scholarly journals Embodiment is related to better performance on an immersive brain computer interface in head-mounted virtual reality: A pilot study

2019 ◽  
Author(s):  
Julia M Juliano ◽  
Ryan P Spicer ◽  
Athanasios Vourvopoulos ◽  
Stephanie Lefebvre ◽  
Kay Jann ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Brain Activity ◽  
Sensory Information ◽  
Individual Performance ◽  
Motor Rehabilitation ◽  
Computer Screen ◽  
Computer Interfaces ◽  
Sense Of Presence ◽  
Immersive Systems ◽  
Sensorimotor Rhythms

AbstractBrain computer interfaces (BCI) can be used to provide individuals with neurofeedback of their own brain activity and train them to learn how to control their brain activity. Neurofeedback-based BCIs used for motor rehabilitation aim to ‘close the loop’ between attempted motor commands and sensory feedback by providing supplemental sensory information when individuals successfully establish specific brain patterns. Existing neurofeedback-based BCIs have used a variety of displays to provide feedback, ranging from devices that provide a more immersive and compelling experience (e.g., head-mounted virtual reality (HMD-VR) or CAVE systems) to devices that are considered less immersive (e.g., computer screens). However, it is not clear whether more immersive systems (i.e., HMD-VR) improve neurofeedback performance compared to computer screens, and whether there are individual performance differences in HMD-VR versus screen-based neurofeedback. In this pilot experiment, we compared neurofeedback performance in HMD-VR versus on a computer screen in twelve healthy individuals. We also examined whether individual differences in presence or embodiment correlated with neurofeedback performance in either environment. Participants were asked to control a virtual right arm by imagining right hand movements. Real-time brain activity indicating motor imagery, which was measured via electroencephalography (EEG) as desynchronized sensorimotor rhythms (SMR; 8-24 Hz) in the left motor cortex, drove the movement of the virtual arm towards (increased SMR desynchronization) or away from (decreased SMR desynchronization) targets. Participants performed two blocks of 30 trials, one for each condition (Screen, HMD-VR), with the order of conditions counterbalanced across participants. After completing each block, participants were asked questions relating to their sense of presence and embodiment in each environment. We found that, while participants’ performance on the neurofeedback-based BCI task was similar between conditions, the participants’ reported levels of embodiment was significantly different between conditions. Specifically, participants experienced higher levels of embodiment in HMD-VR compared to the computer screen. We further found that reported levels of embodiment positively correlated with neurofeedback performance only in the HMD-VR condition. Overall, these preliminary results suggest that embodiment may improve performance on a neurofeedback-based BCI and that HMD-VR may increase embodiment during a neurofeedback-based BCI task compared to a standard computer screen.

scholarly journals Embodiment Is Related to Better Performance on a Brain–Computer Interface in Immersive Virtual Reality: A Pilot Study

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1204 ◽  
Author(s):  
Julia M. Juliano ◽  
Ryan P. Spicer ◽  
Athanasios Vourvopoulos ◽  
Stephanie Lefebvre ◽  
Kay Jann ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Pilot Study ◽  
Sensory Feedback ◽  
Individual Performance ◽  
Computer Interface ◽  
Motor Rehabilitation ◽  
Computer Screen ◽  
Head Mounted Display ◽  
Computer Interfaces ◽  
Two Measures

Electroencephalography (EEG)-based brain–computer interfaces (BCIs) for motor rehabilitation aim to “close the loop” between attempted motor commands and sensory feedback by providing supplemental information when individuals successfully achieve specific brain patterns. Existing EEG-based BCIs use various displays to provide feedback, ranging from displays considered more immersive (e.g., head-mounted display virtual reality (HMD-VR)) to displays considered less immersive (e.g., computer screens). However, it is not clear whether more immersive displays improve neurofeedback performance and whether there are individual performance differences in HMD-VR versus screen-based neurofeedback. In this pilot study, we compared neurofeedback performance in HMD-VR versus a computer screen in 12 healthy individuals and examined whether individual differences on two measures (i.e., presence, embodiment) were related to neurofeedback performance in either environment. We found that, while participants’ performance on the BCI was similar between display conditions, the participants’ reported levels of embodiment were significantly different. Specifically, participants experienced higher levels of embodiment in HMD-VR compared to a computer screen. We further found that reported levels of embodiment positively correlated with neurofeedback performance only in HMD-VR. Overall, these preliminary results suggest that embodiment may relate to better performance on EEG-based BCIs and that HMD-VR may increase embodiment compared to computer screens.

