scholarly journals An Augmented-Reality fNIRS-Based Brain-Computer Interface: A Proof-of-Concept Study

2020 ◽  
Vol 14 ◽  
Author(s):  
Amaia Benitez-Andonegui ◽  
Rodion Burden ◽  
Richard Benning ◽  
Rico Möckel ◽  
Michael Lührs ◽  
...  
2018 ◽  
Vol 55 ◽  
pp. 52-59 ◽  
Author(s):  
B.H. van Duren ◽  
K. Sugand ◽  
R. Wescott ◽  
R. Carrington ◽  
A. Hart

2019 ◽  
Vol 130 (5) ◽  
pp. 1173-1179
Author(s):  
Piotr Pietruski ◽  
Marcin Majak ◽  
Ewelina Świątek‐Najwer ◽  
Magdalena Żuk ◽  
Michał Popek ◽  
...  

2020 ◽  
Vol 69 (4) ◽  
pp. 1530-1539 ◽  
Author(s):  
Leopoldo Angrisani ◽  
Pasquale Arpaia ◽  
Antonio Esposito ◽  
Nicola Moccaldi

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5765
Author(s):  
Soram Kim ◽  
Seungyun Lee ◽  
Hyunsuk Kang ◽  
Sion Kim ◽  
Minkyu Ahn

Since the emergence of head-mounted displays (HMDs), researchers have attempted to introduce virtual and augmented reality (VR, AR) in brain–computer interface (BCI) studies. However, there is a lack of studies that incorporate both AR and VR to compare the performance in the two environments. Therefore, it is necessary to develop a BCI application that can be used in both VR and AR to allow BCI performance to be compared in the two environments. In this study, we developed an opensource-based drone control application using P300-based BCI, which can be used in both VR and AR. Twenty healthy subjects participated in the experiment with this application. They were asked to control the drone in two environments and filled out questionnaires before and after the experiment. We found no significant (p > 0.05) difference in online performance (classification accuracy and amplitude/latency of P300 component) and user experience (satisfaction about time length, program, environment, interest, difficulty, immersion, and feeling of self-control) between VR and AR. This indicates that the P300 BCI paradigm is relatively reliable and may work well in various situations.


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