Motor imagery signal classification using Wavelet packet decomposition and modified binary grey wolf optimization

Measurement: Sensors - Tập 24 - Trang 100553 - 2022
Pawan1, Rohtash Dhiman1
1Department of Electrical Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal (Sonipat), Haryana, 131039, India

Tài liệu tham khảo

Mulder, 2007, Motor imagery and action observation: cognitive tools for rehabilitation, J. Neural. Transm., 114, 1265, 10.1007/s00702-007-0763-z Nicolas-alonso, 2012, 1211 Al-Fahoum, 2014, Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains, ISRN Neurosci, 1, 10.1155/2014/730218 Procházka, 2008, Wavelet transform use for feature extraction and EEG signal segments classification, 719 R. V. Wankar, P. Shah, and R. Sutar, “Feature extraction and selection methods for motor imagery EEG signals: a review,” Proceedings of 2017 International Conference on Intelligent Computing and Control, I2C2 2017, vol. vol. 2018-Janua, pp. 1–9, 2018, doi: 10.1109/I2C2.2017.8321831. Brusini, 2021, A systematic review on motor-imagery brain-connectivity-based computer interfaces, IEEE Trans Hum Mach Syst, 51, 725, 10.1109/THMS.2021.3115094 Al-nafjan, 2022 Manjula, 2018, A comparative study on feature extraction and classification of mind waves for brain computer interface (BCI), Int. J. Eng. Technol., 7, 132, 10.14419/ijet.v7i1.9.9749 V. Bono, W. Jamal, S. Das, and K. Maharatna, “Artifact Reduction in Multichannel Pervasive Eeg Using Hybrid Wpt- Ica and Wpt-Emd Signal Decomposition Techniques”. Tanwani, 2009, Guidelines to select machine learning scheme for classification of biomedical datasets, Lect. Notes Comput. Sci., 5483, 128, 10.1007/978-3-642-01184-9_12 Gupta, 2021, A GUI based application for low intensity object classification count using SVM approach, vol. 2021, 299 Lotte, 2007, A review of classification algorithms for EEG-based brain-computer interfaces, J. Neural. Eng., 4, 10.1088/1741-2560/4/2/R01 Md Isa, 2017, The performance analysis of K-nearest neighbors (K-NN) algorithm for motor imagery classification based on EEG signal, MATEC Web of Conferences, 140, 10.1051/matecconf/201714001024 Xiao, 2021, Motor imagery EEG signal recognition using deep convolution neural network, Front. Neurosci., 15, 1, 10.3389/fnins.2021.655599 Jiao, 2014, An algorithm for improving the coefficient accuracy of wavelet packet analysis, Measurement, 47, 207, 10.1016/j.measurement.2013.08.049 Pattnaik, 2017, DWT-based feature extraction and classification for motor imaginary EEG signals, 186 Bennasar, 2015, Feature selection using joint mutual information maximisation, Expert Syst. Appl., 42, 8520, 10.1016/j.eswa.2015.07.007 ZorarpacI, 2016, A hybrid approach of differential evolution and artificial bee colony for feature selection, Expert Syst. Appl., 62, 91, 10.1016/j.eswa.2016.06.004 Idowu, 2020, Bio-inspired algorithms for optimal feature selection in motor imagery-based brain-computer interface, vol. 2020, 519 Jaffino, 2021, Grey Wolf optimization with deep recurrent neural network for epileptic seizure detection in EEG signals Li, 2021, Identification of emotion using electroencephalogram by tunable Q-factor wavelet transform and binary gray wolf optimization, Front. Comput. Neurosci., 15, 1, 10.3389/fncom.2021.732763 El-Kenawy, 2020, MbGWO-SFS: modified binary grey wolf optimizer based on stochastic fractal search for feature selection, IEEE Access, 8, 107635, 10.1109/ACCESS.2020.3001151 Evans, 1999, Abnormal qeeg patterns associated with dissociation and violence, J. Neurother., 3, 21, 10.1300/J184v03n02_03 Lazarou, 2018, EEG-based brain-computer interfaces for communication and rehabilitation of people with motor impairment: a novel approach of the 21st century, Front. Hum. Neurosci., 12, 1, 10.3389/fnhum.2018.00014 Sayilgan, 2021, Evaluation of mother wavelets on steady-state visually-evoked potentials for triple-command brain-computer interfaces, Turk. J. Electr. Eng. Comput. Sci., 25, 2263, 10.3906/elk-2010-26 Chaudhary, 2020, Non-dyadic wavelet decomposition for sensory-motor imagery EEG classification, Brain-Comp. Interf., 7, 11, 10.1080/2326263X.2020.1736453 Decety, 1990, Brain structures participating in mental, Acta Psychol., 73, 13, 10.1016/0001-6918(90)90056-L Pfurtscheller, 1998, Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters, IEEE Trans. Rehabil. Eng., 6, 316, 10.1109/86.712230 Ramakuri, 2017, Behaviour state analysis through brain computer interface using wearable EEG devices: a review, Electron. Govern., 13, 377, 10.1504/EG.2017.087994 Tacchino, 2020, Bicoherence interpretation in EEG requires signal to noise ratio quantification: an application to sensorimotor rhythms, IEEE Trans. Biomed. Eng., 67, 2696, 10.1109/TBME.2020.2969278 Garrett, 2003, Comparison of linear, nonlinear, and feature selection methods for EEG signal classification, IEEE Trans. Neural Syst. Rehabil. Eng., 11, 141, 10.1109/TNSRE.2003.814441 Herman, 2008, Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification, IEEE Trans. Neural Syst. Rehabil. Eng., 16, 317, 10.1109/TNSRE.2008.926694 Al Canaan, 