scholarly journals Brain Symmetry Analysis during the Use of a BCI Based on Motor Imagery for the Control of a Lower-Limb Exoskeleton

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1746
Author(s):  
Laura Ferrero ◽  
Mario Ortiz ◽  
Vicente Quiles ◽  
Eduardo Iáñez ◽  
José A. Flores ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Lower Limb ◽  
Brain Activity ◽  
Control Methods ◽  
Dominant Hemisphere ◽  
Cortical Function ◽  
Lower Limb Exoskeleton ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Sensorimotor Rhythms

Brain–Computer Interfaces (BCI) are systems that allow external devices to be controlled by means of brain activity. There are different such technologies, and electroencephalography (EEG) is an example. One of the most common EEG control methods is based on detecting changes in sensorimotor rhythms (SMRs) during motor imagery (MI). The aim of this study was to assess the laterality of cortical function when performing MI of the lower limb. Brain signals from five subjects were analyzed in two conditions, during exoskeleton-assisted gait and while static. Three different EEG electrode configurations were evaluated: covering both hemispheres, covering the non-dominant hemisphere and covering the dominant hemisphere. In addition, the evolution of performance and laterality with practice was assessed. Although sightly superior results were achieved with information from all electrodes, differences between electrode configurations were not statistically significant. Regarding the evolution during the experimental sessions, the performance of the BCI generally evolved positively the higher the experience was.

Combining VR and Biofeedback

2020 ◽  
Vol 32 (4) ◽  
pp. 176-186 ◽  
Author(s):  
Christoph Rockstroh ◽  
Johannes Blum ◽  
Anja S. Göritz
Keyword(s):  
Virtual Reality ◽  
Electrodermal Activity ◽  
Display Type ◽  
Future Research ◽  
Subjective Stress ◽  
Stress Induction ◽  
Computer Screen ◽  
Sense Of Presence ◽  
Physical Presence ◽  
Healthy Participants

Abstract. This study explored the effects of combining virtual reality (VR) and biofeedback on the restorativeness of the created experience as judged by the user and the user’s sense of presence. In a between-subjects experiment, we disentangled the effects of display type (VR vs. computer screen) and biofeedback (electrodermal activity biofeedback vs. no biofeedback) in the context of immersive simulated relaxation environments. After a stress induction, 94 healthy participants were randomly assigned to one of four relaxing treatments. There were no treatment-specific differences in subjective stress or physiological arousal. However, VR compared with computer screen increased the sense of presence and, partly, perceived restorativeness. When combined with VR, biofeedback increased physical presence and, in part, perceived restorativeness. The study offers insight that allows for the identification of future research avenues.

Machine-Learning-Based Coadaptive Calibration for Brain-Computer Interfaces

2011 ◽  
Vol 23 (3) ◽  
pp. 791-816 ◽  
Author(s):  
Carmen Vidaurre ◽  
Claudia Sannelli ◽  
Klaus-Robert Müller ◽  
Benjamin Blankertz
Keyword(s):  
Machine Learning ◽  
Brain Activity ◽  
Computer Application ◽  
Brain Computer Interfaces ◽  
Significant Control ◽  
Guided Learning ◽  
Computer Interfaces ◽  
Calibration Measurement ◽  
Online Feedback ◽  
Sensorimotor Rhythms

