separated on a scalp without overlapping. This study presents a referential framework for establishing the effective spatial resolutions of 10/20, 10/10, and 10/5 systems as relative head-surface-based positioning systems.
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            Neurophysiological predictor of SMR-based BCI performance.

            Brain-computer interfaces (BCIs) allow a user to control a computer application by brain activity as measured, e.g., by electroencephalography (EEG). After about 30years of BCI research, the success of control that is achieved by means of a BCI system still greatly varies between subjects. For about 20% of potential users the obtained accuracy does not reach the level criterion, meaning that BCI control is not accurate enough to control an application. The determination of factors that may serve to predict BCI performance, and the development of methods to quantify a predictor value from psychological and/or physiological data serve two purposes: a better understanding of the 'BCI-illiteracy phenomenon', and avoidance of a costly and eventually frustrating training procedure for participants who might not obtain BCI control. Furthermore, such predictors may lead to approaches to antagonize BCI illiteracy. Here, we propose a neurophysiological predictor of BCI performance which can be determined from a two minute recording of a 'relax with eyes open' condition using two Laplacian EEG channels. A correlation of r=0.53 between the proposed predictor and BCI feedback performance was obtained on a large data base with N=80 BCI-naive participants in their first session with the Berlin brain-computer interface (BBCI) system which operates on modulations of sensory motor rhythms (SMRs). Copyright 2010 Elsevier Inc. All rights reserved.
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              Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG.

              Single-trial motor imagery classification is an integral part of a number of brain-computer interface (BCI) systems. The possible significance of the kind of imagery, involving rather kinesthetic or visual representations of actions, was addressed using the following experimental conditions: kinesthetic motor imagery (MIK), visual-motor imagery (MIV), motor execution (ME) and observation of movement (OOM). Based on multi-channel EEG recordings in 14 right-handed participants, we applied a learning classifier, the distinction sensitive learning vector quantization (DSLVQ) to identify relevant features (i.e., frequency bands, electrode sites) for recognition of the respective mental states. For ME and OOM, the overall classification accuracies were about 80%. The rates obtained for MIK (67%) were better than the results of MIV (56%). Moreover, the focus of activity during kinesthetic imagery was found close to the sensorimotor hand area, whereas visual-motor imagery did not reveal a clear spatial pattern. Consequently, to improve motor-imagery-based BCI control, user training should emphasize kinesthetic experiences instead of visual representations of actions.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                06 July 2017
                2017
                : 11
                : 391
                Affiliations
                [1] 1Monterrey's Unit, Biomedical Signal Processing Laboratory, Center for Research and Advanced Studies Apodaca, Mexico
                [2] 2Brain-Machine Interface Systems Lab, Systems Engineering and Automation Department, Universidad Miguel Hernández de Elche Elche, Spain
                Author notes

                Edited by: Jose Manuel Ferrandez, Universidad Politécnica de Cartagena, Spain

                Reviewed by: Monzurul Alam, Hong Kong Polytechnic University, Hong Kong; Antonio Fernández-Caballero, Universidad de Castilla-La Mancha, Spain

                *Correspondence: Eduardo Iáñez eianez@ 123456umh.es

                This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2017.00391
                5498512
                28729822
                3db4bc02-4d04-437c-956a-cd2ac552e23c
                Copyright © 2017 Angulo-Sherman, Rodríguez-Ugarte, Iáñez and Azorín.

                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
                : 26 April 2017
                : 22 June 2017
                Page count
                Figures: 9, Tables: 4, Equations: 3, References: 29, Pages: 12, Words: 8268
                Funding
                Funded by: Ministerio de Economía y Competitividad 10.13039/501100003329
                Award ID: DPI2014-58431-C4-2-R
                Funded by: European Regional Development Fund 10.13039/501100008530
                Award ID: DPI2014-58431-C4-2-R
                Funded by: Consejo Nacional de Ciencia y Tecnología 10.13039/501100003141
                Award ID: 369756
                Categories
                Neuroscience
                Original Research

                Neurosciences
                bci,tdcs,motor imagery,current density,neurorehabilitation,4 × 1 ring
                Neurosciences
                bci, tdcs, motor imagery, current density, neurorehabilitation, 4 × 1 ring

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