@ARTICLE{Jukiewicz_Marcin_Finding_2018,
 author={Jukiewicz, Marcin and Buchwald, Mikołaj and Cysewska-Sobusiak, Anna},
 volume={vol. 64},
 number={No 4},
 journal={International Journal of Electronics and Telecommunications},
 pages={439–444},
 howpublished={online},
 year={2018},
 publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
 abstract={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 persons 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.},
 type={Artykuły / Articles},
 title={Finding Optimal Frequency and Spatial Filters Accompanying Blind Signal Separation of EEG Data for SSVEP-based BCI},
 URL={http://journals.pan.pl/Content/107758/PDF/59_1340.pdf},
 doi={10.24425/123543},
 keywords={BCI, SSVEP, BSS, FastICA, AMUSE, Infomax, Extended Infomax, CAR, Large Laplacian, Small Laplacian, CCAI},
}