Special Section: Robots, Virtual Reality, and Brain–Computer Interfaces in Telepresence Guest Editors’ Introduction

2014 ◽  
Vol 23 (3) ◽  
pp. iv-vi
Author(s):  
Doron Friedman ◽  
Christoph Guger
Keyword(s):  
Virtual Reality ◽  
Special Section ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

scholarly journals Editorial: Brain-Computer Interfaces and Augmented/Virtual Reality

2020 ◽  
Vol 14 ◽  
Author(s):  
Felix Putze ◽  
Athanasios Vourvopoulos ◽  
Anatole Lécuyer ◽  
Dean Krusienski ◽  
Sergi Bermúdez i Badia ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

scholarly journals Brain-Computer Interfaces, Virtual Reality, and Videogames

Computer ◽  
2008 ◽  
Vol 41 (10) ◽  
pp. 66-72 ◽  
Author(s):  
A. Lecuyer ◽  
F. Lotte ◽  
R.B. Reilly ◽  
R. Leeb ◽  
M. Hirose ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

scholarly journals A Virtual Reality Muscle–Computer Interface for Neurorehabilitation in Chronic Stroke: A Pilot Study

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3754 ◽  
Author(s):  
Octavio Marin-Pardo ◽  
Christopher M. Laine ◽  
Miranda Rennie ◽  
Kaori L. Ito ◽  
James Finley ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Pilot Study ◽  
Neural Plasticity ◽  
Muscle Activity ◽  
Chronic Stroke ◽  
Stroke Recovery ◽  
Brain Computer Interfaces ◽  
Chronic Stage ◽  
Computer Interfaces ◽  
Standard Clinical Practice

Severe impairment of limb movement after stroke can be challenging to address in the chronic stage of stroke (e.g., greater than 6 months post stroke). Recent evidence suggests that physical therapy can still promote meaningful recovery after this stage, but the required high amount of therapy is difficult to deliver within the scope of standard clinical practice. Digital gaming technologies are now being combined with brain–computer interfaces to motivate engaging and frequent exercise and promote neural recovery. However, the complexity and expense of acquiring brain signals has held back widespread utilization of these rehabilitation systems. Furthermore, for people that have residual muscle activity, electromyography (EMG) might be a simpler and equally effective alternative. In this pilot study, we evaluate the feasibility and efficacy of an EMG-based variant of our REINVENT virtual reality (VR) neurofeedback rehabilitation system to increase volitional muscle activity while reducing unintended co-contractions. We recruited four participants in the chronic stage of stroke recovery, all with severely restricted active wrist movement. They completed seven 1-hour training sessions during which our head-mounted VR system reinforced activation of the wrist extensor muscles without flexor activation. Before and after training, participants underwent a battery of clinical and neuromuscular assessments. We found that training improved scores on standardized clinical assessments, equivalent to those previously reported for brain–computer interfaces. Additionally, training may have induced changes in corticospinal communication, as indexed by an increase in 12–30 Hz corticomuscular coherence and by an improved ability to maintain a constant level of wrist muscle activity. Our data support the feasibility of using muscle–computer interfaces in severe chronic stroke, as well as their potential to promote functional recovery and trigger neural plasticity.

A Study of Frontal Signals in Brain Computer Interfaces: Interpretation of EEG & FNIRS

2020 ◽  
pp. 136-142
Author(s):  
S. Srilekha ◽  
B. Vanathi
Keyword(s):  
Infrared Spectroscopy ◽  
Healthy Subject ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Economic Conditions ◽  
Brain Computer Interfaces ◽  
Functional Near Infrared Spectroscopy ◽  
Computer Interfaces ◽  
Rehabilitation Devices

This paper focuses on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) comparison to help the rehabilitation patients. Both methods have unique techniques and placement of electrodes. Usage of signals are different in application based on the economic conditions. This study helps in choosing the signal for the betterment of analysis. Ten healthy subject datasets of EEG & FNIRS are taken and applied to plot topography separately. Accuracy, Sensitivity, peaks, integral areas, etc are compared and plotted. The main advantages of this study are to prompt their necessities in the analysis of rehabilitation devices to manage their life as a typical individual.

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