A hybrid BCI system based on motor imagery and transient visual evoked potential

2019 ◽  
Vol 79 (15-16) ◽  
pp. 10327-10340
Author(s):  
Zhengquan Feng ◽  
Qinghua He ◽  
Jingna Zhang ◽  
Li Wang ◽  
Xinjian Zhu ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Transient Visual Evoked Potential ◽  
Bci System ◽  
Visual Evoked

The hybrid BCI system for movement control by combining motor imagery and moving onset visual evoked potential

2017 ◽  
Vol 14 (2) ◽  
pp. 026015 ◽  
Author(s):  
Teng Ma ◽  
Hui Li ◽  
Lili Deng ◽  
Hao Yang ◽  
Xulin Lv ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Movement Control ◽  
Bci System ◽  
Visual Evoked

Short-duration transient visual evoked potential for objective measurement of refractive errors

2011 ◽  
Vol 123 (3) ◽  
pp. 141-147 ◽  
Author(s):  
Aashish Anand ◽  
Carlos Gustavo V. De Moraes ◽  
Christopher C. Teng ◽  
Jeffrey M. Liebmann ◽  
Robert Ritch ◽  
...  
Keyword(s):  
Short Duration ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Objective Measurement ◽  
Refractive Errors ◽  
Transient Visual Evoked Potential ◽  
Visual Evoked

scholarly journals Frequency Detection of Single Channel Steady State Visual Evoked Potential using Canonical Correlation Analysis

2019 ◽  
Vol 8 (3S2) ◽  
pp. 136-139
Keyword(s):  
Steady State ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Single Channel ◽  
Stimulus Frequency ◽  
Bci System ◽  
Visual Evoked

Wave generated into visual cortex of brain, when subject focused his/her attention on visual stimulus flickers at certain frequency. The main challenge with SSVEP Based Brain computer interface (BCI) System is to detect the stimulus frequency from recorded brain signal. Canonical Correlation analysis (CCA) is one of the most popular methods to recognize the frequency of Steady state visual evoked potential (SSVEP). This paper focuses on the study of CCA algorithm to recognize the SSVEP signal frequency. For experiment purpose, a single channel data with flickering frequency in the range of (6Hz-10Hz) is used. The performance of the BCI System is measured in terms of detection accuracy and Information transmission rate (ITR). The maximum accuracy is obtained as 83.90% and ITR is 15.35 at stimulus frequency of 8.2Hz

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