Advertisement

Signal, Image and Video Processing

, Volume 13, Issue 3, pp 567–573 | Cite as

EEG motor movement classification based on cross-correlation with effective channel

  • Mohand Lokman Al-dabagEmail author
  • Nalan Ozkurt
Original Paper

Abstract

In brain–computer interface (BCI) systems, the classification of electroencephalography (EEG) mental tasks is an important issue. This classification involves many steps: signal preprocessing, feature extraction and classification. In this study, a simple and robust method is proposed for preprocessing and feature extraction stages of the EEG classification. The method includes noise removal by EEG subtraction, channel selection, EEG band extraction using discrete wavelet transform, cross-correlation of EEG channels with effective channels and statistical parameter calculation. Two datasets are classified to illustrate the performance of the proposed method. One of them is the BCI competition III dataset IVa which is commonly used in research articles, and the second is recorded using Emotiv Epoc + headset. The results show that the average accuracy of the classification using an artificial neural network and support vector machine is above 96%.

Keywords

Cross-correlation Brain–computer interface (BCI) Electroencephalogram (EEG) Real/imaginary classification Neural networks Support vector machine (SVM) 

Notes

Acknowledgements

This work was supported within the scope of the scientific research project which was accepted by the Project Evaluation Committee of Yasar University under the title of “BAP020: Adaptive modelling of hand movements for brain computer interfaces.”

References

  1. 1.
    Santillán-Guzmán, A., Heute, U., Stephani, U., Galka, A.: Comparison of different methods to suppress muscle artifacts in EEG signals. Signal Image Video Process 11, 761–768 (2017)CrossRefGoogle Scholar
  2. 2.
    Gao, J., Lin, P., Yang, Y., Wang, P., Zheng, C.: Real-time removal of ocular artifacts from EEG based on independent component analysis and manifold learning. Neural Comput Appl 19, 1217–1226 (2010)CrossRefGoogle Scholar
  3. 3.
    Zarei, R., He, J., Siuly, S., Zhang, Y.: A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals. Comput Methods Prog Biomed 146, 47–57 (2017)CrossRefGoogle Scholar
  4. 4.
    Bhattacharyya, S., Sengupta, A., Chakraborti, T., Konar, A., Tibarewala, D.N.: Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata. Med Biol Eng Comput 52, 131–139 (2013)CrossRefGoogle Scholar
  5. 5.
    Siuly, S., Wang, H., Zhang, Y.: Detection of motor imagery EEG signals employing Naïve Bayes based learning process. Measurement 86, 148–158 (2016)CrossRefGoogle Scholar
  6. 6.
    Ince, N.F., Goksu, F., Tewfik, A.H., Arica, S.: Adapting subject specific motor imagery EEG patterns in space–time–frequency for a brain computer interface. Biomed Signal Process Control 4, 236–246 (2009)CrossRefGoogle Scholar
  7. 7.
    Miao, M., Zeng, H., Wang, A., Zhao, C., Liu, F.: Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: an sparse regression. J Neurosci Methods 278, 13–24 (2017)CrossRefGoogle Scholar
  8. 8.
    Mirvaziri, H., Mobarakeh, Z.S.: Improvement of EEG-based motor imagery classification using ringtopology-based particle swarm optimization. Biomed Signal Process Control 32, 69–75 (2016)CrossRefGoogle Scholar
  9. 9.
    McCrimmon, C.M., Fu, J.L., Wang, M., Lopes, L.S., Wang, P.T., Karimi-Bidhendi, A., Liu, C.Y., Heydari, P., Nenadic, Z.: Performance assessment of a custom, portable, and low-cost brain-computer interface platform. IEEE Trans Biomed Eng 64(10), 2313–2320 (2017)CrossRefGoogle Scholar
  10. 10.
    Liu, C., Fu, Y., Yang, J., Xiong, X., Sun, H., Yu, Z.: Discrimination of motor imagery pattern by electroencephalogram phase synchronization combined with frequency band energy. IEEE J Autom Sinica 4, 551–557 (2017)CrossRefGoogle Scholar
  11. 11.
    Krishna, D.H., Pasha, I.A., Savithri, T.S.: Autonomuos robot control based on EEG and cross-correlation. In: International conference on intelligent systems and control, Coimbatore, pp 1–4 (2016)Google Scholar
  12. 12.
    Ubeda, A., Ianez, E., Azorın, J.M., Sabater, J.M., Fernandez, E.: Classification method for BCIs based on the correlation of EEG maps. Neurocomputing 114, 98–106 (2013)CrossRefGoogle Scholar
  13. 13.
    Müller KR, Blankertz B (2004) BCI competition dataset IVa. Intelligent Data Analysis Group and University Medicine Berlin. http://www.bbci.de/competition/iii/desc_IVa.html. Accessed 24 Jan 2018
  14. 14.
    Li, M., Chen, W., Zhang, T.: Classification of epilepsy EEG signals using DWT-based envelope analysis and neural network ensemble. Biomed Signal Process Control 31, 357–365 (2017)CrossRefGoogle Scholar
  15. 15.
    Vapnik, V.N.: The nature of statistical learning theory, 2nd edn. Springer, New York (2000)CrossRefzbMATHGoogle Scholar
  16. 16.
    Haselsteiner, E., Pfurtscheller, G.: Using time-dependent neural networks for EEG classification. IEEE Trans Rehabil Eng 8, 457–463 (2000)CrossRefGoogle Scholar
  17. 17.
    Tibdewal, M.N., Fate, R.R., Mahadevappa, M., Ray, A.K.: Classification of artifactual EEG signal and detection of multiple eye movement artifact zones using novel Time-amplitude algorithm. Signal Image Video Process 11, 333–340 (2017)CrossRefGoogle Scholar
  18. 18.
    Hagan, M.T., Menhaj, M.B.: Training feed-forward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5, 989–993 (1994)CrossRefGoogle Scholar
  19. 19.
    Han, J., Kamber, M.: Data mining concepts and techniques. Elsevier, San Francisco (2006)zbMATHGoogle Scholar
  20. 20.
    Siuly, S., Li, Y.: Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain–computer interface. IEEE Trans Neural Syst Rehabil Eng 20, 526–538 (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer EngineeringYasar UniversityIzmirTurkey
  2. 2.Department of Electrical and Electronics EngineeringYasar UniversityIzmirTurkey

Personalised recommendations