FPGA Implementation of Visual Noise Optimized Online Steady-State Motion Visual Evoked Potential BCI System*

2020 ◽  
Author(s):  
Yanjun Zhang ◽  
Jun Xie ◽  
Guanghua Xu ◽  
Peng Fang ◽  
Guiling Cui ◽  
...  
Keyword(s):  
Steady State ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Fpga Implementation ◽  
Visual Noise ◽  
Steady State Motion ◽  
Bci System ◽  
Visual Evoked

Four Novel Motion Paradigms Based on Steady-State Motion Visual Evoked Potential

2018 ◽  
Vol 65 (8) ◽  
pp. 1696-1704 ◽  
Author(s):  
Wenqiang Yan ◽  
Guanghua Xu ◽  
Jun Xie ◽  
Min Li ◽  
Ziyan Dan
Keyword(s):  
Steady State ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Steady State Motion ◽  
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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