Optimal Time Window Selection in the Wavelet Signal Domain for Brain–Computer Interfaces in Wheelchair Steering Control
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Abstract
Background and objective: Principally, the procedure of pattern recognition in terms of segmentation plays a significant role in a BCI-based wheelchair control system for avoiding recognition errors, which can lead to the initiation of the wrong command that will put the user in unsafe situations. Arguably, each subject might have different motor-imagery signal powers at different times in the trial because he or she could start (or end) performing the motor-imagery task at slightly different time intervals due to differences in the complexities his or her brain. Therefore, the primary goal of this research is to develop a generic pattern recognition model (GPRM)-based EEG-MI brain-computer interface for wheelchair steering control. Additionally, having a simplified and well generalized pattern recognition model is essential for EEG-MI based BCI applications. Methods: Initially, bandpass filtering and segmentation using multiple time windows were used for denoising the EEG-MI signal and finding the best duration that contains the MI feature components. Then, feature extraction was performed using five statistical features, namely the minimum, maximum, mean, median, and standard deviation, were used for extracting the MI feature components from the wavelet coefficient. Then, seven machine learning methods were adopted and evaluated to find the best classifiers. Results: The results of the study showed that, the best durations in the time-frequency domain were in the range of (4-7 s). Interestingly, the GPRM model based on the LR classifier was highly accurate, and achieved an impressive classification accuracy of 85.7%.
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References
P. J. García-Laencina, G. Rodríguez-Bermudez, and J. Roca-Dorda, "Exploring dimensionality reduction of EEG features in motor imagery task classification," Expert Systems with Applications, vol. 41, no. 11, pp. 5285-5295, 2014.
Z. T. Al-qaysi, B. B. Zaidan, A. A. Zaidan, and M. S. Suzani, "A review of disability EEG based wheelchair control system: Coherent taxonomy, open challenges and recommendations," Computer Methods and Programs in Biomedicine, vol. 164, pp. 221-237, 2018/10/01/ 2018, doi: https://doi.org/10.1016/j.cmpb.2018.06.012.
S. Garfan et al., "Telehealth utilization during the Covid-19 pandemic: A systematic review," Computers in Biology and Medicine, vol. 138, p. 104878, 2021/11/01/ 2021, doi: https://doi.org/10.1016/j.compbiomed.2021.104878.
Z. Al-Qaysi et al., "Systematic review of training environments with motor imagery brain–computer interface: coherent taxonomy, open issues and recommendation pathway solution," Health and Technology, vol. 11, no. 4, pp. 783-801, 2021.
M. H. Jasim et al., "Emotion detection among Muslims and non-Muslims while listening to Quran recitation using EEG," Int J Acad Res Bus Soc Sci, vol. 9, p. 14, 2019.
R. D. Ismail, Q. A. Hameed, and M. B. Omar, "An EEG based Physiological Signal for Driver Behavior Monitoring Systems: A Review," Tikrit Journal for Computer Science and Mathematics, vol. 1, no. 1, pp. 38-54, 2023.
A. H. Alamoodi et al., "Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review," Expert systems with applications, vol. 167, p. 114155, 2021.
M. A. Ahmed et al., "Real-time sign language framework based on wearable device: analysis of MSL, DataGlove, and gesture recognition," Soft Computing, vol. 25, no. 16, pp. 11101-11122, 2021/08/01 2021, doi: 10.1007/s00500-021-05855-6.
J. Li, J. Liang, Q. Zhao, J. Li, K. Hong, and L. Zhang, "Design of assistive wheelchair system directly steered by human thoughts," International journal of neural systems, vol. 23, no. 03, p. 1350013, 2013.
Z. T. Al-Qaysi et al., "A systematic rank of smart training environment applications with motor imagery brain-computer interface," Multimedia Tools and Applications, vol. 82, no. 12, pp. 17905-17927, 2023/05/01 2023, doi: 10.1007/s11042-022-14118-x.
S. Mohamad Samuri, T. V. Nova, B. Rahmatullah, S. L. Wang, and Z. T. Al-Qaysi, "CLASSIFICATION MODEL FOR BREAST CANCER MAMMOGRAMS," IIUM Engineering Journal, vol. 23, no. 1, pp. 187 - 199, 01/04 2022, doi: 10.31436/iiumej.v23i1.1825.
R. Liu, Z. Zhang, F. Duan, X. Zhou, and Z. Meng, "Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms," Computational intelligence and neuroscience, vol. 2017, 2017.
