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      Improving Challenge/Skill Ratio in a Multimodal Interface by Simultaneously Adapting Game Difficulty and Haptic Assistance through Psychophysiological and Performance Feedback

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          Abstract

          In order to harmonize robotic devices with human beings, the robots should be able to perceive important psychosomatic impact triggered by emotional states such as frustration or boredom. This paper presents a new type of biocooperative control architecture, which acts toward improving the challenge/skill relation perceived by the user when interacting with a robotic multimodal interface in a cooperative scenario. In the first part of the paper, open-loop experiments revealed which physiological signals were optimal for inclusion in the feedback loop. These were heart rate, skin conductance level, and skin conductance response frequency. In the second part of the paper, the proposed controller, consisting of a biocooperative architecture with two degrees of freedom, simultaneously modulating game difficulty and haptic assistance through performance and psychophysiological feedback, is presented. With this setup, the perceived challenge can be modulated by means of the game difficulty and the perceived skill by means of the haptic assistance. A new metric ( FlowIndex) is proposed to numerically quantify and visualize the challenge/skill relation. The results are contrasted with comparable previously published work and show that the new method afforded a higher FlowIndex (i.e., a superior challenge/skill relation) and an improved balance between augmented performance and user satisfaction (higher level of valence, i.e., a more enjoyable and satisfactory experience).

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          Most cited references57

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          Autonomic nervous system activity in emotion: A review

          Autonomic nervous system (ANS) activity is viewed as a major component of the emotion response in many recent theories of emotion. Positions on the degree of specificity of ANS activation in emotion, however, greatly diverge, ranging from undifferentiated arousal, over acknowledgment of strong response idiosyncrasies, to highly specific predictions of autonomic response patterns for certain emotions. A review of 134 publications that report experimental investigations of emotional effects on peripheral physiological responding in healthy individuals suggests considerable ANS response specificity in emotion when considering subtypes of distinct emotions. The importance of sound terminology of investigated affective states as well as of choice of physiological measures in assessing ANS reactivity is discussed. Copyright © 2010 Elsevier B.V. All rights reserved.
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            Brain-computer interfaces in neurological rehabilitation.

            Recent advances in analysis of brain signals, training patients to control these signals, and improved computing capabilities have enabled people with severe motor disabilities to use their brain signals for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system. Non-invasive, electroencephalogram (EEG)-based brain-computer interface (BCI) technologies can be used to control a computer cursor or a limb orthosis, for word processing and accessing the internet, and for other functions such as environmental control or entertainment. By re-establishing some independence, BCI technologies can substantially improve the lives of people with devastating neurological disorders such as advanced amyotrophic lateral sclerosis. BCI technology might also restore more effective motor control to people after stroke or other traumatic brain disorders by helping to guide activity-dependent brain plasticity by use of EEG brain signals to indicate to the patient the current state of brain activity and to enable the user to subsequently lower abnormal activity. Alternatively, by use of brain signals to supplement impaired muscle control, BCIs might increase the efficacy of a rehabilitation protocol and thus improve muscle control for the patient.
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              Emotion: clues from the brain.

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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
                01 May 2017
                2017
                : 11
                : 242
                Affiliations
                [1] 1Robotics and Multibody Mechanics, Flanders Make, Vrije Universiteit Brussel Brussels, Belgium
                [2] 2Institute for Movement and Neurosciences, German Sport University Cologne Cologne, Germany
                [3] 3Human Physiology Research Group, Vrije Universiteit Brussel Brussels, Belgium
                [4] 4Biomedical Engineering, Fundacion CARTIF, Centro Tecnologico de Boecillo Valladolid, Spain
                [5] 5Department of Statistics and Operative Research, Universidad de Valladolid Valladolid, Spain
                Author notes

                Edited by: Dingguo Zhang, Shanghai Jiao Tong University, China

                Reviewed by: Domen Novak, University of Wyoming, USA; Nicolas Garcia-Aracil, Universidad Miguel Hernández de Elche, Spain

                *Correspondence: Carlos Rodriguez-Guerrero carodrig@ 123456vub.ac.be

                This article was submitted to Neural Technology, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2017.00242
                5410602
                28507503
                658143a4-45ab-4966-b391-bb5a75f9d2ef
                Copyright © 2017 Rodriguez-Guerrero, Knaepen, Fraile-Marinero, Perez-Turiel, Gonzalez-de-Garibay and Lefeber.

                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
                : 30 December 2016
                : 12 April 2017
                Page count
                Figures: 7, Tables: 5, Equations: 5, References: 64, Pages: 15, Words: 11140
                Funding
                Funded by: Ministerio de Ciencia y Tecnología 10.13039/501100006280
                Award ID: DPI2009-10658
                Funded by: Consejería de Educación, Junta de Castilla y León 10.13039/501100008431
                Award ID: VA09
                Categories
                Neuroscience
                Original Research

                Neurosciences
                psychophysiology,human–robot interaction,biocooperative,rehabilitation robotics,haptics,multimodal interfaces,biomechatronics

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