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      Providing Task Instructions During Motor Training Enhances Performance and Modulates Attentional Brain Networks

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          Abstract

          Learning a new motor task is a complex cognitive and motor process. Especially early during motor learning, cognitive functions such as attentional engagement, are essential, e.g., to discover relevant visual stimuli. Drawing participant’s attention towards task-relevant stimuli—e.g., with task instructions using visual cues or explicit written information—is a common practice to support cognitive engagement during training and, hence, accelerate motor learning. However, there is little scientific evidence about how visually cued or written task instructions affect attentional brain networks during motor learning. In this experiment, we trained 36 healthy participants in a virtual motor task: surfing waves by steering a boat with a joystick. We measured the participants’ motor performance and observed attentional brain networks using alpha-band electroencephalographic (EEG) activity before and after training. Participants received one of the following task instructions during training: (1) No explicit task instructions and letting participants surf freely (implicit training; IMP); (2) Task instructions provided through explicit visual cues (explicit-implicit training; E-IMP); or (3) through explicit written commands (explicit training; E). We found that providing task instructions during training (E and E-IMP) resulted in less post-training motor variability—linked to enhanced performance—compared to training without instructions (IMP). After training, participants trained with visual cues (E-IMP) enhanced the alpha-band strength over parieto-occipital and frontal brain areas at wave onset. In contrast, participants who trained with explicit commands (E) showed decreased fronto-temporal alpha activity. Thus, providing task instructions in written (E) or using visual cues (E-IMP) leads to similar motor performance improvements by enhancing activation on different attentional networks. While training with visual cues (E-IMP) may be associated with visuo-attentional processes, verbal-analytical processes may be more prominent when written explicit commands are provided (E). Together, we suggest that training parameters such as task instructions, modulate the attentional networks observed during motor practice and may support participant’s cognitive engagement, compared to training without instructions.

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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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            The assessment and analysis of handedness: The Edinburgh inventory

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              Alpha-band oscillations, attention, and controlled access to stored information

              Alpha-band oscillations are the dominant oscillations in the human brain and recent evidence suggests that they have an inhibitory function. Nonetheless, there is little doubt that alpha-band oscillations also play an active role in information processing. In this article, I suggest that alpha-band oscillations have two roles (inhibition and timing) that are closely linked to two fundamental functions of attention (suppression and selection), which enable controlled knowledge access and semantic orientation (the ability to be consciously oriented in time, space, and context). As such, alpha-band oscillations reflect one of the most basic cognitive processes and can also be shown to play a key role in the coalescence of brain activity in different frequencies.
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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
                09 December 2021
                2021
                : 15
                : 755721
                Affiliations
                [1] 1Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern , Bern, Switzerland
                [2] 2Psychosomatic Medicine, Department of Neurology, Inselspital, Bern University Hospital, University of Bern , Bern, Switzerland
                [3] 3Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern , Bern, Switzerland
                [4] 4Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern , Bern, Switzerland
                [5] 5Perception and Eye Movement Laboratory, Department of Neurology and BioMedical Research, University of Bern , Bern, Switzerland
                [6] 6Department of Neurology, Inselspital, Bern University Hospital, University of Bern , Bern, Switzerland
                [7] 7Department of Cognitive Robotics, Delft University of Technology , Delft, Netherlands
                Author notes

                Edited by: Birgitta Dresp-Langley, Centre National de la Recherche Scientifique (CNRS), France

                Reviewed by: Anıl Ufuk Batmaz, Kadir Has University, Turkey; Waldemar Karwowski, University of Central Florida, United States

                *Correspondence: Joaquin Penalver-Andres, joaquin.penalverdeandres@ 123456unibe.ch

                These authors share first authorship

                This article was submitted to Perception Science, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2021.755721
                8695982
                34955719
                61fa42f8-4770-4ea6-8589-8dab32d68146
                Copyright © 2021 Penalver-Andres, Buetler, Koenig, Müri and Marchal-Crespo.

                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
                : 09 August 2021
                : 18 October 2021
                Page count
                Figures: 6, Tables: 1, Equations: 2, References: 81, Pages: 19, Words: 16491
                Funding
                Funded by: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, doi 10.13039/501100001711;
                Award ID: PP00P2163800
                Funded by: Universität Bern, doi 10.13039/100009068;
                Categories
                Neuroscience
                Original Research

                Neurosciences
                motor learning and control,cognitive neuroscience,neural biomarkers,variability,attention/working memory,eeg oscillations,instructions and feedback

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