scholarly journals Smart Glasses for Visually Evoked Potential Applications: Characterisation of the Optical Output for Different Display Technologies

2021 ◽  
Vol 10 (1) ◽  
pp. 33
Author(s):  
Alessandro Cultrera ◽  
Pasquale Arpaia ◽  
Luca Callegaro ◽  
Antonio Esposito ◽  
Massimo Ortolano
Keyword(s):  
Evoked Potential ◽  
Brain Computer Interface ◽  
Wearable Sensors ◽  
Computer Interface ◽  
Visually Evoked Potentials ◽  
Substantial Impact ◽  
Smart Glasses ◽  
Optical Output ◽  
Potential Applications ◽  
Application Requirements

Off-the-shelf consumer-grade smart glasses are being increasingly used in extended reality and brain–computer interface applications that are based on the detection of visually evoked potentials from the user’s brain. The displays of these kinds of devices can be based on different technologies, which may affect the nature of the visual stimulus received by the user. This aspect has substantial impact in the field of applications based on wearable sensors and devices. We measured the optical output of three models of smart glasses with different display technologies using a photo-transducer in order to gain insight on their exploitability in brain–computer interface applications. The results suggest that preferring a particular model of smart glasses may strongly depend on the specific application requirements.

Obstacles in using a computer screen for steady-state visually evoked potential stimulation

2018 ◽  
Vol 63 (4) ◽  
pp. 377-382
Author(s):  
Katharina Olze ◽  
Christof Jan Wehrmann ◽  
Luyang Mu ◽  
Meinhard Schilling
Keyword(s):  
Operating System ◽  
Steady State ◽  
Programming Language ◽  
Evoked Potential ◽  
Visual Stimulation ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Visually Evoked Potentials ◽  
Refresh Rate ◽  
Computer Screen

Abstract In brain computer interface (BCI) applications, the use of steady-state visually evoked potentials (SSVEPs) is common. Therefore, a visual stimulation with a constant repetition frequency is necessary. However, using a computer monitor, the set of frequencies that can be used is restricted by the refresh rate of the screen. Frequencies that are not an integer divisor of the refresh rate cannot be displayed correctly. Furthermore, the programming language the stimulation software is written in and the operating system influence the actually generated and presented frequencies. The aim of this paper is to identify the main challenges in generating SSVEP stimulation using a computer screen with and without using DirectX in Windows-based PC systems and to provide solutions for these issues.

A novel monitor for practical brain-computer interface applications based on visual evoked potential

2021 ◽  
pp. 1-13
Author(s):  
Hamidreza Maymandi ◽  
Jorge Luis Perez Benitez ◽  
F. Gallegos-Funes ◽  
J. A. Perez Benitez
Keyword(s):  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Visual Evoked

Feature selection for classification in Steady state visually evoked potentials (SSVEP)-based brain-computer interfaces with genetic algorithm

2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Stanisław Karkosz ◽  
Marcin Jukiewicz
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Steady State ◽  
Brain Computer Interface ◽  
Morphological Features ◽  
Computer Interface ◽  
Visually Evoked Potentials ◽  
Computer Interfaces ◽  
Subject Variability ◽  
Selection For

AbstractObjectivesOptimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy.MethodsSystem of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI).ResultsThe designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects.ConclusionsIt is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.

SSVEP-based Brain-computer Interface for Part-picking Robotic Co-worker

2021 ◽  
pp. 1-13
Author(s):  
Yao Li ◽  
T. Kesavadas
Keyword(s):  
Human Subject ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Industrial Robots ◽  
Computer Interface ◽  
Human Cognition ◽  
Grasp Planning ◽  
Planning Algorithm

Abstract One of the expectations for the next generation of industrial robots is to work collaboratively with humans as robotic co-workers. Robotic co-workers must be able to communicate with human collaborators intelligently and seamlessly. However, industrial robots in prevalence are not good at understanding human intentions and decisions. We demonstrate a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) which can directly deliver human cognition to robots through a headset. The BCI is applied to a part-picking robot and sends decisions to the robot while operators visually inspecting the quality of parts. The BCI is verified through a human subject study. In the study, a camera by the side of the conveyor takes photos of each part and presents it to the operator automatically. When the operator looks at the photo, the electroencephalography (EEG) is collected through BCI. The inspection decision is extracted through SSVEPs in EEG. When a defective part is identified by the operator, the signal is communicated to the robot which locates the defective part through a second camera and removes it from the conveyor. The robot can grasp various part with our grasp planning algorithm (2FRG). We have developed a CNN-CCA model for SSVEP extraction. The model is trained on a dataset collected in our offline experiment. Our approach outperforms the existing CCA, CCA-SVM, and PSD-SVM models. The CNN-CCA is further validated in an online experiment that achieves 93% accuracy in identifying and removing a defective part.

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