Adaptive canonical correlation analysis for harmonic stimulation frequencies recognition in SSVEP-based BCIs

Adaptive canonical correlation analysis for harmonic stimulation frequencies recognition in SSVEP-based BCIs

Steady-state visual evoked potential (SSVEP) is the brain’s response to quickly repetitive visual stimuluswith a certain frequency. To increase the information transfer rate (ITR) in SSVEP-based systems, due to the frequencyresolution restriction, we are forced to broaden the frequency range, which causes harmonic frequencies to come intothe stimulation frequency range. Conventional canonical correlation analysis (CCA) may be associated with error forSSVEP frequency recognition at stimulation frequencies with harmonic relations. The number of harmonics consideredto construct reference signals are determined adaptively; for frequencies whose second harmonic exists in the frequencyrange, two harmonics are used, and for other frequencies, just one harmonic is used. After constructing reference signalsand recognizing the frequency corresponding to the maximum value of correlation by CCA, the target frequency isdetermined after a postprocessing step. Results show that for the 8-s time window length, the average classificationaccuracy for the adaptive CCA was 84%, while the corresponding values for the CCA with one harmonic (N = 1) andtwo harmonics (N = 2) were 78% and 74%, respectively. For 4-s length, this accuracy for the adaptive CCA was 86%,while it was 78% for both harmonic selection modes of the standard CCA, N = 1 and N = 2. In SSVEP applicationswith harmonic stimulation frequencies, the adaptive CCA has significantly improved the frequency recognition accuracyin comparison with the popularly standard CCA method. The proposed method can be useful for SSVEP-based BCIsystems that use broad ranges of stimulation frequencies with harmonic relation.