2022, 141 Zhang, 2021, Research on feature extraction algorithm commonly used in brain-computer interface technology, J Phys Conf Ser, 1861, 10.1088/1742-6596/1861/1/012027 Kant, 2019 Daqrouq, 2016, Wavelet based method for congestive heart failure recognition by three confirmation functions, Comput. Math. Methods Med., 2016, 10.1155/2016/7359516 Medina Salgado, 2015, Fuzzy entropy relevance analysis in DWT and EMD for BCI motor imagery applications, Ingenieria, 20 Alhakeem, 2020, Wheelchair free hands navigation using robust DWT-AR features extraction method with muscle brain signals, IEEE Access, 8, 64266, 10.1109/ACCESS.2020.2984538 Xue, 2003, Wavelet packet transform for feature extraction of EEG during mental tasks, vol. 1, 360 Kołodziej, 2011, A new method of EEG classification for BCI with feature extraction based on higher order statistics of wavelet components and selection with genetic algorithms, Lect. Notes Comput. Sci., 6593, 280, 10.1007/978-3-642-20282-7_29 Malan, 2019, Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals, Comput. Biol. Med., 107, 118, 10.1016/j.compbiomed.2019.02.009 Rahman, 2019, Four-class motor imagery EEG signal classification using PCA, wavelet and two-stage neural network, Int. J. Adv. Comput. Sci. Appl., 10, 481 Alotaiby, 2015, A review of channel selection algorithms for EEG signal processing, EURASIP J. Appl. Signal Process., 10.1186/s13634-015-0251-9 He, 2009, Bhattacharyya bound based channel selection for classification of motor imageries in EEG signals, Chin. Control. Decis. Conf., 2009, 2353 Shan, 2012, EEG-based motor imagery classification accuracy improves with gradually increased channel number, Proceed. Ann. Int. Conf. IEEE Eng. Med. Biol. Soc, EMBS, 1695 Qi, 2020, Channel and feature selection for a motor imagery-based BCI system using Multilevel particle swarm optimization, Comput. Intell. Neurosci., 2020, 10.1155/2020/8890477 Alyasseri, 2022, EEG channel selection for person identification using binary grey wolf optimizer, IEEE Access, 10, 10500, 10.1109/ACCESS.2021.3135805 Yang, 2012, Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach, Artif. Intell. Med., 55, 117, 10.1016/j.artmed.2012.02.001 Abenna, 2022, An enhanced EEG prediction system for motor cortex-imagery tasks using SVM, E3S Web. Conf., 351, 10.1051/e3sconf/202235101026 S. K. Mosavi, E. Jalalian, and F. S. Gharahchopog, “A comprehensive survey of grey wolf optimizer algorithm and its application,” Int. J. Adv. Robot. Expet Syst., vol. 1, no. 6, pp. 23–45. Hamad, 2018, A hybrid EEG signals classification approach based on grey wolf optimizer enhanced SVMS for epileptic detection, Adv. Intell. Syst. Comput., 639, 108, 10.1007/978-3-319-64861-3_10 Hu, 2020, Improved binary grey wolf optimizer and its application for feature selection, Knowl. Base Syst., 195, 10.1016/j.knosys.2020.105746 Tangermann, 2012, Review of the BCI competition IV, Front. Neurosci., 6, 1, 10.3389/fnins.2012.00055 Zhang, 2018, A review of EEG-based brain-computer interface systems design, Brain Sci. Adv., 4, 156, 10.26599/BSA.2018.9050010 Zhao, 2009, Application of wavelet packet technique in BCI, 3 Aggarwal, 2019, Signal processing techniques for motor imagery brain computer interface: a review, Array, 1–2 Dhiman, 2017, Biogeography based hybrid scheme for automatic detection of epileptic seizures from EEG signatures, Appl. Soft Comput. J., 51, 116, 10.1016/j.asoc.2016.12.009 Pincus, 1991, Approximate entropy as a measure of system complexity, Proc. Natl. Acad. Sci. U. S. A., 88, 2297, 10.1073/pnas.88.6.2297 Acharya, 2013, Automated EEG analysis of epilepsy: a review, Knowl. Base Syst., 45, 147, 10.1016/j.knosys.2013.02.014 Dhiman, 2018, Motor imagery classification from human EEG signatures, Int. J. Biomed. Eng. Technol., 26, 101, 10.1504/IJBET.2018.089265 Mirjalili, 2014, Grey wolf optimizer, Adv. Eng. Software, 69, 46, 10.1016/j.advengsoft.2013.12.007 Emary, 2016, Binary grey wolf optimization approaches for feature selection, Neurocomputing, 172, 371, 10.1016/j.neucom.2015.06.083 Thaher, 2022, An Enhanced Evolutionary Based Feature Selection Approach Using Grey Wolf Optimizer for the Classification of High-dimensional Biological Data, J. Univers. Comput. Sci., 28 Yazdani, 2009, Classification of EEG signals using Dempster Shafer theory and a K-nearest neighbor classifier, vol. 1, 327 Mridha, 2021, Brain-computer interface: advancement and challenges, Sensors, 21, 10.3390/s21175746 Tibrewal, 2022, Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users, PLoS One, 17, 10.1371/journal.pone.0268880 Yin, 2021, Optimal channel-based sparse time-frequency blocks common spatial pattern feature extraction method for motor imagery classification, Math. Biosci. Eng., 18, 4247, 10.3934/mbe.2021213 Tiwari, 2021, A novel channel selection method for BCI classification using dynamic channel relevance, IEEE Access, 9, 126698, 10.1109/ACCESS.2021.3110882 Jiang, 2020, Temporal combination pattern optimization based on feature selection method for motor imagery BCIs, Front. Hum. Neurosci., 14, 1, 10.3389/fnhum.2020.00231