Comparative Study of Different Ensemble Compositions in EEG Signal Classification Problem

The leading perspective of this paper is an introduction to three \(\left( Type-I, Type-II,\,and\,Type-III\right) \) types of ensemble architectures in Electroencephalogram (EEG) signal classification problem. Motor imagery EEG signal is filtered and subsequently used for three different types of feature extraction techniques: Wavelet-based Energy and Entropy \(\left( EngEnt\right) \), Bandpower \(\left( BP\right) \), and Adaptive Autoregressive (AAR). Ensemble architectures have been used in various compositions with different classifiers as base learners along with majority voting as the combined method. This standard procedure is also compared with the mean accuracy method obtained from multiple base classifiers. The Type-I ensemble architecture with EngEnt and BP feature sets provides most consistent performance for both majority voting and mean accuracy combining techniques. Similarly, Type-II architecture with EngEnt and AAR feature sets provides most consistent performance for both majority voting and mean accuracy combining techniques. However, the Type-III ensemble architecture contributes highest result \(82.86\%\) with K-Nearest Neighbor \(\left( KNN\right) \) classifier among all three types.

[1]  D. N. Tibarewala,et al.  Classification of lower limb motor imagery using K Nearest Neighbor and Naïve-Bayesian classifier , 2016, 2016 3rd International Conference on Recent Advances in Information Technology (RAIT).

[2]  Sumaira Tasnim,et al.  Ensemble Classifiers and Their Applications: A Review , 2014, ArXiv.

[3]  Debarshi Kumar Sanyal,et al.  An ensemble classification approach to motor-imagery brain state discrimination problem , 2017, 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS).

[4]  Rajdeep Chatterjee,et al.  EEG Based Motor Imagery Classification Using SVM and MLP , 2016, 2016 2nd International Conference on Computational Intelligence and Networks (CINE).

[5]  Brijesh Verma,et al.  Effect of ensemble classifier composition on offline cursive character recognition , 2013, Inf. Process. Manag..

[6]  Shayma Alani,et al.  Design of intelligent ensembled classifiers combination methods , 2015 .

[7]  Anatole Lécuyer,et al.  Comparative study of band-power extraction techniques for Motor Imagery classification , 2011, 2011 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB).

[8]  Fabien Lotte,et al.  Study of Electroencephalographic Signal Processing and Classification Techniques towards the use of Brain-Computer Interfaces in Virtual Reality Applications , 2008 .

[9]  Mohit Kumar Goel,et al.  An overview of brain computer interface , 2015, 2015 Recent and Emerging trends in Computer and Computational Sciences (RETCOMP).

[10]  G. Pfurtscheller,et al.  Continuous EEG classification during motor imagery-simulation of an asynchronous BCI , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  Debarshi Kumar Sanyal,et al.  Effects of wavelets on quality of features in motor-imagery EEG signal classification , 2016, 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).

[12]  Javier Gomez-Pilar,et al.  Ensemble learning for classification of motor imagery tasks in multiclass brain computer interfaces , 2014, 2014 6th Computer Science and Electronic Engineering Conference (CEEC).

[13]  Anne M. P. Canuto,et al.  Combining different ways to generate diversity in bagging models: An evolutionary approach , 2011, The 2011 International Joint Conference on Neural Networks.

[14]  Xi Chen,et al.  Feature extraction of motor imagery EEG signals based on wavelet packet decomposition , 2011, The 2011 IEEE/ICME International Conference on Complex Medical Engineering.

[15]  Alois Schlögl,et al.  The Electroencephalogram and the Adaptive Autoregressive Model: Theory and Applications , 2000 .

[16]  Shiliang Sun,et al.  An experimental evaluation of ensemble methods for EEG signal classification , 2007, Pattern Recognit. Lett..

[17]  Lior Rokach,et al.  Ensemble-based classifiers , 2010, Artificial Intelligence Review.

[18]  Kin Keung Lai,et al.  Investigation of Diversity Strategies in SVM Ensemble Learning , 2008, 2008 Fourth International Conference on Natural Computation.

[19]  Puteh Saad,et al.  A survey of analysis and classification of EEG signals for brain-computer interfaces , 2015, 2015 2nd International Conference on Biomedical Engineering (ICoBE).

[20]  Amit Konar,et al.  Performance analysis of ensemble methods for multi-class classification of motor imagery EEG signal , 2014, Proceedings of The 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC).

[21]  Swati Vaid,et al.  EEG Signal Analysis for BCI Interface: A Review , 2015, 2015 Fifth International Conference on Advanced Computing & Communication Technologies.

[22]  Yan Li,et al.  Improving the Separability of Motor Imagery EEG Signals Using a Cross Correlation-Based Least Square Support Vector Machine for Brain–Computer Interface , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[23]  Dezhong Yao,et al.  Common Spatial Pattern Ensemble Classifier and Its Application in Brain-Computer Interface , 2009 .