Brain-computer interfaces (BCIs) allow users to control a computer application by brain activity as acquired (e.g., by EEG). In our classic machine learning approach to BCIs, the participants undertake a calibration measurement without feedback to acquire data to train the BCI system. After the training, the user can control a BCI and improve the operation through some type of feedback. However, not all BCI users are able to perform sufficiently well during feedback operation. In fact, a nonnegligible portion of participants (estimated 15%–30%) cannot control the system (a BCI illiteracy problem, generic to all motor-imagery-based BCIs). We hypothesize that one main difficulty for a BCI user is the transition from offline calibration to online feedback. In this work, we investigate adaptive machine learning methods to eliminate offline calibration and analyze the performance of 11 volunteers in a BCI based on the modulation of sensorimotor rhythms. We present an adaptation scheme that individually guides the user. It starts with a subject-independent classifier that evolves to a subject-optimized state-of-the-art classifier within one session while the user interacts continuously. These initial runs use supervised techniques for robust coadaptive learning of user and machine. Subsequent runs use unsupervised adaptation to track the features’ drift during the session and provide an unbiased measure of BCI performance. Using this approach, without any offline calibration, six users, including one novice, obtained good performance after 3 to 6 minutes of adaptation. More important, this novel guided learning also allows participants with BCI illiteracy to gain significant control with the BCI in less than 60 minutes. In addition, one volunteer without sensorimotor idle rhythm peak at the beginning of the BCI experiment developed it during the course of the session and used voluntary modulation of its amplitude to control the feedback application.

The Science Behind Virtual Reality Displays

2019 ◽  
Vol 5 (1) ◽  
pp. 529-547 ◽  
Author(s):  
Peter Scarfe ◽  
Andrew Glennerster
Keyword(s):  
Virtual Reality ◽  
Virtual Environment ◽  
Sensory Processing ◽  
Real Life ◽  
Sensory Information ◽  
Perceptual Processing ◽  
Sense Of Presence ◽  
History Of

Virtual reality (VR) is becoming an increasingly important way to investigate sensory processing. The converse is also true: in order to build good VR technologies, one needs an intimate understanding of how our brain processes sensory information. One of the key advantages of studying perception with VR is that it allows an experimenter to probe perceptual processing in a more naturalistic way than has been possible previously. In VR, one is able to actively explore and interact with the environment, just as one would do in real life. In this article, we review the history of VR displays, including the philosophical origins of VR, before discussing some key challenges involved in generating good VR and how a sense of presence in a virtual environment can be measured. We discuss the importance of multisensory VR and evaluate the experimental tension that exists between artifice and realism when investigating sensory processing.

scholarly journals Examining Tourism Consumers’ Attitudes and the Role of Sensory Information in Virtual Reality Experiences of a Tourist Destination

2021 ◽  
pp. 004728752110377
Author(s):  
Mansour Alyahya ◽  
Graeme McLean
Keyword(s):  
Virtual Reality ◽  
Mental Imagery ◽  
Experimental Approach ◽  
Sensory Information ◽  
Tourist Destination ◽  
Positive Role ◽  
Sense Of Presence ◽  
Positive Effect ◽  
Different Levels

The purpose of this research is twofold: firstly, we aim to understand the role of virtual reality (VR) in influencing tourism consumers’ attitudes toward a tourist destination and, secondly, understand the influence of different levels of sensory information presented through VR experiences on the development of mental imagery, attitudes toward the destination, and visit intention. We tackle this through a multistudy experimental approach. First, in study 1, we demonstrate that VR plays a positive role in enhancing previously held consumer attitudes toward a tourist destination. Second, we affirm that VR has a greater positive effect on attitudes toward a destination in comparison to a less immersive technology (i.e., website). Third, in study 2, we find that different levels of sensory information in VR experiences result in significant differences with regard to the developed mental imagery, sense of presence in the experience, attitudes toward the destination and visit intentions.

scholarly journals Using Brain-Computer Interface to Control a Virtual Drone Using Non-Invasive Motor Imagery and Machine Learning

