Control Based on Brain-Bomputer Interface Technology for Video-Gaming With Virtual Reality Techniques

Paszkiel, Szczepan

doi:10.14313/jamris_4-2016/26

Cited by 20 publications

(8 citation statements)

References 9 publications

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“…The brain-computer interface (BCI) provides an interlink between the brain and external devices (Vidal, 1973 ; Wolpaw et al, 2002 ). The information received from the brain in the form of physiological/magnetic/metabolic signals is decoded and interpreted to determine the user intentions, and is later utilized for various purposes, such as rehabilitation (Do et al, 2013 ; Khan R. A. et al, 2018 ); control of robots (Doud et al, 2011 ; Bozinovski, 2016 ; Khan A. H. et al, 2018 ; Rosca et al, 2018 ; Duan et al, 2019 ) and of prosthetics (Buch et al, 2018 ; Yanagisawa et al, 2019 ); and neurogaming (Paszkiel, 2016 , 2020 ; Vasiljevic and de Miranda, 2020 ). Among the existing non-invasive acquisition methods, arguably EEG (Wolpaw et al, 2002 ; Pfurtscheller et al, 2006 ; Choi, 2013 ; Abiri et al, 2019 ) and fNIRS (Ferrari et al, 1985 ; Delpy et al, 1988 ; Coyle et al, 2004 , 2007 ; Fazli et al, 2012 ; Naseer and Keum-Shik, 2015 ; Yin et al, 2015 ) are considered the most explored.…”

Section: Introductionmentioning

confidence: 99%

Hybrid EEG-fNIRS BCI Fusion Using Multi-Resolution Singular Value Decomposition (MSVD)

Khan

Hasan

2020

Front. Hum. Neurosci.

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Brain-computer interface (BCI) multi-modal fusion has the potential to generate multiple commands in a highly reliable manner by alleviating the drawbacks associated with single modality. In the present work, a hybrid EEG-fNIRS BCI system—achieved through a fusion of concurrently recorded electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals—is used to overcome the limitations of uni-modality and to achieve higher tasks classification. Although the hybrid approach enhances the performance of the system, the improvements are still modest due to the lack of availability of computational approaches to fuse the two modalities. To overcome this, a novel approach is proposed using Multi-resolution singular value decomposition (MSVD) to achieve system- and feature-based fusion. The two approaches based up different features set are compared using the KNN and Tree classifiers. The results obtained through multiple datasets show that the proposed approach can effectively fuse both modalities with improvement in the classification accuracy.

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Section: Introductionmentioning

confidence: 99%

Hybrid EEG-fNIRS BCI Fusion Using Multi-Resolution Singular Value Decomposition (MSVD)

Khan

Hasan

2020

Front. Hum. Neurosci.

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“…As it was stated in the proposed vision, the application should offer virtual reality environment, which includes the use of a VR headset. It relies on use of a motion capture technology and 3D engine which is a powerful environment when dealing with 3D rendering [12].…”

Section: A Supported Technologiesmentioning

confidence: 99%

A Contribution to Workplace Ergonomics Evaluation Using Multimedia Tools and Virtual Reality

Leskovsky

Kučera

Haffner

et al. 2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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The paper demonstrates an application developed to help to evaluate ergonomics of a workplace. Ergonomics of a workplace has enormous impact on employees and their longterm work effectiveness, which causes an interest in this field from employers' point of view. The paper describes and compares several attitudes companies use to set up and evaluate workplace metrics, potential of virtual reality (VR) in the process, VR application proposal, implementation within Unity 3D engine and results achieved with implementation of this proposed solution. Current approaches also include motion tracking for ergonomics evaluation. These technologies are often far over smaller companies' budget. Described solution is reasonably priced also for small companies, using cheaper motion capture equipment.

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“…With the recent drastic advancement of sensing hardware along with estimation algorithms, the techniques for virtual reality (VR) and augmented reality (AR) have been tremendously researched and employed for many real-world engineering applications such as telerobotics [1,2], medical training [3], and brain-computer interface technology [4]. Since humans perceive stiffness of an object mainly relying on haptic sensation (contact force, compliance of the object, deformation of the finger-tip, tactile sensation), usage of wearable haptic devices has been tremendously researched for finger-based interaction [5,6] in VR, surely including studies for stiffness rendering [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Wearable Haptic Device for Stiffness Rendering of Virtual Objects in Augmented Reality

Lee

2021

Applied Sciences

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We propose a novel wearable haptic device that can provide kinesthetic haptic feedback for stiffness rendering of virtual objects in augmented reality (AR). Rendering stiffness of objects using haptic feedback is crucial for realistic finger-based object manipulation, yet challenging particularly in AR due to the co-presence of a real hand, haptic device, and rendered AR objects in the scenes. By adopting passive actuation with a tendon-based transmission mechanism, the proposed haptic device can generate kinesthetic feedback strong enough for immersive manipulation and prevention of inter-penetration in a small-form-factor, while maximizing the wearability and minimizing the occlusion in AR usage. A selective locking module is adopted in the device to allow for the rendering of the elasticity of objects. We perform an experimental study of two-finger grasping to verify the efficacy of the proposed haptic device for finger-based manipulation in AR. We also quantitatively compare/articulate the effects of different types of feedbacks across haptic and visual sense (i.e., kinesthetic haptic feedback, vibrotactile haptic feedback, and visuo-haptic feedback) for stiffness rendering of virtual objects in AR for the first time.

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Control Based on Brain-Bomputer Interface Technology for Video-Gaming With Virtual Reality Techniques

Abstract: Abstract:The

Cited by 20 publications

References 9 publications

Hybrid EEG-fNIRS BCI Fusion Using Multi-Resolution Singular Value Decomposition (MSVD)

Hybrid EEG-fNIRS BCI Fusion Using Multi-Resolution Singular Value Decomposition (MSVD)

A Contribution to Workplace Ergonomics Evaluation Using Multimedia Tools and Virtual Reality

Wearable Haptic Device for Stiffness Rendering of Virtual Objects in Augmented Reality

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