M. A. Ahmed, B. B. Zaidan, A. A. Zaidan, M. M. Salih, Z. T. Al-qaysi, and A. H. Alamoodi, "Based on wearable sensory device in 3D-printed humanoid: A new real-time sign language recognition system," Measurement, vol. 168, p. 108431, 2021/01/15/ 2021, doi: https://doi.org/10.1016/j.measurement.2020.108431.
Z. Mu, D. Xiao, and J. Hu, "Classification of Motor Imagery EEG Signals Based on Time Frequency Analysis," International Journal of Digital Content Technology and its Applications, vol. 3, no. 4, pp. 116-119, 2009.
A. Ferreira, T. F. Bastos Filho, M. Sarcinelli Filho, J. L. M. Sanchez, J. C. G. García, and M. M. Quintas, "Evaluation of PSD Components and AAR Parameters as Input Features for a SVM Classifier Applied to a Robotic Wheelchair," in BIODEVICES, 2009, pp. 7-12.
R. Chai, S. H. Ling, G. P. Hunter, Y. Tran, and H. T. Nguyen, "Brain–computer interface classifier for wheelchair commands using neural network with fuzzy particle swarm optimization," IEEE journal of biomedical and health informatics, vol. 18, no. 5, pp. 1614-1624, 2013.
M. J. Ferdous and M. S. Ali, "Time-Frequency Analysis of EEG Signal processing for Artifact Detection."
C. H. Nguyen and P. Artemiadis, "EEG feature descriptors and discriminant analysis under Riemannian Manifold perspective," Neurocomputing, vol. 275, pp. 1871-1883, 2018.
S. Siuly and Y. Li, "Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain–computer interface," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 20, no. 4, pp. 526-538, 2012.
R. Ebrahimpour, K. Babakhani, and M. Mohammad-Noori, "EEG-based motor imagery classification using wavelet coefficients and ensemble classifiers," in The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012), 2012: IEEE, pp. 458-463.
P. Bhuvaneswari and J. S. Kumar, "Support vector machine technique for EEG signals," International Journal of Computer Applications, vol. 63, no. 13, 2013.
A. S. Albahri et al., "A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology," International Journal of Telemedicine and Applications, vol. 2023, p. 7741735, 2023/04/30 2023, doi: 10.1155/2023/7741735.
A. S. Albahri et al., "Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review," Journal of Medical Systems, vol. 44, no. 7, p. 122, 2020/05/25 2020, doi: 10.1007/s10916-020-01582-x.
Y. Zhang et al., "Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces," Expert Systems with Applications, vol. 96, pp. 302-310, 2018.
M. Hadid, Q. M. Hussein, Z. Al-Qaysi, M. Ahmed, and M. M. Salih, "An Overview of Content-Based Image Retrieval Methods And Techniques," Iraqi Journal For Computer Science and Mathematics, vol. 4, no. 3, pp. 66-78, 2023.
M. L. Shuwandy et al., "Sensor-based authentication in smartphone: A systematic review," Journal of Engineering Research, 2024/02/09/ 2024, doi: https://doi.org/10.1016/j.jer.2024.02.003.
T. L. Fan, C. Ng, J. Ng, and S. Goh, "A brain-computer interface with intelligent distributed controller for wheelchair," in 4th Kuala Lumpur International Conference on Biomedical Engineering 2008, 2008: Springer, pp. 641-644.
A. B. Benevides, T. F. Bastos, and M. Sarcinelli Filho, "Proposal of Brain-Computer Interface architecture to command a robotic wheelchair," in Industrial Electronics (ISIE), 2011 IEEE International Symposium on, 2011: IEEE, pp. 2249-2254.
M. Carra and A. Balbinot, "Evaluation of sensorimotor rhythms to control a wheelchair," in Biosignals and Biorobotics Conference (BRC), 2013 ISSNIP, 2013: IEEE, pp. 1-4.
K.-T. Kim, T. Carlson, and S.-W. Lee, "Design of a robotic wheelchair with a motor imagery based brain-computer interface," in Brain-Computer Interface (BCI), 2013 International Winter Workshop on, 2013: IEEE, pp. 46-48.
F. Galán et al., "A brain-actuated wheelchair: asynchronous and non-invasive brain–computer interfaces for continuous control of robots," Clinical neurophysiology, vol. 119, no. 9, pp. 2159-2169, 2008.
G. Gentiletti, J. Gebhart, R. Acevedo, O. Yáñez-Suárez, and V. Medina-Bañuelos, "Command of a simulated wheelchair on a virtual environment using a brain-computer interface," Irbm, vol. 30, no. 5-6, pp. 218-225, 2009.
