Statistical Characterization of Steady-State Visual Evoked Potentials and Their Use in Brain–Computer Interfaces

2009 ◽  
Vol 29 (3) ◽  
pp. 179-187 ◽  
Author(s):  
Miguel A. Lopez ◽  
Francisco Pelayo ◽  
Eduardo Madrid ◽  
Alberto Prieto
Keyword(s):  
Steady State ◽  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Brain Computer Interfaces ◽  
Statistical Characterization ◽  
Computer Interfaces ◽  
Visual Evoked

Use of Phase in Brain–Computer Interfaces based on Steady-State Visual Evoked Potentials

2010 ◽  
Vol 32 (1) ◽  
pp. 1-9 ◽  
Author(s):  
M. A. Lopez-Gordo ◽  
A. Prieto ◽  
F. Pelayo ◽  
C. Morillas
Keyword(s):  
Steady State ◽  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Visual Evoked

scholarly journals Comparison of Modern Highly Interactive Flicker-Free Steady State Motion Visual Evoked Potentials for Practical Brain–Computer Interfaces

2020 ◽  
Vol 10 (10) ◽  
pp. 686
Author(s):  
Piotr Stawicki ◽  
Ivan Volosyak
Keyword(s):  
Steady State ◽  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Information Transfer ◽  
Minimum Energy ◽  
Brain Computer Interfaces ◽  
Steady State Motion ◽  
Computer Interfaces ◽  
Information Transfer Rate ◽  
Visual Evoked

Motion-based visual evoked potentials (mVEP) is a new emerging trend in the field of steady-state visual evoked potentials (SSVEP)-based brain–computer interfaces (BCI). In this paper, we introduce different movement-based stimulus patterns (steady-state motion visual evoked potentials—SSMVEP), without employing the typical flickering. The tested movement patterns for the visual stimuli included a pendulum-like movement, a flipping illusion, a checkerboard pulsation, checkerboard inverse arc pulsations, and reverse arc rotations, all with a spelling task consisting of 18 trials. In an online experiment with nine participants, the movement-based BCI systems were evaluated with an online four-target BCI-speller, in which each letter may be selected in three steps (three trials). For classification, the minimum energy combination and a filter bank approach were used. The following frequencies were utilized: 7.06 Hz, 7.50 Hz, 8.00 Hz, and 8.57 Hz, reaching an average accuracy between 97.22% and 100% and an average information transfer rate (ITR) between 15.42 bits/min and 33.92 bits/min. All participants successfully used the SSMVEP-based speller with all types of stimulation pattern. The most successful SSMVEP stimulus was the SSMVEP1 (pendulum-like movement), with the average results reaching 100% accuracy and 33.92 bits/min for the ITR.

scholarly journals Towards an Optimization of Stimulus Parameters for Brain-Computer Interfaces Based on Steady State Visual Evoked Potentials

PLoS ONE ◽  
2014 ◽  
Vol 9 (11) ◽  
pp. e112099 ◽  
Author(s):  
Anna Duszyk ◽  
Maria Bierzyńska ◽  
Zofia Radzikowska ◽  
Piotr Milanowski ◽  
Rafał Kuś ◽  
...  
Keyword(s):  
Steady State ◽  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Stimulus Parameters ◽  
Visual Evoked

scholarly journals Population-Based Algorithms Applied to Brain-Computer Interfaces upon Steady-State Visual Evoked Potentials

2018 ◽  
Vol 7 (2) ◽  
Author(s):  
Marco Antonio Aceves-Fernandez . ◽  
Santiago M. Fernandez-Fraga . ◽  
José Emilio Vargas Soto . ◽  
Juan Manuel Ramos Arreguín .
Keyword(s):  
Steady State ◽  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Population Based ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Visual Evoked
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