Title :
The combination of CCA and PSDA detection methods in a SSVEP-BCI system
Author :
Ruimin Wang ; Wen Wu ; Iramina, Keiji ; Sheng Ge
Author_Institution :
Sch. of Electron. Eng. & Optoelectron. Technol., Nanjing Univ. of Sci. & Technol., Nanjing, China
Abstract :
In recent years, based on the steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) have generated significant interest, due to their shorter calibration times and higher information transfer rates. Target identification is the core signal processing task in BCIs. Power spectral density analysis (PSDA) and canonical correlation analysis (CCA) are the most popular and widely used classification methods in SSVEP-BCI systems. In this paper, we first combined these two methods for detecting the SSVEP signals. Moreover, we compared the proposed method with PSDA, CCA method, respectively. The results showed that the proposed method can improve the accuracy and the transfer rate of BCIs.
Keywords :
brain-computer interfaces; medical signal processing; CCA combination; PSDA detection methods; SSVEP-BCI system; canonical correlation analysis; core signal processing; power spectral density analysis; steady state visual evoked potential brain computer interfaces; target identification; Accuracy; Brain-computer interfaces; Correlation; Educational institutions; Electroencephalography; Electrooculography; Visualization; Brain-computer interface (BCI); canonical correlation analysis (CCA); power spectral density analysis (PSDA); steady-state visual evoked potential (SSVEP);
Conference_Titel :
Intelligent Control and Automation (WCICA), 2014 11th World Congress on
DOI :
10.1109/WCICA.2014.7053101