2021 ◽  
Vol 11 (24) ◽  
pp. 11876
Author(s):  
Catalin Dumitrescu ◽  
Ilona-Madalina Costea ◽  
Augustin Semenescu
Keyword(s):  
Motor Imagery ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Rapid Change ◽  
Training Session ◽  
Extraction Methods ◽  
Computer Interface ◽  
Eeg Signals ◽  
Computer Interfaces ◽  
Sensorimotor Rhythms

In recent years, the control of devices “by the power of the mind” has become a very controversial topic but has also been very well researched in the field of state-of-the-art gadgets, such as smartphones, laptops, tablets and even smart TVs, and also in medicine, to be used by people with disabilities for whom these technologies may be the only way to communicate with the outside world. It is well known that BCI control is a skill and can be improved through practice and training. This paper aims to improve and diversify signal processing methods for the implementation of a brain-computer interface (BCI) based on neurological phenomena recorded during motor tasks using motor imagery (MI). The aim of the research is to extract, select and classify the characteristics of electroencephalogram (EEG) signals, which are based on sensorimotor rhythms, for the implementation of BCI systems. This article investigates systems based on brain-computer interfaces, especially those that use the electroencephalogram as a method of acquisition of MI tasks. The purpose of this article is to allow users to manipulate quadcopter virtual structures (external, robotic objects) simply through brain activity, correlated with certain mental tasks using undecimal transformation (UWT) to reduce noise, Independent Component Analysis (ICA) together with determination coefficient (r2) and, for classification, a hybrid neural network consisting of Radial Basis Functions (RBF) and a multilayer perceptron–recurrent network (MLP–RNN), obtaining a classification accuracy of 95.5%. Following the tests performed, it can be stated that the use of biopotentials in human–computer interfaces is a viable method for applications in the field of BCI. The results presented show that BCI training can produce a rapid change in behavioral performance and cognitive properties. If more than one training session is used, the results may be beneficial for increasing poor cognitive performance. To achieve this goal, three steps were taken: understanding the functioning of BCI systems and the neurological phenomena involved; acquiring EEG signals based on sensorimotor rhythms recorded during MI tasks; applying and optimizing extraction methods, selecting and classifying characteristics using neuronal networks.

Using BCI and EEG to process and analyze driver’s brain activity signals during VR simulation

2021 ◽  
Vol 60 (4) ◽  
pp. 137-153
Author(s):  
Mirosław Nader ◽  
Ilona Jacyna-Gołda ◽  
Stanisław Nader ◽  
Karol Nehring
Keyword(s):  
Virtual Reality ◽  
Brain Activity ◽  
Age Groups ◽  
Functional Model ◽  
Flow Simulation ◽  
Human Mind ◽  
Pilot Studies ◽  
Computer Interfaces ◽  
External Stimuli ◽  
Electroencephalogram Eeg

The use of popular brain–computer interfaces (BCI) to analyze signals and the behavior of brain activity is a very current problem that is often undertaken in various aspects by many researchers. This comparison turns out to be particularly useful when studying the flows of information and signals in the human-machine-environment system, especially in the field of transportation sciences. This article presents the results of a pilot study of driver behavior with the use of a proprietary simulator based on Virtual Reality technology. The study uses the technology of studying signals emitted by the human mind and its specific zones in response to given environmental factors. A solution based on virtual reality with the limitation of external stimuli emitted by the real world was proposed, and computational analysis of the obtained data was performed. The research focused on traffic situations and how they affect the subject. The test was attended by representatives of various age groups, both with and without a driving license. This study presents an original functional model of a research stand in VR technology that we designed and built. Testing in VR conditions allows to limit the influence of undesirable external stimuli that may distort the results of readings. At the same time, it increases the range of road events that can be simulated without generating any risk for the participant. In the presented studies, the BCI was used to assess the driver's behavior, which allows for the activity of selected brain waves of the examined person to be registered. Electroencephalogram (EEG) was used to study the activity of brain and its response to stimuli coming from the Virtual Reality created environment. Electrical activity detection is possible thanks to the use of electrodes placed on the skin in selected areas of the skull. The structure of the proprietary test-stand for signal and information flow simulation tests, which allows for the selection of measured signals and the method of parameter recording, is presented. An important part of this study is the presentation of the results of pilot studies obtained in the course of real research on the behavior of a car driver.

