Finding optimal frequency and spatial filters accompanying blind signal separation of\\ EEG data for SSVEP-based BCI

Marcin Jukiewicz, Mikołaj Buchwald, Anna Cysewska-Sobusiak


Brain-computer interface (BCI) is a device which allows paralyzed people to navigate a robot, prosthesis or wheelchair using only their own brains’ reactions. By creating a direct communication pathway between the human brain and a machine, without muscles contractions or activity from within the peripheral nervous system, BCI makes mapping person’s intentions onto directive signals possible. One of the most commonly utilized phenomena in BCI is steady-state visually evoked potentials (SSVEP). If subject focuses attention on the flashing stimulus (with specified frequency) presented on the computer screen, a signal of the same frequency will appear in his or hers visual cortex and from there it can be measured. When there is more than one stimulus on the screen (each flashing with a different frequency) then based on the outcomes of the signal analysis we can predict at which of these objects (e.g., rectangles) subject was/is looking at that particular moment. Proper preprocessing steps have taken place in order to obtain maximally accurate stimuli recognition (as the specific frequency). In the current article, we compared various preprocessing and processing methods for BCI purposes. Combinations of spatial and temporal filtration methods and the proceeding blind source separation (BSS) were evaluated in terms of the resulting decoding accuracy. Canonical-correlation analysis (CCA) to signals classification was used.

Full Text:



P. Comon, “Independent component analysis, A new concept?” Signal Processing, vol. 36, no. 3, pp. 287–314, 1994.

T. P. Jung, S. Makeig, C. Humphries, T. W. Lee, M. J. McKeown, I. Iragui, and T. J. Sejnowski, “Removing Electroencephalographic aretfacts by blind source seperation,” Psychophysiology, vol. 37, no. 2, pp. 163–178, 2000.

F. Piccione, F. Giorgi, P. Tonin, K. Priftis, S. Giove, S. Silvoni, G. Palmas, and F. Beverina, “P300-based brain computer interface Reliability and performance in healthy and paralysed participants,” Clinical Neurophysiology, vol. 117, no. 3, pp. 531–537, 2006.

Y. Wang, Z. Zhang, X. Gao, and S. Gao, “Lead selection for SSVEPbased brain-computer interface.” in Proceedings of the 26th Annual International Conference of the IEEE EMBS, vol. 6, no. April 2016, San Francisco, CA, USA, 2004, pp. 4507–4510.

H. Bakardjian, T. Tanaka, and A. Cichocki, “Optimization of ssvep brain responses with application to eight-command brain–computer interface,” Neuroscience letters, vol. 469, no. 1, pp. 34–38, 2010.

M. Jukiewicz, M. Buchwald, and A. Cysewska-Sobusiak, “Usuwanie artefaktow z sygnalow sterujacych interfejsem mozg-komputer,” Poznan University of Technology Academic Journals. Electrical Engineering, no. 89, pp. 195–204, 2017.

A. Hyvarinen and E. Oja, “Independent component analysis: algorithms and applications,” Neural networks, vol. 13, no. 4-5, pp. 411–430, 2000.

J.-F. Cardoso, “Infomax and maximum likelihood for blind source separation,” IEEE Signal processing letters, vol. 4, no. 4, pp. 112–114, 1997.

T.-W. Lee, M. Girolami, and T. J. Sejnowski, “Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources,” Neural computation, vol. 11, no. 2, pp. 417–441, 1999.

P. Martinez, H. Bakardjian, and A. Cichocki, “Fully online multicommand brain-computer interface with visual neurofeedback using ssvep paradigm,” Computational intelligence and neuroscience, vol. 2007, 2007.

M. Kolodziej, A. Majkowski, and R. J. Rak, “Automatic detection of ssvep using independent component analysis,” in Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 2016. IEEE, 2016, pp. 196–201.

A. Kachenoura, L. Albera, L. Senhadji, and P. Comon, “ICA: A potential tool for BCI systems,” IEEE Signal Processing Magazine, vol. 25, no. 1, pp. 57–68, 2008.

D. J. McFarland, L. M. McCane, S. V. David, and J. R. Wolpaw, “Spatial filter selection for EEG-based communication,” Electroencephalography and Clinical Neurophysiology, vol. 103, no. 3, pp. 386–394, 1997.

J. R. Wolpaw, H. Ramoser, D. J. McFarland, and G. Pfurtscheller, “Eegbased communication: improved accuracy by response verification,” IEEE transactions on Rehabilitation Engineering, vol. 6, no. 3, pp. 326–333, 1998.

H. Jasper, “Report of the committee on methods of clinical examination in electroencephalography,” Electroencephalogr Clin Neurophysiol, vol. 10, pp. 370–375, 1958.

A. Gramfort, M. Luessi, E. Larson, D. A. Engemann, D. Strohmeier, C. Brodbeck, R. Goj, M. Jas, T. Brooks, L. Parkkonen, and M. Hamalainen, “MEG and EEG data analysis with MNE-Python,” Frontiers in Neuroscience, vol. 7, pp. 1–13, 2013.

A. Gramfort, M. Luessi, E. Larson, D. A. Engemann, D. Strohmeier, C. Brodbeck, L. Parkkonen, and M. S. Hamalainen, “MNE software for processing MEG and EEG data,” NeuroImage, vol. 86, pp. 446–460, 2014. [Online]. Available:

Z. Lin, C. Zhang, W. Wu, and X. Gao, “Frequency recognition based on canonical correlation analysis for ssvep-based bcis,” Biomedical Engineering, IEEE Transactions on, vol. 53, no. 12, pp. 2610–2614, 2006.

G. Bin, X. Gao, Z. Yan, B. Hong, and S. Gao, “An online multi-channel ssvep-based brain–computer interface using a canonical correlation analysis method,” Journal of neural engineering, vol. 6, no. 4, p. 046002, 2009.

Y. Zhang, G. Zhou, J. Jin, X. Wang, and A. Cichocki, “Frequency recognition in ssvep-based bci using multiset canonical correlation analysis,” International journal of neural systems, vol. 24, no. 04, p. 1450013, 2014.

Z. Lin, C. Zhang, W. Wu, and X. Gao, “Frequency recognition based on canonical correlation analysis for ssvep-based bcis,” IEEE transactions on biomedical engineering, vol. 54, no. 6, pp. 1172–1176, 2007.

S.-i. Amari, A. Cichocki, and H. H. Yang, “A new learning algorithm for blind signal separation,” in Advances in neural information processing systems, 1996, pp. 757–763.

A. Cichocki and S.-i. Amari, Adaptive blind signal and image processing: learning algorithms and applications. John Wiley & Sons, 2002, vol. 1.

S. Choi, A. Cichocki, H.-M. Park, and S.-Y. Lee, “Blind source separation and independent component analysis: A review,” Neural Information Processing-Letters and Reviews, vol. 6, no. 1, pp. 1–57, 2005.


  • There are currently no refbacks.

International Journal of Electronics and Telecommunications
is a periodical of Electronics and Telecommunications Committee
of Polish Academy of Sciences

eISSN: 2300-1933