A pairwise output coding method for multi-class EEG classification of a self-induced BCI

Nurhan Gursel Ozmen, Levent Gumusel

Abstract


In brain computer interface (BCI) research, electroencephalography (EEG) is the most widely used method due to its noninvasiveness, high temporal resolution and portability. Most of the EEG-based BCI studies are aimed at developing methodologies for signal processing, feature extraction and classification. In this study, an experimental EEG study was carried out with six subjects performing imagery mental and motor tasks. We present a  multi-class EEG decoding with a novel pairwise output coding method of EEGs to improve the performance of self-induced BCI systems. This method involves an augmented one-versus-one multiclass classification with less time and reduced number of electrodes. Furthermore, a train repetition number is introduced in the training step to optimize the data selection. The difference among right and left hemispheres is also searched. Finally, the difference between experienced and novice subjects is also observed.

The experimental results have demonstrated that, the use of proposed classification algorithm produces high classification accuracies (98%) with nine channels. Reduced numbers of channels (four channels) have 100% accuracies for mental tasks and 87% accuracies for motor tasks with Support Vector Machines (SVM). The classification accuracies are quite high though the proposed one-versus-one technique worked well compared to the classical method. The results would be promising for a real-time study.


Keywords


multi-class EEG classification; channel reduction;optimizing output;Brain-computer interfaces (BCI)

Full Text:

PDF

References


Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G. & Vaughan, T.M. (2002). Brain-Computer Interfaces for Communication and Control, Clinical Neurophysiology, 113, 767-791.

Hwang, H.J., Kim, S., Choi, S. & Im, C.H. (2013). EEG-Based Brain-Computer Interfaces: A Thorough Literature Survey, International Journal of Human-Computer Interaction, 29(12), 814-826.

Brunner, C., Birbaumer, N., Blankertz, B., Guger, C., Kübler, A., Mattia, D., & Ramsey, N. (2015). BNCI Horizon 2020: towards a roadmap for the BCI community. Brain-computer interfaces, 2(1), 1-10.

Mühl, C., Allison, B., Nijholt, A., & Chanel, G. (2014). A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges. Brain-Computer Interfaces, 1(2), 66-84.

Bashashati, A., Fatourechi, M. (2007). A Survey of Signal Processing Algorithms in brain-computer interfaces based on electrical brain signals, Journal of Neural Engineering, 4, 32-57.

Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F. & Arnaldi, B. (2007). A Review of Classification Algortihms for EEG-based Brain-computer Interfaces, Journal of Neural Engineering, 4, R1-R13.

Schlögl, A., Lee, F., Bischof, H. & Pfurtscheller, G. (2005). Characterization of four-class Motor Imagery EEG Data for the BCI-competition, Journal of Neural Engineering, 2, L14-L22.

Li, T., Zhu, S. & Ogihara, M. (2006). Using Discriminant Analysis for Multi-class Classification: An Experimental Investigation, Knowledge of Information Systems, 10(4), 453-472.

Zhou, S.M., Gan, J.Q., & Sepulveda, F. (2008). Classifying Mental Tasks Based on Features of Higher-order Statistics from EEG Signals in Brain-computer Interface, Information Sciences, 178, 1629-1640.

Liang, N.Y., Saratchandran, P., Huang, G.B. & Sundararajan, N. (2006). Classification of Mental Tasks from EEG Signals Using Extreme Learning Machine, International Journal of Neural Systems, 16(1), 29-38.

Lee, F., Scherer, R., Leeb, R., Neuper, C., Bischof, H., & Pfurtscheller, G. (2005). A comparative analysis of multi-class EEG classification for brain computer interface. In Proceedings of the 10th Computer Vision Winter Workshop (195-204).

Keirn, Z. A., Aunon, J. I. (1990). A New Mode of Communication Between Man and His Surroundings. IEEE Transactions on biomedical engineering, 37,12.

Huan, N., Palaniappan, R. (2004). Brain Computer Interface Design Using Mental Task Classification, Multimedia Cyberscape Journal, 1, 35-43.

Flores, M.E., Cortés, J.M.R., Gil, P.G., Aquino, V.A. (2013). Mental Tasks Temporal Classification Using an Architecture Based on ANFIS and Recurrent Neural Networks, Recent Advances on Hybrid Intelligent Systems, Studies in Computational Intelligence 451, 135-146.

