Spatial smoothing of canonical correlation analysis for steady state visual evoked potential based brain computer interfaces

2016 ◽  
Author(s):  
Shingo Ryu ◽  
Hiroshi Higashi ◽  
Toshihisa Tanaka ◽  
Shigeki Nakauchi ◽  
Tetsuto Minami
Keyword(s):  
Steady State ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Brain Computer Interfaces ◽  
Spatial Smoothing ◽  
Computer Interfaces ◽  
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

scholarly journals Improvement of the Classification Accuracy of Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces by Combining L1-MCCA with SVM

2021 ◽  
Vol 11 (23) ◽  
pp. 11453
Author(s):  
Yuhang Gao ◽  
Juanning Si ◽  
Sijin Wu ◽  
Weixian Li ◽  
Hao Liu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Steady State ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Classification Accuracy ◽  
Canonical Correlation ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Support Vector ◽  
Visual Evoked

Canonical correlation analysis (CCA) has been used for the steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) for a long time. However, the reference signal of CCA is relatively simple and lacks subject-specific information. Moreover, over-fitting may occur when a short time window (TW) length was used in CCA. In this article, an optimized L1-regularized multiway canonical correlation analysis (L1-MCCA) is combined with a support vector machine (SVM) to overcome the aforementioned shortcomings in CCA. The correlation coefficients obtained by L1-MCCA were transferred into a particle-swarm-optimization (PSO)-optimized support vector machine (SVM) classifier to improve the classification accuracy. The performance of the proposed method was evaluated and compared with the traditional CCA and power spectral density (PSD) methods. The results showed that the accuracy of the L1-MCCA-PSO-SVM was 96.36% and 98.18% respectively when the TW lengths were 2 s and 6 s. This accuracy is higher than that of the traditional CCA and PSD methods.

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