I. Iturrate, J. M. Antelis, A. Kubler, and J. Minguez, "A noninvasive brain-actuated wheelchair based on a P300 neurophysiological protocol and automated navigation," IEEE transactions on robotics, vol. 25, no. 3, pp. 614-627, 2009.
K.-T. Kim and S.-W. Lee, "Towards an EEG-based intelligent wheelchair driving system with vibro-tactile stimuli," in Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on, 2016: IEEE, pp. 002382-002385.
K.-T. Kim, H.-I. Suk, and S.-W. Lee, "Commanding a brain-controlled wheelchair using steady-state somatosensory evoked potentials," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 3, pp. 654-665, 2018.
I. Iturrate, J. Antelis, and J. Minguez, "Synchronous EEG brain-actuated wheelchair with automated navigation," in Robotics and Automation, 2009. ICRA'09. IEEE International Conference on, 2009: IEEE, pp. 2318-2325.
T. Kaufmann, A. Herweg, and A. Kübler, "Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials," Journal of neuroengineering and rehabilitation, vol. 11, no. 1, p. 7, 2014.
J. Li et al., "Evaluation and application of a hybrid brain computer interface for real wheelchair parallel control with multi-degree of freedom," International journal of neural systems, vol. 24, no. 04, p. 1450014, 2014.
Y. Li, J. Pan, F. Wang, and Z. Yu, "A hybrid BCI system combining P300 and SSVEP and its application to wheelchair control," IEEE Transactions on Biomedical Engineering, vol. 60, no. 11, pp. 3156-3166, 2013.
F. Aziz, H. Arof, N. Mokhtar, and M. Mubin, "HMM based automated wheelchair navigation using EOG traces in EEG," Journal of neural engineering, vol. 11, no. 5, p. 056018, 2014.
K. Choi, "Control of a vehicle with EEG signals in real-time and system evaluation," European journal of applied physiology, vol. 112, no. 2, pp. 755-766, 2012.
H. Wang, Y. Li, J. Long, T. Yu, and Z. Gu, "An asynchronous wheelchair control by hybrid EEG–EOG brain–computer interface," Cognitive neurodynamics, vol. 8, no. 5, pp. 399-409, 2014.
I. H. Parmonangan, J. Santoso, W. Budiharto, and A. A. S. Gunawan, "Fast brain control systems for electric wheelchair using support vector machine," in First International Workshop on Pattern Recognition, 2016, vol. 10011: International Society for Optics and Photonics, p. 100111N.
Z. Li, S. Lei, C.-Y. Su, and G. Li, "Hybrid brain/muscle-actuated control of an intelligent wheelchair," in Robotics and Biomimetics (ROBIO), 2013 IEEE International Conference on, 2013: IEEE, pp. 19-25.
K. Choi and A. Cichocki, "Control of a wheelchair by motor imagery in real time," in International Conference on Intelligent Data Engineering and Automated Learning, 2008: Springer, pp. 330-337.
K. Kaneswaran, K. Arshak, E. Burke, and J. Condron, "Towards a brain controlled assistive technology for powered mobility," in Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, 2010: IEEE, pp. 4176-4180.
B.-G. Shin, T. Kim, and S. Jo, "Non-invasive brain signal interface for a wheelchair navigation," in Int. Conf. on Control Automation and Systems, 2010.
G. Reshmi and A. Amal, "Design of a BCI system for piloting a wheelchair using five class MI Based EEG," in 2013 Third International Conference on Advances in Computing and Communications (ICACC), 2013: IEEE, pp. 25-28.
S. He et al., "A p300-based threshold-free brain switch and its application in wheelchair control," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 6, pp. 715-725, 2017.
L. Cao, J. Li, H. Ji, and C. Jiang, "A hybrid brain computer interface system based on the neurophysiological protocol and brain-actuated switch for wheelchair control," Journal of neuroscience methods, vol. 229, pp. 33-43, 2014.
H. A. Lamti, P. Gorce, M. M. Ben Khelifa, and A. M. Alimi, "When mental fatigue maybe characterized by Event Related Potential (P300) during virtual wheelchair navigation," Computer methods in biomechanics and biomedical engineering, vol. 19, no. 16, pp. 1749-1759, 2016.
B. M. Faria, L. P. Reis, and N. Lau, "Cerebral palsy eeg signals classification: Facial expressions and thoughts for driving an intelligent wheelchair," in Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on, 2012: IEEE, pp. 33-40.
Z. Bahri, S. Abdulaal, and M. Buallay, "Sub-band-power-based efficient brain computer interface for wheelchair control," in Computer Applications & Research (WSCAR), 2014 World Symposium on, 2014: IEEE, pp. 1-7.
