Brain-Computer Interfaces - A Survey on Interactive Virtual Environments

Kerous, Bojan; Liarokapis, Fotis

doi:10.1109/vs-games.2016.7590339

Cited by 14 publications

(9 citation statements)

References 40 publications

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“…A BBMI system is composed of four main parts: a sensing device, an amplifier, a filter, and a control system [83]. For the brain interface, the system decodes and processes signals from a complex brain mechanisms to facilitate a digital interface between the brain and the computer [84]. The brain signals reflect the voluntary or involuntary neural actions generated by human's current activity.…”

Section: Brain and Body Machine Interfacesmentioning

confidence: 99%

Deep Learning in the Biomedical Applications: Recent and Future Status

2019

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Deep neural networks represent, nowadays, the most effective machine learning technology in biomedical domain. In this domain, the different areas of interest concern the Omics (study of the genome—genomics—and proteins—transcriptomics, proteomics, and metabolomics), bioimaging (study of biological cell and tissue), medical imaging (study of the human organs by creating visual representations), BBMI (study of the brain and body machine interface) and public and medical health management (PmHM). This paper reviews the major deep learning concepts pertinent to such biomedical applications. Concise overviews are provided for the Omics and the BBMI. We end our analysis with a critical discussion, interpretation and relevant open challenges.

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Section: Brain and Body Machine Interfacesmentioning

confidence: 99%

Deep Learning in the Biomedical Applications: Recent and Future Status

2019

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“…Aside from medical applications an intriguing sphere for EEG-based BCI implementation is gaming. Game industry is able not only to gain from BCIs adaptive gameplay and new ways of interacting with 3D virtual environments (Kerous and Liarokapis, 2016), but can also offer an opportunity to better understand and improve BCIs, to stimulate basic neurophysiological research , dedicated to investigation of the brain responses (Järvelä et al, 2015) and involved neural processes and behavioral research (Washburn, 2003). For example, BCI interconnected with Virtual reality seems to be a promising approach to study specific brain responses, mental states and manipulating attention.…”

Section: Gamesmentioning

confidence: 99%

“…In the area of gaming recent research (Borhani et al, 2018;McMahon and Schukat, 2018;Cohen et al, 2016;Kerous and Liarokapis, 2016;Yan et al, 2016;Vourvopoulos and i Badia, 2016;Ferreira et al, 2014) as well as some past studies considered the possibilities of interaction of BCIs with virtual environments (Groenegress et al, 2010;Lécuyer et al, 2008;Lotte and Guan, 2010;Pfurtscheller et al, 2011).Most of these studies were again focused on applications for users with physical disabilities, whereas BCI-based interaction with virtual reality could also be an interesting experience for the user from the general public.…”

Section: Bci and Vrmentioning

confidence: 99%

Playing a P300-based BCI VR game leads to changes in cognitive functions of healthy adults

Bulat

Karpman²,

Samokhina³

et al. 2020

Preprint

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In this paper, we present the results of a study to determine the effect of the P300-based brain-computer interface (BCI) virtual reality game on the cognitive functions of healthy human subjects. This study is a part of on-going research related to evaluation of the long-term effect of P300 training in Virtual Reality surrounding (VR game) on the cognitive performance of the young healthy population.A comparison of results between 3 groups of participants (15 people each) revealed the progressing difference in cognitive assessment for experimental group played P300-BCI VR game, showing the positive increase in flanker and conjunction visual search task performance associated with selective attention and mental inhibition. We show that the effect is due to the use of P300 BCI paradigm. Our results suggest that P300 BCI games combined with virtual reality can not only be used for rehabilitation in patients with slight mental disorders or elderly, but for increasing some cognitive functions in healthy subjects, giving an additional improvement in learning in case of combination with possible educational tasks or used for attention training. K E Y W O R D S

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“…The paper compares the proposed mechanical design and its performance with other haptic devices. Two survey papers were also studied that presents the Brain-Computer Interface (BCI) applications and perspectives [9], [10]. According to the survey BCI has shown major development in virtual reality games, entertainment as well as cloud computing.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A survey paper provides a description of Electroencephalogram (EEG) based BCI models and machine learning techniques used for the analysis of BCI readings [9]. Another survey paper analysis of publications and applications of brain-computer interfaces in virtual environment using electroencephalography (EEG) technique, is done [10]. Direct Neural Interfacing processed using BC1200 is used to control VR and IoT devices [29].…”

Section: Figure 3 Wearable Vr Haptic Devicementioning

confidence: 99%

Comparative Analysis of Interaction Techniques in Virtual Reality

Khan

Azam

Ahmed

et al. 2020

2020 IEEE 23rd International Multitopic Conference (INMIC)

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Virtual Reality is an emerging breed in technology. It is gaining an increasing amount of interest and considerable work is being done on it recently. A virtual environment without any user interaction can quickly lose users' interest. To make virtual environment more captivating, user interaction with virtual world is important. The paper reviews recent technologies and methods of human-computer interaction in the virtual environment. First a thorough examination of recent work done on different ways of interacting with virtual environment was carried out. Based on this review a variety of virtual reality interaction techniques were identified and then categorized into four major categories namely, haptic devices, gesture recognition, brain-computer interface, and gaze-based interaction. These techniques were evaluated against certain predefined parameter. An extensive analysis of the different techniques and their literature has led to a consolidated review that is presented in this paper.

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Brain-Computer Interfaces - A Survey on Interactive Virtual Environments

Cited by 14 publications

References 40 publications

Deep Learning in the Biomedical Applications: Recent and Future Status

Deep Learning in the Biomedical Applications: Recent and Future Status

Playing a P300-based BCI VR game leads to changes in cognitive functions of healthy adults

Comparative Analysis of Interaction Techniques in Virtual Reality

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