The Improvement of a Brain Computer Interface Based on EEG Signals
Abstract
Purpose: Brain Computer Interface (BCI) has provided a novel way of communication that can significantly revolutionize life of people suffering from disabilities. Motor Imagery (MI) EEG BCI is one of the most promising solutions to address. The main phases of such systems include signal acquisition, pre-processing, feature extraction, classification and the intended interface. The challenging obstacles in such systems are to detect and extract efficient features that present reliability and robustness alongside promising classification accuracy. In this paper it is endeavored to present a robust method for a two-class MI BCI that results in high accuracy.
Materials and Methods: For this purpose, the dataset 2b from BCI competition 2008, consisting of three channels (C3, C and Cz), was utilized. Firstly, the signals were bandpass filtered. Secondly, Common Spatial Pattern (CSP) was employed and then a number of features, including non-linear chaotic features were extracted from channels C3 and C4. After feature selection phase the number of features were reduced to 38 and 47. Finally, these features were fed into two classifiers, namely Support Vector Machine classifier (SVM) and Bagging to evaluate the performance of the system.
Results: Classification accuracy and Cohen’s Kappa coefficient of the proposed method for two MI EEG channels are 96.40% and 0.92, respectively.
Conclusion: These results indicate the high accuracy and stability of our method in comparison with similar studies. Therefore, it can be a promising approach in two-class MI BCI systems.
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28- S. Seifpour, H. Niknazar, M. Mikaeili, A. M. Nasrabadi, “A new automatic sleep staging system based on statistical behavior of local extrema using single channel EEG signal”, Expert Systems with Applications, vol. 104, pp. 277-293, August 2018.
29- H. Niknazar, S. Seifpour, M. Mikaili, A. M. Nasrabadi, A. K. Banaraki, “A novel method to detect the phases of Cyclic Alternating Pattern (CAP) using similarity index”, 2015 23rd Iranian Conference on Electrical Engineering, pp. 67-71, July 2015.
30- J. Luo, X. Gao, X. Zhu, B. Wang, N. Lu, J. Wang, “Motor imagery EEG classification based on ensemble support vector learning”, Computer Methods and Programs in Biomedicine, vol. 193, September 2020.
31- L. Sun, Z. Feng, N. Lu, B. Wang, W. Zhang, “An advanced bispectrume features for EEG-based motor imagery classification”, Expert Systems with Applications, vol. 131, pp. 9-19, October 2019.
32- X. Tang, W. Li, X. Li, W. Ma, X. Dang, “Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network”, Expert Systems with Applications, vol. 149, July 2020.
33- J. luo, Z. Feng, N. lu, “Spatio-temporal discrepancy feature for classification of motor imageries”, Biomedical Signal Processing and Control, vol. 47, pp. 137-144, January 2019.
34- M. Dai, D. Zheng, R. Na, S. Wang, S. Zhang, “EEG classification of motor imagery using a novel deep learning framework”, Sensors, vol. 19, ISS. 3, January 2019.
2- N. S. Malan, S. Sharma, “Feature selection using regularized neighborhood component analysis to enhance the classification performance of motor imagery signals”, Computers in Biology and Medicine, vol. 107, pp. 118-126, 2019.
3- C. S. Nam, A. Nijholt, F. Lotte, “Brain-Computer Interfaces Handbook: Technological and Theoretical Advances”, Taylor and Francis, CRC Press, 2018.
4- G. Xu et al., “A deep transfer convolutional neural network framework for EEG signal classification”, IEEE Access, vol. 7, pp. 112767-112776, 2019.
5- J. Luo, J. Wang, R. Xu, K. Xu, “Class discrepancy-guided sub-band filter-based common spatial pattern for motor imagery classification”, Journal of Neuroscience Methods, vol. 323, pp. 98-107, 2019.
6- K. K. Ang, Z. Y. Chin, C. Wang, C. Guan, and H. Zhang, “Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b”, Frontiers in Neuroscience, vol. 6, pp. 39, 2012.
7- P. Gaur, R. . Pachori, H. Wang, G. Prasad, “An empirical mode decomposition based filtering method for classification of motor-imagery EEG signals for enhancing brain-computer interface”, 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1-7, October 2015.
8- Y. R. Tabar, U. Halici, “A novel deep learning approach for classification of EEG motor imagery signals”, Journal of Neural Engineering, vol. 14, no. 1, November 2016.
9- C. Kim, J. Sun, D. Liu, Q. Wang, S. Paek, “An effective feature extraction method by power spectral density of EEG signals for 2-class motor imagery-based”, Medical and Biological Engineering and Computation, vol. 56, pp. 1645-1658, March 2018.
10- N. Bagh, M. R. Reddy, “Hilbert transform-based event-related patterns for motor imagery brain-computer interface”, Biomedical Signal Processing and Control, vol. 62, September 2020.
11- R. Leeb, C. Brunner, G. R. Muller-Putz, A. Schlogl, G. Pfurtscheller, BCI Competition 2008 – Graz data set B, Graz University of Technology, Austria, 2008.
12- F. Lotte, “A tutorial on EEG signal processing techniques for mental state recognition in brain-computer interfaces”, Guide to Brain-Computer Music Interfacing, 2014.
