The use of optimal spatial filtering by the method of common spatial pattern for the classification of EEG signals according to the type of brain activity


  • P. H. Kytsun LLC "Samsung Electronics Ukraine Company"



EEG, classification, CSP, BCI


During EEG due to the volume conduction the signal from each individual source appears simultaneously in a number of channels registered from different leads. Therefore, the recorded EEG signal gives a blurred picture of human brain activity, which makes the task of classifying such signals rather complicated. One of the effective methods to obtain an informative signal from the EEG record is to use the optimal spatial filtering (some kind of deblurring), when the maximum content of the signal from the particular region of the brain (which corresponds to a certain type of brain activity) is achived in the output signal. An algorithm for classifying EEG signals using optimal spatial filtering by the method of common spatial pattern is proposed to identify two classes of brain activity -- imaginary left and right hand movements. To evaluate the quality of the algorithm, an EEG record, known as BCI Competition IV dataset 2b, was used. To determine the efficacy of the algorithm, the result of its operation was compared with the result of the algorithm without using optimal spatial filtering. The use of optimal spatial filtering improved the accuracy of classification from 0.74 to 0.79, which has shown its efficacy.

Author Biography

P. H. Kytsun, LLC "Samsung Electronics Ukraine Company"

Kytsun P. H., Postgraduate student


Nicolas-Alonso L. F. and Gomez-Gil J. (2012) Brain computer interfaces, a review. Sensors (Basel, Switzerland), vol. 12, no. 2, pp. 1211-79. DOI:10.3390/s120201211.

Boninger M. L., Wechsler L. R., and Stein J. (2014) Robotics, stem cells, and brain-computer interfaces in rehabilitation and recovery from stroke: updates and advances. American journal of physical medicine & rehabilitation, vol. 93, no. 11, Suppl 3, pp. S145-54. DOI:10.1097/PHM.0000000000000128.

Blankertz B., Tangermann M., Vidaurre C., Fazli S., Sannelli C., Haufe S., Maeder C., Ramsey L., Sturm I., Curio G., and Muller K.-R. (2010) The Berlin Brain-Computer Interface: Non-Medical Uses of BCI Technology. Frontiers in neuroscience, vol. 4, p. 198. DOI:10.3389/fnins.2010.00198.

Yong X. and Menon C. (2015) EEG classification of different imaginary movements within the same limb. PloS one, vol. 10, no. 4, p. e0121896. DOI:10.1371/journal.pone.0121896.

Yuan H. and He B. (2014) Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives. IEEE transactions on bio-medical engineering, vol. 61, no. 5, pp. 1425-35. DOI:10.1109/TBME.2014.2312397.

Blankertz B., Tomioka R., Lemm S., Kawanabe M., and Muller K.-r. (2008) Optimizing Spatial filters for Robust EEG Single-Trial Analysis. IEEE Signal Processing Magazine, vol. 25, no. 1, pp. 41-56. DOI:10.1109/MSP.2008.4408441.

Tangermann M., Muller K.-R., Aertsen A., Birbaumer N., Braun C., Brunner C., Leeb R., Mehring C., Miller K. J., Muller-Putz G. R., Nolte G., Pfurtscheller G., Preissl H., Schalk G., Schlogl A., Vidaurre C., Waldert S., and Blankertz B. (2012) Review of the BCI Competition IV. Frontiers in neuroscience, vol. 6, p. 55. DOI:10.3389/fnins.2012.00055.



How to Cite

Кицун, П. Г. (2017) “The use of optimal spatial filtering by the method of common spatial pattern for the classification of EEG signals according to the type of brain activity”, Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, 0(71), pp. 36-39. doi: 10.20535/RADAP.2017.71.36-39.



Radioelectronics Medical Technologies