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      BCI to Potentially Enhance Streaming Images to a VR Headset by Predicting Head Rotation

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          Abstract

          While numerous studies show that brain signals contain information about an individual’s current state that are potentially valuable for smoothing man–machine interfaces, this has not yet lead to the use of brain computer interfaces (BCI) in daily life. One of the main challenges is the common requirement of personal data that is correctly labeled concerning the state of interest in order to train a model, where this trained model is not guaranteed to generalize across time and context. Another challenge is the requirement to wear electrodes on the head. We here propose a BCI that can tackle these issues and may be a promising case for BCI research and application in everyday life. The BCI uses EEG signals to predict head rotation in order to improve images presented in a virtual reality (VR) headset. When presenting a 360° video to a headset, field-of-view approaches only stream the content that is in the current field of view and leave out the rest. When the user rotates the head, other content parts need to be made available soon enough to go unnoticed by the user, which is problematic given the available bandwidth. By predicting head rotation, the content parts adjacent to the currently viewed part could be retrieved in time for display when the rotation actually takes place. We here studied whether head rotations can be predicted on the basis of EEG sensor data and if so, whether application of such predictions could be applied to improve display of streaming images. Eleven participants generated left- and rightward head rotations while head movements were recorded using the headsets motion sensing system and EEG. We trained neural network models to distinguish EEG epochs preceding rightward, leftward, and no rotation. Applying these models to streaming EEG data that was withheld from the training showed that 400 ms before rotation onset, the probability “no rotation” started to decrease and the probabilities of an upcoming right- or leftward rotation started to diverge in the correct direction. In the proposed BCI scenario, users already wear a device on their head allowing for integrated EEG sensors. Moreover, it is possible to acquire accurately labeled training data on the fly, and continuously monitor and improve the model’s performance. The BCI can be harnessed if it will improve imagery and therewith enhance immersive experience.

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          Most cited references31

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          Hirnpotential�nderungen bei Willk�rbewegungen und passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale

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            CONTINGENT NEGATIVE VARIATION: AN ELECTRIC SIGN OF SENSORIMOTOR ASSOCIATION AND EXPECTANCY IN THE HUMAN BRAIN.

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              Modern mind-brain reading: psychophysiology, physiology, and cognition.

              This paper reviews the actual and potential benefits of a marriage between cognitive psychology and psychophysiology. Psychophysiological measures, particularly those of the event-related brain potential, can be used as markers for psychological events and physiological events. Thus, they can serve as "windows" on the mind and as "windows" on the brain. These ideas are illustrated in the context of a series of studies utilizing the lateralized readiness potential, a measure of electrical brain activity that is related to preparation for movement. This measure has been used to illuminate presetting processes that prepare the motor system for action, to demonstrate the presence of the transmission of partial information in the cognitive system, and to identify processes responsible for the inhibition of responses. The lateralized readiness potential appears to reflect activity in motor areas of cortex. Thus, this measure, along with other psychophysiological measures, can be used to understand how the functions of the mind are implemented in the brain.
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                Author and article information

                Contributors
                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                16 October 2018
                2018
                : 12
                : 420
                Affiliations
                [1] 1Department of Perceptual and Cognitive Systems, Netherlands Organization for Applied Scientific Research (TNO) , Soesterberg, Netherlands
                [2] 2Department of Media Networking, Netherlands Organization for Applied Scientific Research (TNO) , Den Haag, Netherlands
                Author notes

                Edited by: Fabien Lotte, Institut National de Recherche en Informatique et en Automatique (INRIA), France

                Reviewed by: Jeremy Frey, University of Bordeaux, France; Reinhold Scherer, Graz University of Technology, Austria

                *Correspondence: Anne-Marie Brouwer, anne-marie.brouwer@ 123456tno.nl
                Article
                10.3389/fnhum.2018.00420
                6232781
                30459580
                6d2cf3d7-9cce-4436-9187-e3416f217be1
                Copyright © 2018 Brouwer, van der Waa and Stokking.

                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
                : 07 February 2018
                : 27 September 2018
                Page count
                Figures: 8, Tables: 0, Equations: 0, References: 40, Pages: 12, Words: 0
                Categories
                Neuroscience
                Original Research

                Neurosciences
                eeg,brain computer interface,neuroadaptive technology,virtual reality,head mounted display,head rotation,movement prediction,applied neuroscience

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