Elbow Joints for Upper-Limb Prosthesis: Analysis of Biomedical EEG Signals using Discrete Wavelet Transform

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Umashankar G, Vimala Juliet A, Hari Krishnan G
DOI : 10.14445/22315381/IJETT-V70I7P220

How to Cite?

Umashankar G, Vimala Juliet A, Hari Krishnan G, "Elbow Joints for Upper-Limb Prosthesis: Analysis of Biomedical EEG Signals using Discrete Wavelet Transform" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 190-197, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P220

Abstract
Signal classification is an essential feature in cognitive science, which separates large datasets into classes based on frequency. This research was conducted to analyze the brain signals by signal classification using a convolutional neural network (CNN) to obtain the required frequency spectrum. The signals can be used for upper-limb prostheses, especially elbow joint applications. The feature extraction process is an important step in brain signal classification. During the current study, electroencephalography (EEG) signals are extracted using a 10-20 electrode system from the flexion and extension movement of the elbow joints. Using MATLAB tools, it is done through a user interface. The expected performance is obtained as an exact parameter analysis, e.g., the classifier's precision, simplicity, and sensitivity using a convolution neural network should be connected as a benchmark for applications for the upper-limb prosthesis.

Keywords
Convolutional Neural Network, Elbow joint, Electroencephalography, Prosthesis, Signals.