R. H. Abiyev, N. Akkaya, E. Aytac, I. Günsel, and A. Çağman, "Brain-Computer Interface for Control of Wheelchair Using Fuzzy Neural Networks," BioMed research international, vol. 2016, 2016.
M. E. Abdalsalam, M. Z. Yusoff, N. Kamel, A. Malik, and M. Meselhy, "Mental task motor imagery classifications for noninvasive brain computer interface," in Intelligent and Advanced Systems (ICIAS), 2014 5th International Conference on, 2014: IEEE, pp. 1-5.
H. T. Nguyen, N. Trung, V. Toi, and V.-S. Tran, "An autoregressive neural network for recognition of eye commands in an EEG-controlled wheelchair," in Advanced Technologies for Communications (ATC), 2013 International Conference on, 2013: IEEE, pp. 333-338.
A. Ferreira, D. C. Cavalieri, R. L. Silva, T. F. Bastos Filho, and M. Sarcinelli Filho, "A Versatile Robotic Wheelchair Commanded by Brain Signals or Eye Blinks," in BIODEVICES (2), 2008, pp. 62-67.
T. A. Izzuddin, M. Ariffin, Z. H. Bohari, R. Ghazali, and M. H. Jali, "Movement intention detection using neural network for quadriplegic assistive machine," in Control System, Computing and Engineering (ICCSCE), 2015 IEEE International Conference on, 2015: IEEE, pp. 275-280.
W. Caesarendra, M. Ariyanto, S. U. Lexon, E. D. Pasmanasari, C. R. Chang, and J. D. Setiawan, "EEG based pattern recognition method for classification of different mental tasking: Preliminary study for stroke survivors in Indonesia," in Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT), 2015 International Conference on, 2015: IEEE, pp. 138-144.
C. Naijian, H. Xiangdong, W. Yantao, C. Xinglai, and C. Hui, "Coordination control strategy between human vision and wheelchair manipulator based on BCI," in Industrial Electronics and Applications (ICIEA), 2016 IEEE 11th Conference on, 2016: IEEE, pp. 1872-1875.
L. R. Borges, F. R. Martins, E. L. Naves, T. F. Bastos, and V. F. Lucena, "Multimodal system for training at distance in a virtual or augmented reality environment for users of electric-powered wheelchairs," IFAC-PapersOnLine, vol. 49, no. 30, pp. 156-160, 2016.
R.-M. Hjørungdal, F. Sanfilippo, O. Osen, A. Rutle, and R. T. Bye, "A game-based learning framework for controlling brain-actuated wheelchairs," in 30th European Conference on Modelling and Simulation, Regensburg Germany, May 31st–June 3rd, 2016, 2016: ECMS European Council for Modelling and Simulation.
G. Pires and U. Nunes, "A Brain Computer Interface methodology based on a visual P300 paradigm," in Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on, 2009: IEEE, pp. 4193-4198.
A. C. Lopes, G. Pires, and U. Nunes, "Assisted navigation for a brain-actuated intelligent wheelchair," Robotics and Autonomous Systems, vol. 61, no. 3, pp. 245-258, 2013.
S. T. Müller, W. C. Celeste, T. F. Bastos-Filho, and M. Sarcinelli-Filho, "Brain-computer interface based on visual evoked potentials to command autonomous robotic wheelchair," Journal of Medical and Biological Engineering, vol. 30, no. 6, pp. 407-415, 2010.
T. Bastos-Filho, A. Ferreira, D. Cavalieri, R. Silva, S. Muller, and E. Pérez, "Multi-modal interface for communication operated by eye blinks, eye movements, head movements, blowing/sucking and brain waves," in Biosignals and Biorobotics Conference (BRC), 2013 ISSNIP, 2013: IEEE, pp. 1-6.
T. F. Bastos, S. M. Muller, A. B. Benevides, and M. Sarcinelli-Filho, "Robotic wheelchair commanded by SSVEP, motor imagery and word generation," in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, 2011: IEEE, pp. 4753-4756.
S. M. T. Müller, T. F. Bastos-Filho, and M. Sarcinelli-Filho, "Using a SSVEP-BCI to command a robotic wheelchair," in Industrial Electronics (ISIE), 2011 IEEE International Symposium on, 2011: IEEE, pp. 957-962.
M. A. Ahmed et al., "Intelligent Decision-Making Framework for Evaluating and Benchmarking Hybridized Multi-Deep Transfer Learning Models: Managing COVID-19 and Beyond," International Journal of Information Technology & Decision Making, p. 2350046, 2023, doi: 10.1142/S0219622023500463.
