Multi-Layer Perceptron Neural Network Classifier with Binary Particle Swarm Optimization Based Feature Selection for Brain-Computer Interfaces

Authors: K. Akilandeswari, G. M. Nasira

Abstract:

Brain-Computer Interfaces (BCIs) measure brain signals activity, intentionally and unintentionally induced by users, and provides a communication channel without depending on the brain’s normal peripheral nerves and muscles output pathway. Feature Selection (FS) is a global optimization machine learning problem that reduces features, removes irrelevant and noisy data resulting in acceptable recognition accuracy. It is a vital step affecting pattern recognition system performance. This study presents a new Binary Particle Swarm Optimization (BPSO) based feature selection algorithm. Multi-layer Perceptron Neural Network (MLPNN) classifier with backpropagation training algorithm and Levenberg-Marquardt training algorithm classify selected features.

Keywords: Brain-Computer Interfaces (BCI), Feature Selection (FS), Walsh–Hadamard Transform (WHT), Binary Particle Swarm Optimization (BPSO), Multi-Layer Perceptron (MLP), Levenberg–Marquardt algorithm.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1109968

References:

[1] Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain–computer interfaces for communication and control. Clinical neurophysiology, 113(6), 767-791.
[2] Vallabhaneni, A., Wang, T., & He, B. (2005). Brain—computer interface. In Neural engineering (pp. 85-121). Springer US.
[3] Millán, J. D. R., Franzé, M., Mouriño, J., Cincotti, F., & Babiloni, F. (2002). Relevant EEG features for the classification of spontaneous motor-related tasks. Biological cybernetics, 86(2), 89-95.
[4] Khushaba, R. N., Al-Ani, A., & Al-Jumaily, A. (2008, December). Differential evolution based feature subset selection. In Pattern Recognition, 2008. ICPR 2008. 19th International Conference on (pp. 1- 4). IEEE.
[5] Zhao, Z., Morstatter, F., Sharma, S., Alelyani, S., Anand, A., & Liu, H. (2010). Advancing feature selection research. ASU feature selection repository.
[6] Koprinska, I. (2010). Feature selection for brain-computer interfaces. In New frontiers in applied data mining (pp. 106-117). Springer Berlin Heidelberg.
[7] Schalk, G., & Leuthardt, E. C. (2011). Brain-computer interfaces using electrocorticographic signals. Biomedical Engineering, IEEE Reviews in, 4, 140-154.
[8] Leuthardt, E. C., Schalk, G., Wolpaw, J. R., Ojemann, J. G., & Moran, D. W. (2004). A brain–computer interface using electrocorticographic signals in humans. Journal of neural engineering, 1(2), 63.
[9] Shenoy, P., Miller, K. J., Ojemann, J. G., & Rao, R. P. (2008). Generalized features for electrocorticographic BCIs. Biomedical Engineering, IEEE Transactions on, 55(1), 273-280.
[10] Mishra, A. K., Das, M., & Panda, T. C. (2013). Hybrid Swarm Intelligence Technique for CBIR Systems. International Journal of Computer Science Issues (IJCSI), 10(2).
[11] Abraham, A., Guo, H., & Liu, H. (2006). Swarm intelligence: foundations, perspectives and applications (pp. 3-25). Springer Berlin Heidelberg.
[12] Moubayed, A. (2010). Binary-SDMOPSO and its application in channel selection for Brain-Computer Interfaces. In 2010 UK Workshop on Computational Intelligence (UKCI) (pp. 1-6).
[13] Ang, K. K., Yu, J., & Guan, C. (2012, March). Extracting effective features from high density nirs-based BCI for assessing numerical cognition. InAcoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on (pp. 2233-2236). IEEE.
[14] Polat, D., & Cataltepe, Z. (2012, April). Feature selection and classification on brain computer interface (BCI) data. In Signal Processing and Communications Applications Conference (SIU), 2012 20th (pp. 1-4). IEEE.
[15] Hatamikia, S., Nasrabadi, A. M., & Shourie, N. (2014, November). Plausibility assessment of a subject independent mental task-based BCI using electroencephalogram signals. In Biomedical Engineering (ICBME), 2014 21th Iranian Conference on (pp. 150-155). IEEE.
[16] Zhiping, H., Guangming, C., Cheng, C., He, X., & Jiacai, Z. (2010, November). A new EEG feature selection method for self-paced braincomputer interface. InIntelligent Systems Design and Applications (ISDA), 2010 10th International Conference on (pp. 845-849). IEEE.
[17] Nasehi, S., & Pourghassem, H. (2011, May). A novel effective feature selection algorithm based on S-PCA and wavelet transform features in EEG signal classification. In Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on (pp. 114-117). IEEE.