Reference
[1] Iqbal, M.U., Shahab, M.A., Choudhary, M., Srinivasan, B., Srinivasan, R, “Electroencephalography (EEG) Based Cognitive Measures for Evaluating the Effectiveness of Operator Training,” Process Saf Environ, vol.150, Pp.51-67, 2021.
[2] Su, Y., Chen, P., Liu, X., Li, W., Lv, Z, “A Spatial Filtering Approach To Environmental Emotion Perception Based on Electroencephalography,” Med Eng Phys, vol.60, Pp.77-85, 2018.
[3] T. Sudhakar, G. Hari Krishnan, N. R. Krishnamoorthy, B. Janney J, M. Pradeepa and J. P. Raghavi, “Sleep Disorder Diagnosis Using EEG Based Deep Learning Techniques,” in Proc. ICBSII 2021, Pp.1-4, 2021.
[4] Hari Krishnan, G., Hemalatha, R.J., Umashankar, G., Ahmed, N., Nayak, S.R, “Development of Magnetic Control System for Electric Wheel Chair Using Tongue,” Series Adv Intellsystcomput, vol.308, Pp.635–41, 2014.
[5] Krishnan, G.H., Natarajan, R.A., Nanda, A, “Microcontroller Based non Invasive Diagnosis of Knee Joint Diseases,” in Proc. ICICES vol. 2014, no. 7034178, 2015.
[6] Rong, Y., Wu, X., Zhang, Y, “ Classification of Motor Imagery Electroencephalography Signals Using Continuous Small Convolutional Neural Network,” Int J Imagsyst Tech, vol.30, no.3, Pp.653-659, 2020.
[7] Saha, M., Das, S, “Electrochemical Detection of L-Serine and L-Phenylalanine At Bamboo Charcoal–Carbon Nanosphere Electrode,” Jnanostructure Chem, vol.4, Pp.102, 2014.
[8] Khosla, A., Khandnor, P., Chand, T, “A Comparative Analysis of Signal Processing and Classification Methods for Different Applications Based on EEG Signals,” Biocybern Biomed Eng, vol.40, no.2, Pp.649-690, 2020.
[9] Liang, W., Pei, H., Cai, Q., Wang, Y, “Scalp EEG Epileptogenic Zone Recognition and Localization Based on Long-Term Recurrent Convolutional Network,” Neurocomputing, vol.396, Pp.569-576, 2020.
[10] Usman, S.M., Khalid, S., Akhtar, R., Bortolotto, Z., Bashir, Z., Qiu, H, “Using Scalp EEG and Intracranial EEG Signals for Predicting Epileptic Seizures: Review of Available Methodologies,” Seizure, vol.71, Pp.258-269, 2019.
[11] Chakraborty, J., Nandy, A, “Discrete Wavelet Transform Based Data Representation in Deep Neural Network for Gait Abnormality Detection,” Biomed Signal Process Control, vol.62, Pp.102076, 2020.
[12] Santhosh, S., Juliet, A.V. & Krishnan, G.H, “Predictive Analysis of Identification and Disease Condition Monitoring Using Bioimpedance Data,” J Ambient Intell Human Comput, vol.12, Pp.2955–2963, 2021.
[13] Zarei, A., Asl, B.M, “Automatic Seizure Detection Using Orthogonal Matching Pursuit, Discrete Wavelet Transform, and Entropy Based Features of EEG Signals,” Computbiol Med, vol.131, Pp.104250, 2021.
[14] Bajpai, R., Yuvaraj, R., Prince, A, “Automated EEG Pathology Detection Based on Different Convolutional Neural Network Models: Deep Learning Approach, “ Computbiol Med, vol.133, Pp.104434, 2021.
[15] Raghu, S., Sriraam, N., Temel, Y., Rao, S.V., Kubben, P.L, “ EEG Based Multi-Class Seizure Type Classification Using Convolutional Neural Network and Transfer Learning, “ Neural Netw, vol.124, Pp.202-212, 2020.
[16] Yildiz, A., Zan, H., Said, S, “Classification and Analysis of Epileptic EEG Recordings Using Convolutional Neural Network and Class Activation Mapping, Biomed Signal Process, Control,” vol.68, Pp.102720, 2021.
[17] Guru Anand, V., Hari Krishna, G., Mohandass, G., Hemalatha, R.J., Sundaram, S, “ Predicting Grade of Prostate Cancer Using Image Analysis Software,” in Proc. TISC-2010, no.5714621, Pp.122–124, 2010.
[18] Diwakar, M., Tripathi, A., Joshi, K., Sharma, A., Singh, P., Memoria, M., Kumar, N, “A Comparative Review: Medical Image Fusion Using SWT and DWT,” Mater. Today: Proc, vol.37. no.2, Pp. 3411-3416, 2021.
[19] Vijayarajan, R., Muttan, S, “ Discrete Wavelet Transform Based Principal Component Averaging Fusion for Medical Images,” AEUInt. Jelectron Commun, vol.69, no.6, Pp. 896-902, 2015.
[20] Q!Electreng Technol, vol.9, no.6, Pp.2114–2117, 2014.