___

  • [1] Singla R, Khosla A, Jha R. Influence of stimuli color on steady-state visual evoked potentials based BCI wheelchair control. Journal of Biomedical Science and Engineering 2013; 6 (11): 1050.
  • [2] Wang YT, Wang Y, Jung TP. A cell-phone-based brain–computer interface for communication in daily life. Journal of Neural Engineering 2011; 8 (2): 025018.
  • [3] Nawrocka A, Holewa K. Brain-computer interface based on steady-state visual evoked potentials (SSVEP). In: IEEE 2013 International Carpathian Control Conference; Rytro, Poland; 2013. pp. 251-254.
  • [4] Wang Y, Gao X, Hong B, Jia C, Gao S. Brain-computer interfaces based on visual evoked potentials. IEEE Engineering in Medicine and Biology Magazine 2008; 27 (5): 64-71.
  • [5] Volosyak I, Cecotti H, Valbuena D, Graser A. Evaluation of the Bremen SSVEP based BCI in real world conditions. In: IEEE 2009 International Conference on Rehabilitation Robotics; Kyoto, Japan; 2009. pp. 322-331.
  • [6] Wu Z, Lai Y, Xia Y, Wu D, Yao D. Stimulator selection in SSVEP-based BCI. Medical Engineering and Physics 2008; 30 (8): 1079-1088.
  • [7] Bin G, Gao X, Yan Z, Hon B, Gao S. An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method. Journal of Neural Engineering 2009; 6 (4): 046002.
  • [8] Hwang HJ, Lim JH, Jung YJ, Choi H, Lee SW et al. Development of an SSVEP-based BCI spelling system adopting a QWERTY-style LED keyboard. Journal of Neuroscience Methods 2012; 208 (1): 59-65.
  • [9] Cecotti H. A self-paced and calibration-less SSVEP-based brain–computer interface speller. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2010; 18 (2): 127-133.
  • [10] Cui J, Wong W, Mann S. Time-frequency analysis of visual evoked potentials by means of matching pursuit with chirplet atoms. In: IEEE 2004 International Conference on Engineering in Medicine and Biology Society; San Francisco, CA, USA; 2004. pp. 267-270.
  • [11] Hwang HJ, Kim DH, Han CH, Im CH. A new dual-frequency stimulation method to increase the number of visual stimuli for multi-class SSVEP-based brain–computer interface. Brain Research 2013; 1515: 66-77.
  • [12] Sadeghi S, Maleki A. The EMD-CCA with Neural Network classifier to recognize the SSVEP frequency. Iranian Journal of Biomedical Engineering 2017; 11 (2): 914-918 (article in Persian with an abstract in English).
  • [13] Wong CM, Wang B, Wan F, Mak PU, Mak PI et al. A solution to harmonic frequency problem: frequency and phase coding-based brain-computer interface. In: IEEE 2011 International Neural Networks Joint Conference; San Jose, CA, USA; 2011. pp. 2119-2126.
  • [14] Wong CM, Wang B, Wan F, Mak PU, Mak PI et al. An improved phase-tagged stimuli generation method in steady-state visual evoked potential based brain-computer interface. In: IEEE 2010 International Conference on Biomedical Engineering and Informatics; Yantai, China; 2010. pp. 745-749.
  • [15] Bin G, Gao X, Yan Z, Hong B, Gao S. An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method. Journal of Neural Engineering 2009; 6 (4): 046002.
  • [16] Zhang Y, Xu P, Liu T, Hu J, Zhang R et al. Multiple frequencies sequential coding for SSVEP-based brain-computer interface. PLoS One 2012; 7 (3): 29519.
  • [17] Zhang Y, Zhou G, Jin J, Wang X, Cichocki A. Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis. International Journal of Neural Systems 2014; 24 (4): 1450013.
  • [18] Zhang Y, Zhou G, Zhao Q, Onishi A, Jin J et al. Multiway canonical correlation analysis for frequency components recognition in SSVEP-based BCIs. In: IEEE 2011 International Conference on Neural Information Processing; Shanghai, China; 2011. pp. 287-295.
  • [19] Pan J, Gao X, Duan F, Yan Z, Gao S. Enhancing the classification accuracy of steady-state visual evoked potentialbased brain–computer interfaces using phase constrained canonical correlation analysis. Journal of Neural Engineering 2011; 8 (3): 036027.
  • [20] Zhang Z, Wang C, Ang KK, Wai AA, Nanyang CG. Spectrum and phase adaptive CCA for SSVEP-based brain computer interface. In: 2018 International Conference in Medicine and Biology Society; Honolulu, HI, USA; 2018. pp. 311-314.
  • [21] Kumar GK, Reddy MR. Exploiting the temporal structure of EEG data for SSVEP detection. In: 2018 International Conference in Brain-Computer Interface; Gangwon, South Korea; 2018. pp. 1-4.
  • [22] Nunez PL, Srinivasan R. Electric Fields of the Brain: The Neurophysics of EEG. 2nd ed. New York, NY, USA: Oxford University Press, 2006.
  • [23] Bédard C, Kröger H, Destexhe A. Modeling extracellular field potentials and the frequency-filtering properties of extracellular space. Biophysical Journal 2004; 86 (3): 1829-1842.
  • [24] Friman O, Volosyak I, Graser A. Multiple channel detection of steady-state visual evoked potentials for braincomputer interfaces. IEEE Transactions on Biomedical Engineering 2007; 54 (4): 742–750.
  • [25] Lin Z, Zhang C, Wu W, Gao X. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Transactions on Biomedical Engineering 2007; 54 (6): 1172-1176.
  • [26] Manyakov NV, Chumerin N, Hulle MMV. Multichannel decoding for phase-coded SSVEP brain computer interface. International Journal of Neural Systems 2012; 22 (5): 1250022.
  • [27] Wu C, Chang H, Lee P, Li K, Sie J et al. Frequency recognition in an SSVEP-based brain computer interface using empirical mode decomposition and refined generalized zero-crossing. Journal of Neuroscience Methods 2011; 196 (1): 170–181.
  • [28] Pan J, Gao X, Duan F, Yan Z, Gao S. Enhancing the classification accuracy of steady-state visual evoked potentialbased brain-computer interfaces using phase constrained canonical correlation analysis. Journal of Neural Engineering 2011; 8 (3): 036027.
  • [29] Pastor MA, Artieda J, Arbizu J, Valencia M, Masdeu JC. Human cerebral activation during steady-state visualevoked responses. Journal of Neuroscience 2003; 23 (37): 11621-11627.
  • [30] Castillo J, Muller S, Caicedo E, Bastos T. Feature extraction techniques based on power spectrum for a SSVEP-BCI. In: IEEE 2014 International Symposium on Industrial Electronics; İstanbul, Turkey; 2014. pp. 1051-1055.
  • [31] Tello RM, Muller SM, Bastos-Filho T, Ferreira A. A comparison of techniques and technologies for SSVEP classification. In: IEEE 2014 Conference on Biosignals and Robotics for Better and Safer Living; Bahia, Brazil; 2014. pp. 1-6.