Classifying Various EMG and EOG Artifacts in EEG Signals

• Autorzy: Aydemir O. , Pourzare S. , Kayikcioglu T.

Warianty tytułu

PL

Klasyfikacja zakłóceń sygnałów w technice EEG w badaniach EMG i EOG

• Języki publikacji: EN

Abstrakty

EN

EEG is the most popular potential non-invasive interface, mainly due to its fine temporal resolution, ease of use, portability and low set-up cost. However, it has some limitations. The main limitation is that EEG is frequently contaminated by various artifacts. In this paper, a novel approach to classify various electromyography and electrooculography artifacts in EEG signals is presented. EEG signals were acquired at the Department of Electrical and Electronics Engineering Karadeniz Technical University from three healthy human subjects in age groups between 28 and 30 years old and on two different days. Extracted feature vectors based on root mean square, polynomial fitting and Hjorth descriptors were classified by k-nearest neighbor algorithm. The proposed method was successfully applied to the data sets and achieved an average classification rate of 94% on the test data.

PL

W artykule przedstawiono nową metodę analizy sygnałów w technice EEG pod względem klasyfikacji błędów zakłóceniowych w wynikach badań elektromiografii i elektrookulografii. Badanie przeprowadzone zostało na podstawie rzeczywistych wyników EEG.

Słowa kluczowe

EN

artifact; classification; EEG; feature extraction

PL

EEG; artefakt; klasyfikacja; wybór cech obrazu

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 11a
• Strony: 218--222
• Opis fizyczny: Bibliogr. 16 poz., tab., rys.

Twórcy

autor

Aydemir O.

autor

Pourzare S.

autor

Kayikcioglu T.

• Karadeniz Technical University,

Bibliografia

• [1] Teplan M., Fundamentals of EEG measurement, measurement science review, 2 (2002), nr 2, 1-11
• [2] Marcin K., Andrzej M., Remigiusz R.J., A new method of feature extraction from EEG signal for brain-computer interface design, Przeglad Elektrotechniczny, 86 (2010), nr 9, 35-38
• [3] Savelainen A., An introduction to EEG artifacts, independent research projects in applied mathematics, (2010),1-22
• [4] Boudet S., Peyrodie L., Gallois P. and Vasseur C., A global approach for automatic artifact removal for standard EEG record, Proceedings of the 28th IEEE EMBS Annual International Conference, New York City, USA (2006), 5719-5722
• [5] Babu P.A., Prasad Dr. K.V.S.V.R., Removal of ocular artifacts from EEG signals by Fast RLS algorithm using Wavelet Transform, International Journal of Computer Applications, 21 (2011), nr 4, 1-5
• [6] Park S., Lee H., Choi S., ICA plus OPCA for artifact-robust classification of EEG data, IEEE XI11 workshop on neural networks for signal processing, Toulouse, France (2003), 585-594
• [7] Gao J., Zheng C., Wang P., Automatic removal of ocular artifacts from EEG signals, 2nd International Conference on Biomedical Engineering and Informatics, Tianjin (2009), 1-5
• [8] Barea R., Boquete L., Mazo M., López E., System for assisted mobility using eye movements based on electrooculography, IEEE Transactions On Neural Systems and Rehabilitation Engineering, 10 (2002), nr 4, 209-218
• [9] Chadwick N.A., McMeekin D.A., Tan T., Classifying Eye and Head Movement Artifacts in EEG Signals, 5th IEEE International Conference on Digital Ecosystems and Technologies, Daejeon, Korea (2011), 285-291
• [10] Dat T.H., Shue L., Guan C., Electrocorticographic signal classification based on time-frequency decomposition and nonparametric statistical modeling, Proceedings of the 28th IEEE EMBS Annual International Conference ,New York City, USA (2006), 2292-2295
• [11] Subasi A., Ercelebi E., Classification of EEG signals using neural network and logistic regression, Computer Methods and Programs in Biomedicine, 78 (2005), 87-99
• [12] Smith J.O., Audio FFT filter banks, Proc. of the 12th Int. Conf. on Digital Audio Effects, Italy, September (2009), 1-8
• [13] Rezazadeh I.M., Wang X.Y., Firoozabadi M., Golpayegani M.R.H., Using affective human–machine interface to increase the operation performance in virtual construction crane training system: A novel approach, Automation in Construction, 20 (2011), nr 3, 289–298
• [14] Barreto A.B., Scargle S.D., Adjouadi M., A practical EMGbased human-computer interface for users with motor disabilities, Journal of Rehabilitation Research and Development, 37 (2000), nr 1, 53-64
• [15] Kayikcioglu T., Aydemir, O., A polynomial fitting and k-NN based approach for improving classification of motor imagery BCI data, Pattern Recognition Letters, 31 (2010), nr.11, 1207- 1215
• [16] Palaniappan R., Biological Signal Analysis, Ramaswamy Palaniappan & Ventus Publishing, ISBN 978-87-7681-594-3, 2010.