Effect of Stimulus Size and Shape on Steady-State Visually Evoked Potentials for Brain-Computer Interface Optimization

2013 ◽  
Keyword(s):  
Steady State ◽  
Evoked Potentials ◽  
Brain Computer Interface ◽  
Stimulus Size ◽  
Computer Interface ◽  
Visually Evoked Potentials ◽  
Size And Shape

scholarly journals Analysis of User Interaction with a Brain-Computer Interface Based on Steady-State Visually Evoked Potentials: Case Study of a Game

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Harlei Miguel de Arruda Leite ◽  
Sarah Negreiros de Carvalho ◽  
Thiago Bulhões da Silva Costa ◽  
Romis Attux ◽  
Heiko Horst Hornung ◽  
...  
Keyword(s):  
Steady State ◽  
Evoked Potentials ◽  
Visual Stimulation ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Innovative Technology ◽  
Visually Evoked Potentials ◽  
Visual Fatigue ◽  
Systematic Analysis ◽  
Visual Contrast

This paper presents a systematic analysis of a game controlled by a Brain-Computer Interface (BCI) based on Steady-State Visually Evoked Potentials (SSVEP). The objective is to understand BCI systems from the Human-Computer Interface (HCI) point of view, by observing how the users interact with the game and evaluating how the interface elements influence the system performance. The interactions of 30 volunteers with our computer game, named “Get Coins,” through a BCI based on SSVEP, have generated a database of brain signals and the corresponding responses to a questionnaire about various perceptual parameters, such as visual stimulation, acoustic feedback, background music, visual contrast, and visual fatigue. Each one of the volunteers played one match using the keyboard and four matches using the BCI, for comparison. In all matches using the BCI, the volunteers achieved the goals of the game. Eight of them achieved a perfect score in at least one of the four matches, showing the feasibility of the direct communication between the brain and the computer. Despite this successful experiment, adaptations and improvements should be implemented to make this innovative technology accessible to the end user.

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.

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