Comparison of new techniques based on EMD for control of a SSVEP-BCI

This paper presents the comparation of three different feature extraction techniques based on the Empirical Mode Decomposition (EMD) for a SSVEP-BCI. This approach based on the characterization of the signal by EMD, is proposed as a novel alternative to other techniques and it was demonstrated that it exceeds both in accuracy rate and Information Transfer Rate (ITR). The experiments were performed in an offline way, and seven volunteers participated of the study. The stimulis were generated both by LCD and LEDs. The frequencies used were 8, 11, 13 and 15 Hz. The results here reported such represent the average of the seven participants, achieving a success rate of 81% and ITR of 23.32 bits/min of the total set of cases analyzed. It is further confirmed that the highest success rates and ITRs were obtained for stimulation by LEDs.

[1]  A. Cichocki,et al.  Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives , 2010, Progress in Neurobiology.

[2]  Xingyu Wang,et al.  Author's Personal Copy Biomedical Signal Processing and Control Lasso Based Stimulus Frequency Recognition Model for Ssvep Bcis , 2022 .

[3]  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.

[4]  D. Regan Human brain electrophysiology: Evoked potentials and evoked magnetic fields in science and medicine , 1989 .

[5]  Reto Meuli,et al.  fMRI responses in medial frontal cortex that depend on the temporal frequency of visual input , 2007, Experimental Brain Research.

[6]  G. Sperling,et al.  Attentional modulation of SSVEP power depends on the network tagged by the flicker frequency. , 2006, Cerebral cortex.

[7]  J. Masdeu,et al.  Human Cerebral Activation during Steady-State Visual-Evoked Responses , 2003, The Journal of Neuroscience.

[8]  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.

[9]  Mahmoud Hassan,et al.  Combination of Canonical Correlation Analysis and Empirical Mode Decomposition Applied to Denoising the Labor Electrohysterogram , 2011, IEEE Transactions on Biomedical Engineering.

[10]  Hans Knutsson,et al.  A canonical correlation approach to blind source separation , 2001 .

[11]  N. Badruddin,et al.  Automatic eye-blink artifact removal method based on EMD-CCA , 2013, 2013 ICME International Conference on Complex Medical Engineering.

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

[13]  Xiaorong Gao,et al.  An independent brain-computer interface based on covert shifts of non-spatial visual attention , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  Ying Liu,et al.  Application and Contrast in Brain-Computer Interface between Hilbert-Huang Transform and Wavelet Transform , 2008, 2008 The 9th International Conference for Young Computer Scientists.

[15]  Dezhong Yao,et al.  Stimulator selection in SSVEP-based BCI. , 2008, Medical engineering & physics.

[16]  Toshihisa Tanaka,et al.  SSVEP frequency detection methods considering background EEG , 2012, The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems.

[17]  Qingguo Wei,et al.  A Comparative Study of Canonical Correlation Analysis and Power Spectral Density Analysis for SSVEP Detection , 2011, 2011 Third International Conference on Intelligent Human-Machine Systems and Cybernetics.

[18]  J. Wolpaw,et al.  Towards an independent brain–computer interface using steady state visual evoked potentials , 2008, Clinical Neurophysiology.

[19]  Yangsong Zhang,et al.  Multivariate synchronization index for frequency recognition of SSVEP-based brain–computer interface , 2014, Journal of Neuroscience Methods.

[20]  Feng Wan,et al.  Flashing color on the performance of SSVEP-based brain-computer interfaces , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  Ramesh Srinivasan,et al.  Steady-State Visual Evoked Potentials: Distributed Local Sources and Wave-Like Dynamics Are Sensitive to Flicker Frequency , 2006, Brain Topography.

[22]  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.