[18] Huang, D., Qian, K., Oxenham, S., Fei, D. Y., & Bai, O. (2011, April). Event-related desynchronization/synchronization-based brain-computer interface towards volitional cursor control in a 2D center-out paradigm. In Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), 2011 IEEE Symposium on (pp. 1-8). IEEE.
[19] Samiee, S., Hajipour, S., & Shamsollahi, M. B. (2010, June). Five-class finger flexion classification using ECoG signals. In Intelligent and Advanced Systems (ICIAS), 2010 International Conference on (pp. 1-4). IEEE.
[20] Elsawy, A. S., Eldawlatly, S., Taher, M., & Aly, G. M. (2013, September). A principal component analysis ensemble classifier for P300 speller applications. In Image and Signal Processing and Analysis (ISPA), 2013 8th International Symposium on (pp. 444-449). IEEE.
[21] Yang, Y., Chevallier, S., Wiart, J., & Bloch, I. (2012, August). Timefrequency selection in two bipolar channels for improving the classification of motor imagery EEG. In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE (pp. 2744-2747). IEEE.
[22] Gottemukkula, V., & Derakhshani, R. (2011, April). Classificationguided feature selection for NIRS-based BCI. In Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on (pp. 72-75). IEEE.
[23] Gonzalez, A., Nambu, I., Hokari, H., Iwahashi, M., & Wada, Y. (2013, October). Towards the Classification of Single-Trial Event-Related Potentials Using Adapted Wavelets and Particle Swarm Optimization. In Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on (pp. 3089-3094). IEEE.
[24] Bhattacharyya, S., Rakshiti, P., Konar, A., Tibarewala, D. N., Das, S., & Nagar, A. K. (2013, April). Differential evolution with temporal difference Q-learning based feature selection for motor imagery EEG data. In Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), 2013 IEEE Symposium on (pp. 138-145). IEEE.
[25] Shahriari, Y., & Erfanian, A. (2011, April). A mutual information based channel selection scheme for P300-based brain computer interface. In Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on (pp. 434-437). IEEE.
[26] Jayathilake, A. A. C. A., Perera, A. A. I., & Chamikara, M. A. P. Discrete Walsh-Hadamard Transform in Signal Processing.
[27] Johnson, J., & Puschel, M. (2000). In search of the optimal Walsh- Hadamard transform. In Acoustics, Speech, and Signal Processing, 2000. ICASSP'00. Proceedings. 2000 IEEE International Conference on (Vol. 6, pp. 3347-3350). IEEE.
[28] Sasikala, D., & Neelaveni, R. (2010). Correlation coefficient measure of multimodal brain image registration using fast walsh hadamard transform. Journal of Theoretical & Applied Information Technology, 22(2).
[29] Abdolreza Asadi Ghanbari, Karim Adinehvand, & Mousa Mohammad Nia (2014) Overhead Reduction in EEG signals using Particle Swarm Optimization and Independent Component Analysis
[30] Hasan, B. A. S., & Gan, J. Q. (2009). Multi-objective particle swarm optimization for channel selection in brain-computer interfaces.
[31] Das, S., Abraham, A., & Konar, A. (2008). Particle swarm optimization and differential evolution algorithms: technical analysis, applications and hybridization perspectives. In Advances of Computational Intelligence in Industrial Systems (pp. 1-38). Springer Berlin Heidelberg.
[32] Balochian, S., Seidabad, E. A., & Rad, S. Z. (2013). Neural Network Optimization by Genetic Algorithms for the Audio Classification to Speech and Music. International Journal of Signal Processing, Image Processing & Pattern Recognition, 6(3).
[33] Taravat, A., Proud, S., Peronaci, S., Del Frate, F., & Oppelt, N. (2015). Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking. Remote Sensing, 7(2), 1529-1539.
[34] Lotte, F., Congedo, M., Lécuyer, A., & Lamarche, F. (2007). A review of classification algorithms for EEG-based brain–computer interfaces. Journal of neural engineering, 4.
[35] Craven, M. P. (1997). A faster learning neural network classifier using selective backpropagation.
[36] CH Satyananda Reddy (2012) design of multilayer perceptron neural network for mental task recognition, 1(1), 74-80.
[37] Yu, H., & Wilamowski, B. M. (2011). Levenberg-marquardt training. Industrial Electronics Handbook, 5, 12-1.
[38] Sapna, S., Tamilarasi, A., & Kumar, M. P. (2012). Backpropagation learning algorithm based on Levenberg Marquardt Algorithm. DOI= DOI, 10.
[39] Mojtaba Ahmadieh Khanesar, Hassan Tavakoli, Mohammad Teshnehlab and Mahdi Aliyari Shoorehdeli (2009). Novel Binary Particle Swarm Optimization, Particle Swarm Optimization, Aleksandar Lazinica (Ed.),
[40] Khanesar, M. A., Teshnehlab, M., & Shoorehdeli, M. A. (2007, June). A novel binary particle swarm optimization. In Control & Automation, 2007. MED'07. Mediterranean Conference on (pp. 1-6). IEEE.