A method for recognizing high-frequency steady-state visual evoked potential based on empirical modal decomposition and canonical correlation analysis

In most of current studies on SSVEP based BCIs, low-frequency and medium-frequency visual stimuli are used, and subjects are prone to fatigue. The BCI based on high-frequency SSVEP can improve the comfort level of subjects in experiment and reduce the possibility of inducing diseases such as epilepsy. This paper proposed a method combining empirical modal decomposition (EMD) and canonical correlation analysis (CCA) to improve the classification accuracy of high-frequency SSVEP. The experiment results show that the EMD-CCA based method is more suitable for high-frequency SSVEP based BCI, which can achieve a maximum accuracy of 93.68% and an information transmission rate of $15.0236bit/min^{-1}$.

[1]  Feng Wan,et al.  A comparison of minimum energy combination and canonical correlation analysis for SSVEP detection , 2011, 2011 5th International IEEE/EMBS Conference on Neural Engineering.

[2]  Vicente Mut,et al.  Robotic Wheelchair Commanded by People with Disabilities Using Low/High-Frequency SSVEP-based BCI , 2015 .

[3]  Po-Lei Lee,et al.  Frequency recognition in an SSVEP-based brain computer interface using empirical mode decomposition and refined generalized zero-crossing , 2011, Journal of Neuroscience Methods.

[4]  Xiaorong Gao,et al.  An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method , 2009, Journal of neural engineering.

[5]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[6]  Nasour Bagheri,et al.  Multiple classifier system for EEG signal classification with application to brain–computer interfaces , 2012, Neural Computing and Applications.

[7]  Seyed Navid Resalat,et al.  High-speed SSVEP-based BCI: Study of various frequency pairs and inter-sources distances , 2012, Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics.

[8]  Piotr Milanowski,et al.  A high frequency steady-state visually evoked potential based brain computer interface using consumer-grade EEG headset , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[10]  Gao Xiaorong,et al.  Brain-computer interface based on the high-frequency steady-state visual evoked potential , 2005, Proceedings. 2005 First International Conference on Neural Interface and Control, 2005..

[11]  Tzyy-Ping Jung,et al.  High-speed spelling with a noninvasive brain–computer interface , 2015, Proceedings of the National Academy of Sciences.

[12]  Wei Wu,et al.  Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs , 2006, IEEE Transactions on Biomedical Engineering.

[13]  Teodiano Freire Bastos Filho,et al.  Comparison of new techniques based on EMD for control of a SSVEP-BCI , 2014, 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE).

[14]  Keiji Iramina,et al.  The combination of CCA and PSDA detection methods in a SSVEP-BCI system , 2014, Proceeding of the 11th World Congress on Intelligent Control and Automation.

[15]  Po-Lei Lee,et al.  Adaptive SSVEP-Based BCI System With Frequency and Pulse Duty-Cycle Stimuli Tuning Design , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[16]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[17]  Xingyu Wang,et al.  Frequency Recognition in SSVEP-Based BCI using Multiset Canonical Correlation Analysis , 2013, Int. J. Neural Syst..

[18]  Pablo F. Diez,et al.  Asynchronous BCI control using high-frequency SSVEP , 2011, Journal of NeuroEngineering and Rehabilitation.