scholarly journals Sensorimotor functional connectivity: a neurophysiological factor related to BCI performance

2020 ◽  
Author(s):  
Carmen Vidaurre ◽  
Stefan Haufe ◽  
Tania Jorajuría ◽  
Klaus-Robert Müller ◽  
Vadim V. Nikulin
Keyword(s):  
Functional Connectivity ◽  
Signal To Noise Ratio ◽  
Brain Activity ◽  
Training Session ◽  
Physiological Factors ◽  
Computer Interfaces ◽  
Control Devices ◽  
Feedback Frequency ◽  
Online Feedback ◽  
Sensorimotor Rhythms

AbstractBrain-Computer Interfaces (BCIs) are systems that allow users to control devices using brain activity alone. However, the ability of participants to command BCIs varies from subject to subject. For BCIs based on the modulation of sensorimotor rhythms as measured by means of electroen-cephalography (EEG), about 20% of potential users do not obtain enough accuracy to gain reliable control of the system. This lack of efficiency of BCI systems to decode user’s intentions requires the identification of neuro-physiological factors determining ‘good’ and ‘poor’ BCI performers. Given that the neuronal oscillations, used in BCI, demonstrate rich a repertoire of spatial interactions, we hypothesized that neuronal activity in sensorimotor areas would define some aspects of BCI performance. Analyses for this study were performed on a large dataset of 80 inexperienced participants. They took part in calibration and an online feedback session in the same day. Undirected functional connectivity was computed over sensorimotor areas by means of the imaginary part of coherency. The results show that post-as well as pre-stimulus connectivity in the calibration recordings is significantly correlated to online feedback performance in μ and feedback frequency bands. Importantly, the significance of the correlation between connectivity and BCI feedback accuracy was not due to the signal-to-noise ratio of the oscillations in the corresponding post and pre-stimulus intervals. Thus, this study shows that BCI performance is not only dependent on the amplitude of sensorimotor oscillations as shown previously, but that it also relates to sensorimotor connectivity measured during the preceding training session. The presence of such connectivity between motor and somatosensory systems is likely to facilitate motor imagery, which in turn is associated with the generation of a more pronounced modulation of sen-sorimotor oscillations (manifested in ERD/ERS) required for the adequate BCI performance. We also discuss strategies for the up-regulation of such connectivity in order to enhance BCI performance.

scholarly journals Using Virtual Reality as a Tool in the Rehabilitation of Movement Abnormalities in Schizophrenia

2021 ◽  
Vol 11 ◽  
Author(s):  
Anastasia Pavlidou ◽  
Sebastian Walther
Keyword(s):  
Virtual Reality ◽  
Virtual Environments ◽  
Real Life ◽  
Sensory Information ◽  
Behavioral Performance ◽  
Motor Rehabilitation ◽  
Pharmacological Agents ◽  
Motor Functioning ◽  
Head Mounted Display

Movement abnormalities are prevalent across all stages of schizophrenia contributing to poor social functioning and reduced quality of life. To date, treatments are scarce, often involving pharmacological agents, but none have been shown to improve movement abnormalities effectively. Virtual reality (VR) is a tool used to simulate virtual environments where behavioral performance can be quantified safely across different tasks while exerting control over stimulus delivery, feedback and measurement in real time. Sensory information is transmitted via a head mounted display allowing users to directly interact with virtual objects and bodies using gestures and body movements in the real world to perform different actions, permitting a sense of immersion in the simulated virtual environment. Although, VR has been widely used for successful motor rehabilitation in a variety of different neurological domains, none have been exploited for motor rehabilitation in schizophrenia. The objectives of this article are to review movement abnormalities specific to schizophrenia, and how VR can be utilized to restore and improve motor functioning in patients with schizophrenia. Constructing VR-mediated motor-cognitive interventions that can help in retaining and transferring the learned outcomes to real life are also discussed.

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