A pairwise output coding method for multi-class EEG classification of a self-induced BCI
DOI:
https://doi.org/10.11121/ijocta.01.2018.00516Keywords:
multi-class EEG classification, channel reduction, optimizing output, Brain-computer interfaces (BCI)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.
Downloads
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.
Downloads
Published
How to Cite
Issue
Section
License
Articles published in IJOCTA are made freely available online immediately upon publication, without subscription barriers to access. All articles published in this journal are licensed under the Creative Commons Attribution 4.0 International License (click here to read the full-text legal code). This broad license was developed to facilitate open access to, and free use of, original works of all types. Applying this standard license to your work will ensure your right to make your work freely and openly available.
Under the Creative Commons Attribution 4.0 International License, authors retain ownership of the copyright for their article, but authors allow anyone to download, reuse, reprint, modify, distribute, and/or copy articles in IJOCTA, so long as the original authors and source are credited.
The readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
- for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
This work is licensed under a Creative Commons Attribution 4.0 International License.