A. Albahri et al., "A Trustworthy and Explainable Framework for Benchmarking Hybrid Deep Learning Models Based on Chest X-Ray Analysis in CAD Systems," International Journal of Information Technology and Decision Making, 2024.
A. A. Rasha, Z. T. Al-Qaysi, M.A.Ahmed, and M. S. Mahmood, "Hybrid Model for Motor Imagery Biometric Identification," Iraqi Journal For Computer Science and Mathematics, vol. 5, no. 1, pp. 1-12, 12/27 2023, doi: 10.52866/ijcsm.2024.05.01.001.
A. Al-Saegh and A. F. Hussein, "Wavelet-based Hybrid learning framework for motor imagery classification," 2022.
M. A. Ahmed, Z. T. Al-qaysi, M. L. Shuwandy, M. M. Salih, and M. H. Ali, "Automatic COVID-19 pneumonia diagnosis from x-ray lung image: A Deep Feature and Machine Learning Solution," Journal of Physics: Conference Series, vol. 1963, no. 1, p. 012099, 2021/07/01 2021, doi: 10.1088/1742-6596/1963/1/012099.
M. M. Salih, M. Ahmed, B. Al-Bander, K. F. Hasan, M. L. Shuwandy, and Z. Al-Qaysi, "Benchmarking Framework for COVID-19 Classification Machine Learning Method Based on Fuzzy Decision by Opinion Score Method," Iraqi Journal of Science, pp. 922-943, 2023.
Z. T. Al-qaysi, A. S. Albahri, M. A. Ahmed, and M. M. Salih, "Dynamic decision-making framework for benchmarking brain–computer interface applications: a fuzzy-weighted zero-inconsistency method for consistent weights and VIKOR for stable rank," Neural Computing and Applications, 2024/03/16 2024, doi: 10.1007/s00521-024-09605-1.
Z. T. Al-Qaysi, A. S. Albahri, M. A. Ahmed, and S. M. Mohammed, "Development of hybrid feature learner model integrating FDOSM for golden subject identification in motor imagery," Physical and Engineering Sciences in Medicine, vol. 46, no. 4, pp. 1519-1534, 2023/12/01 2023, doi: 10.1007/s13246-023-01316-6.
"Multi-Tiered CNN Model for Motor Imagery Analysis: Enhancing UAV Control in Smart City Infrastructure for Industry 5.0," Applied Data Science and Analysis, pp. 88-101, 2023, doi: 10.58496/adsa/2023/007.
M. D. Salman and R. Adel, "An intelligent attendance system based on convolutional neural networks for real-time student face identifications," Journal of Engineering Science and Technology, vol. 17, no. 5, pp. 3326-3341, 2022.
N. Saad Baqer, H. A. Mohammed, A. S. Albahri, A. A. Zaidan, Z. T. Al-qaysi, and O. S. Albahri, "Development of the Internet of Things sensory technology for ensuring proper indoor air quality in hospital facilities: Taxonomy analysis, challenges, motivations, open issues and recommended solution," Measurement, vol. 192, p. 110920, 2022/03/31/ 2022, doi: https://doi.org/10.1016/j.measurement.2022.110920.
R. A. Hamid et al., "How smart is e-tourism? A systematic review of smart tourism recommendation system applying data management," Computer Science Review, vol. 39, p. 100337, 2021/02/01/ 2021, doi: https://doi.org/10.1016/j.cosrev.2020.100337.
G. Ali and M. M. Mijwil, "Cybersecurity for Sustainable Smart Healthcare: State of the Art, Taxonomy, Mechanisms, and Essential Roles," Mesopotamian Journal of CyberSecurity, vol. 4, no. 2, pp. 20-62, 2024.
A. Saihood, M. A. Al-Shaher, and M. A. Fadhel, "A New Tiger Beetle Algorithm for Cybersecurity, Medical Image Segmentation and Other Global Problems Optimization," Mesopotamian Journal of CyberSecurity, vol. 4, no. 1, pp. 17-46, 2024.
P. Jahankhani, V. Kodogiannis, and K. Revett, "EEG signal classification using wavelet feature extraction and neural networks," in Modern Computing, 2006. JVA'06. IEEE John Vincent Atanasoff 2006 International Symposium on, 2006: IEEE, pp. 120-124.
A. F. Hussein et al., "Focal and non-focal epilepsy localization: A review," IEEE Access, vol. 6, pp. 49306-49324, 2018.