13- G. Pfurtscheller, F.H. Lopes da Silva, “Event-related EEG/MEG synchronization and desynchronization: basic principles”, Clinical Neurophysiology, vol. 110, ISS. 11, pp. 1842-1857, November 1999.
14- Z. Rosta akova, R. Rosipal, S. Seifpour, L. J. Trejo, “A comparison of non-negative Tucker decomposition and parallel factor analysis for identification and measurement of human EEG rhythms”, Measurement Science Review, vol. 20, ISS. 3, March 2008.
15- G. Feng, L. Hao, G. Nuo, “Feature extraction algorithm based on CSP and wavelet packet for motor imagery EEG signals”, 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP), pp. 798-802, 2019.
16- M. Clerc, L. Bougrain, F. Lotte, “Brain-Computer Interfaces 1: Foundations and Methods”, ISTE Ltd and John Wiley & Sons Inc., 2016.
17- R.Boostani, M. H. Moradi, “A new approach in the BCI research based on fractal dimension as feature and Adaboost as classifier”, Journal of Neural Engineering, vol. 1, no. 4, pp. 212-217, November 2004.
18- S. Bayatfar, S. Seifpour, M. A. Oskoei, A. Khadem, “An automated system for diagnosis of sleep apnea syndrome using single-channel EEG signals”, 2019 27th Iranian Conference on Electrical Engineering (ICEE), pp. 1829-1833, August 2019.
19- S. Lahmiri, “Generalized Hurst exponent estimates differentiate EEG signals of healthy and epileptic patients”, Physica A: Statistical Mechanics and its Applications, vol. 490, pp. 378-385, January 2018.
20- S. Geng, W. Zhou, Q. Yuan, D. Cai, Y. Zeng, “EEG non-linear feature extraction using correlation dimension and Hurst exponent”, Neurological Research, vol. 33, ISS. 9, pp. 908-912, 2011.
21- K. Natarajan, R. Acharya U, F. Alias, T, Tiboleng, S. K. Puthusserypady, “Nonlinear analysis of EEG signals at different mental states”, BioMedical Engineering Online 3, article no. 7, March 2004.
22- G. Chandrashekar and F. Sahin, “A survey on feature selection methods”, Computers and Electrical Engineering, vol. 40, pp. 16-28, 2014.
23- A. C. Haury, P. Gestraud, and J. P. Vert, “The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures”, hal-00559580v1, 2010.
24- R. A. Armstrong, “When to use the Bonferroni correction”, Ophthalmic and Physiological Optics, vol. 34, pp. 502-508, 2014.
25- F. Lotte, M. Congedo, A. Lecuyer, F. Lamarche, and B. Arnaldi, “A review of classification algorithms for EEG-based brain-computer interfaces”, Journal of Neural Engineering, vol. 4, no. 2, January 2007.
26- A. Khorshidtalab, M. J. E. Salami, “EEG signal classification for real-time brain-computer interface applications: A review”, 2011 4th International Conference on Mechatronics (ICOM), pp. 1-7, July 2011.
27- Z. H. Zhou, “Ensemble Methods: Foundations and Algorithms”, Taylor and Francis, CRC Press, 2012.
28- S. Seifpour, H. Niknazar, M. Mikaeili, A. M. Nasrabadi, “A new automatic sleep staging system based on statistical behavior of local extrema using single channel EEG signal”, Expert Systems with Applications, vol. 104, pp. 277-293, August 2018.
29- H. Niknazar, S. Seifpour, M. Mikaili, A. M. Nasrabadi, A. K. Banaraki, “A novel method to detect the phases of Cyclic Alternating Pattern (CAP) using similarity index”, 2015 23rd Iranian Conference on Electrical Engineering, pp. 67-71, July 2015.
30- J. Luo, X. Gao, X. Zhu, B. Wang, N. Lu, J. Wang, “Motor imagery EEG classification based on ensemble support vector learning”, Computer Methods and Programs in Biomedicine, vol. 193, September 2020.
31- L. Sun, Z. Feng, N. Lu, B. Wang, W. Zhang, “An advanced bispectrume features for EEG-based motor imagery classification”, Expert Systems with Applications, vol. 131, pp. 9-19, October 2019.
32- X. Tang, W. Li, X. Li, W. Ma, X. Dang, “Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network”, Expert Systems with Applications, vol. 149, July 2020.
33- J. luo, Z. Feng, N. lu, “Spatio-temporal discrepancy feature for classification of motor imageries”, Biomedical Signal Processing and Control, vol. 47, pp. 137-144, January 2019.
34- M. Dai, D. Zheng, R. Na, S. Wang, S. Zhang, “EEG classification of motor imagery using a novel deep learning framework”, Sensors, vol. 19, ISS. 3, January 2019.
| Files | ||
| Issue | Vol 7 No 4 (2020) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v7i4.5323 | |
| Keywords | ||
| Brain Computer Interface Electroencephalography Signal Motor Imagery Common Spatial Pattern Non-linear and Chaotic Features Support Vector Machine | ||
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How to Cite
1.
Khakpour M. The Improvement of a Brain Computer Interface Based on EEG Signals. Frontiers Biomed Technol. 2020;7(4):259-265.