Solhjoo, S., Nasrabadi, A. M. & Golpayegani, M.R.H.(2005). Classification Of Chaotic Signals Using HMM Classifiers:EEG-Based Mental Task Classification, 13th Signal Processing Conference, September 4-8, 2005, Antalya,Turkey.

Tolić, M. & Jović, F. (2013). Classification of Wavelet Transformed EEG Signals With Neural Network For Imagined Mental And Motor Tasks, Kinesiology, 45(1), 130-138.

Schalk, G. (2009). EEG motor movement/imagery dataset. Retrieved November 18, 2011 from http://www.physionet. org/pn4/eegmmidb.

Özmen, N. G. & Gümüşel, L. (2010). Mental and Motor Task Classification by LDA, MEDICON 2010, IFMBE Proceedings, 29, 172-175.

Benbadis, S.R. (2006). Introduction to EEG. In: Lee-Chiong T. Sleep: A Comprehensive Handbook. Hoboken, NJ: Wiley & Sons Inc.

Carriea, J., Gorodnitsky, I.F. & Kutas, M. (2004). Automatic removal of eye movement and blink artifacts from EEG data using blind component separation, Psychophysiology, 41.

Wang, D., Miao, D. & Blohm, G. (2012). Multi-class motor imagery EEG decoding for brain-computer interfaces, Frontiers in Neuroscience, 6,151,1-13.

Sun, S. & Zhang, C.( 2006). Adaptive feature extraction for EEG signal classification, Medical Biological Engineering and Computing, 44, 931-935.

Hsu, W.Y. (2010). EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features, Journal of Neuroscience Methods, 189(2), 295–302.

Lin, Y., Wang, C.H., Jung, T.P., Wu, T. L., Jeng, S.K., Duann, J.R. & Chen, J. H. (2010). EEG-Based Emotion Recognition in Music Listening, IEEE Transactions On Biomedical Engineering, 57(7), 1798-806.

Barachant, A., Bonnet, S., Congedo, M. & Jutten, C. (2012). Multi-class Brain Computer Interface Classification by Riemannian Geometry, IEEE Transactions on Biomedical Engineering, 59(4), 920-928.

Nijboer, F., OudeBos, D. P., Blokland,Y., Wijk, R. & Farquhar, J. (2014). Design requirements and potential target users for brain-computer interfaces– recommendations from rehabilitation professionals, Brain-Computer Interfaces, 1(1), 50-61.

Özmen, N.G. & Gümüşel, L. (2011). Discrimination Between Mental and Motor Tasks of EEG Signals Using Different Classification Methods, 2011 IEEE International Conference on Innovations in Intelligent Systems and Applications, 15-18 June 2011.

Subasi, A., Erçelebi, E., Alkan, A., & Koklukaya, E. (2006). Comparison of Subspace-Based Methods With AR Parametric Methods In Epileptic Seizure Detection, Computers in Biology and Medicine, 36(2), 195-208.

Siuly, Y.L. & Wen, P. (2009). Classification of EEG signals using sampling techniques and least square support vector machines, Fourth International Conference on Rough Sets and Knowledge Technology 2009, LNCS 5589, 375-382.

Mosquera, C. G., Trigueros, A.M., Franco, J.I. & Vaszquez, A.N. (2010). New feature extraction approach for epileptic EEG signal detection using time-frequency distributions, Medical and Biological Engineering and Computing, 48(4), 321-330.

Rifkin, R. & Klautau, A. (2004). Parallel networks that learn to pronounce english text. Journal of Machine Learning Research, 5, 101–141.

Vapnik V. (1995). The Nature of Statistical Learning Theory, New York, Springer.

Anderson, C.W., Stolz, E.A. & Shamsunder, S. (1998). Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks, IEEE Transactions Biomedical Engineering, 45(3), 277-286.




DOI: http://dx.doi.org/10.11121/ijocta.01.2018.00516

Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 Nurhan Gursel Ozmen, Levent Gumusel

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

footer_771

   ithe_170     crossref_284         ind_131_43_x_117_117  Scopus  EBSCO_Host    ULAKBIM   PROQUEST   ZBMATH more...