[21] Guerrero, M.C., Parada, J.S., Espitia, H.E, “EEG Signal Analysis Using Classification Techniques: Logistic Regression, Artificial Neural Networks, Support Vector Machines, and Convolutional Neural Networks,” Heliyon, vol.7, no.6, Pp.E07258, 2021.
[22] Mammone, N., Leracitano, C., Morabito, F.C, “A Deep CNN Approach To Decode Motor Preparation of Upper Limbs From Time– Frequency Maps of EEG Signals At Source Level,” Neural Netw, vol.124, Pp.357-372, 2020.
[23] Asadzadeh, S, Rezaii, T.Y., Beheshti, S., Delpak, A., Meshgini, S, “A Systematic Review of EEG Source Localization Techniques and Their Applications on Diagnosis of Brain Abnormalities,” J Neurosci Methods, vol.339, Pp.108740, 2020.
[24] Miragila, F., Tomino, C., Vecchio, F., Alu, F., Orticoni, A., Judica, E., Cotelli, M., Rossini, P.M, “Assessing the Dependence of the Number of EEG Channels in the Brain Networks’ Modulations,” Brain Res Bull, vol.167, Pp.33-36, 2021.
[25] Vijayaraghavan, V., Garg, A., Wong, C.H., Tai, K., Bhalerao, Y,“Predicting the Mechanical Characteristics of Hydrogen Functionalized Graphene Sheets Using Artificial Neural Network Approach, “ J Nanostructure Chem, vol.3, Pp.83, 2013.
[26] Santhosh S, Juliet V, Krishnan G. H, “Impact of Electrodes Separation Distance on Bio-Impedance Diagnosis, “ Biomed Pharmacol J. vol.14, no.1, 2021.
[27] Yogendra Narayan. "Motor-Imagery based EEG Signals Classification using MLP and KNN Classifiers" International Journal of Engineering Trends and Technology 69.1(2021):121-125.
[28] Yao, Y., Plested, J., Gedeon, T, “Information-Preserving Feature Filter for Short-Term EEG Signals,” Neurocomputing, vol.408, Pp.91-99, 2020.
[29] Krishnan, G.H, Nanda, A., Natarajan, A, “Synovial Fluid Density Measurement for Diagnosis of Arthritis,” Biomed Pharmacol J, vol.7, no.1, Pp.221–224, 2014.
[30] Guido, R.C, “A note on A Practical Relationship Between Filter Coefficients and Scaling and Wavelet Functions of Discrete Wavelet Transforms, “ Appl Math Lett, vol.24, no.7, Pp.1257-1259, 2021.
[31] Tabassum, F., Islam, M.I., Amin, M.R, “Comparison of Filter Banks of DWT in Recovery of Image Using One Dimensional Signal Vector,” J. King Saud. Univ – Comput. Inf. Sci, vol.33, no.5, Pp.542-551, 2021.
[32] Mohandass, G., Ananda Natarajan, R., Hari Krishnan, G, “Comparative Analysis of Optical Coherence Tomography Retinal Image Using Multidimensional and Cluster Methods,” Biomed Res (India), vol.26, no.2, Pp.273-285, 2015.
[33] Alcin, O.F, Siuly, S, Bajaj, V, Guo, Y, Sengu, A., Zhang, Y, “Multi-Category EEG Signal Classification Developing Time-Frequency Texture Features Based Fisher Vector Encoding Method,” Neuro Computing, vol.218, Pp.251-258, 2016.
[34] Li, Y., Cui, W., Luo, M., Li, K., Wang, L, “Epileptic Seizure Detection Based on Time-Frequency Images of EEG Signals Using Gaussian Mixture Model and Gray Level Co-Occurrence Matrix Features,” Int J Neural Syst, vol.28, no.2, Pp.1850003, 2018.
[35] Sengur, A., Guo, Y., Akbulut, Y, “Time–Frequency Texture Descriptors of EEG Signals for Efficient Detection of Epileptic Seizure,” Brain Inf, vol.3, Pp.101-108, 2016.
[36] Gupta, R., Bera, J.N., Mitra, M, “Development of an Embedded System and MATLAB-Based GUI for Online Acquisition and Analysis of ECG Signal, Measurement,” vol.43, no.9, Pp.1119-1126, 2010.
[37] Krishnan, G.H, Natarajan, R.A., Nanda, A, “Comparative Study of Rheumatoid Arthritis Diagnosis Using Two Different Methods,” Biomed Pharmacol J, vol.7, no.4, Pp.379–382, 2014.
[38] John, A.A., Subramanian, A.P., Jaganathan, S.K., Sethuraman, B, “Evaluation of Cardiac Signals Using Discrete Wavelet Transform With MATLAB Graphical User Interface,” Indian Heart J,vol.67, no.6, Pp.549-551, 2015.
[39] Priyadarsini, S., Mohanty, S., Mukherjee, S., Basu, S., Mishra, M, “ Graphene and Graphene Oxide As Nanomaterials for Medicine and Biology Application,” J Nanostructure Chem, vol.8, Pp.123-137, 2018.
[40] T. Sudhakar, G. Hari Krishnan, G Umashankar, Sindudivakaran, U. Bhurnima and B. Shanchita, “Drug Retrieving System in Hospitals Using Robotics,” Biomed Pharmacol J, vol.3, no.3, 2020. 1239-1244.