Title :
Development of SSVEP-based BCI using common frequency pattern to enhance system performance
Author :
Li-Wei Ko ; Shih-Chuan Lin ; Wei-Gang Liang ; Komarov, Oleksii ; Meng-Shue Song
Author_Institution :
Dept. of Biol. Sci. & Technol., Univ., Hsinchu, Taiwan
Abstract :
Brain Computer Interface(BCI) systems provide an additional way for people to interact with external environment without using peripheral nerves or muscles[1]. In a variety of BCI systems, a BCI system based on the steady-state visual evoked potentials (SSVEP) is one most common system known for application, because of its ease of use and good performance with little user training. In this study, the common frequency pattern method (CFP) is used to improve the accuracy of our EEG-based SSVEP BCI system. There are four basic classifiers (SVM, KNNC, PARZENDC, LDC) in this paper to estimate the accuracy of our SSVEP system. Without using CFP, the highest accuracy of the EEG-based SSVEP system was 80%. By using CFP, the accuracy could be upgraded to 95%.
Keywords :
brain-computer interfaces; electroencephalography; medical signal processing; signal classification; statistical analysis; support vector machines; KNNC classifier; LDC classifier; PARZENDC classifier; SSVEP-based BCI development; SVM classifier; brain computer interface systems; common frequency pattern method; discriminant analysis; electroencephalography; k-nearest-neighbor classifier; nonparametric density estimation; steady-state visual evoked potentials; support vector machine; system performance enhancement; Accuracy; Covariance matrices; Eigenvalues and eigenfunctions; Electroencephalography; Static VAr compensators; Support vector machines; Visualization; Brain computer interface(BCI); common frequency pattern(CFP); electroencephalography(EEG); steady-state visual evoked potential(SSVEP);
Conference_Titel :
Computational Intelligence in Brain Computer Interfaces (CIBCI), 2014 IEEE Symposium on
Conference_Location :
Orlando, FL
DOI :
10.1109/CIBCI.2014.7007789
