Generic pattern recognition models based on EEG-MI brain computer interfaces for wheelchair steering control

The purpose of this study was to develop generic pattern recognition models (GPRMs)based on two-class EEGMI brain-computer interfaces for wheelchair steering control.Initially, a preprocessing procedure was performed to remove unwanted signals and toidentify the optimal duration of MI feature compon...

Full description

Saved in:

Bibliographic Details
Main Author:	Al-Qaysi, Ziadoon Tareq AbdulWahhab
Format:	thesis
Language:	eng
Published:	2020
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	https://ir.upsi.edu.my/detailsg.php?det=6509
Tags:	Add Tag No Tags, Be the first to tag this record!

id	oai:ir.upsi.edu.my:6509
record_format	uketd_dc
institution	Universiti Pendidikan Sultan Idris
collection	UPSI Digital Repository
language	eng
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Al-Qaysi, Ziadoon Tareq AbdulWahhab Generic pattern recognition models based on EEG-MI brain computer interfaces for wheelchair steering control
description	The purpose of this study was to develop generic pattern recognition models (GPRMs)based on two-class EEGMI brain-computer interfaces for wheelchair steering control.Initially, a preprocessing procedure was performed to remove unwanted signals and toidentify the optimal duration of MI feature components. Then, feature extraction basedon five statistical features, namely min, max, mean, median, and standard deviation wereutilized for extracting the MI feature components in three signal domains, namely time,frequency, and time-frequency domains. Seven classification algorithms, namely LDA,SVM, KNN, ANN, NB, DT, and LR were selected and tested to find the best algorithmsthat could be used for the development of hybrid classifiers. Two datasets were used,namely the BCI Competition dataset (which belonged to Graz University) and theEmotive EPOC dataset (which was collected in this study), with the former beingutilized in the development, evaluation, and validation of the GPRM models and thelatter being used for validation only. The research findings showed that GPRM modelsbased on the LR classifier were highly accurate in the time and time-frequency domainsin the range of 4 and 6 seconds and 4 and 7 seconds, respectively. In addition, GPRMmodels based on the MLP-LR classifier were highly accurate in the frequency domainin the range of 4 and 6 seconds. Furthermore, the validation of such models using theEmotive EPOC dataset showed that the LR-based GPRM model attained highclassification accuracies of 90.2% and 85.7% in the time domain and time-frequencydomain, respectively. The MLP-LR-based GPRM models achieved a classificationaccuracy of 84.2% in the frequency domain. In conclusion, the main findings showedthat GPRMs were highly adaptable when deployed in the real-time application of theEEG-MI-based wheelchair steering control system. The implication of this study is thatgeneric pattern recognition models based on EEG-MI Brain-Computer interfaces can beutilized to improve the effectiveness of wheelchair steering control.
format	thesis
qualification_name
qualification_level	Doctorate
author	Al-Qaysi, Ziadoon Tareq AbdulWahhab
author_facet	Al-Qaysi, Ziadoon Tareq AbdulWahhab
author_sort	Al-Qaysi, Ziadoon Tareq AbdulWahhab
title	Generic pattern recognition models based on EEG-MI brain computer interfaces for wheelchair steering control
title_short	Generic pattern recognition models based on EEG-MI brain computer interfaces for wheelchair steering control
title_full	Generic pattern recognition models based on EEG-MI brain computer interfaces for wheelchair steering control
title_fullStr	Generic pattern recognition models based on EEG-MI brain computer interfaces for wheelchair steering control
title_full_unstemmed	Generic pattern recognition models based on EEG-MI brain computer interfaces for wheelchair steering control
title_sort	generic pattern recognition models based on eeg-mi brain computer interfaces for wheelchair steering control
granting_institution	Universiti Pendidikan Sultan Idris
granting_department	Fakulti Seni, Komputeran dan Industri Kreatif
publishDate	2020
url	https://ir.upsi.edu.my/detailsg.php?det=6509
_version_	1747833271428841472
spelling	oai:ir.upsi.edu.my:65092021-12-08 Generic pattern recognition models based on EEG-MI brain computer interfaces for wheelchair steering control 2020 Al-Qaysi, Ziadoon Tareq AbdulWahhab TK Electrical engineering. Electronics Nuclear engineering The purpose of this study was to develop generic pattern recognition models (GPRMs)based on two-class EEGMI brain-computer interfaces for wheelchair steering control.Initially, a preprocessing procedure was performed to remove unwanted signals and toidentify the optimal duration of MI feature components. Then, feature extraction basedon five statistical features, namely min, max, mean, median, and standard deviation wereutilized for extracting the MI feature components in three signal domains, namely time,frequency, and time-frequency domains. Seven classification algorithms, namely LDA,SVM, KNN, ANN, NB, DT, and LR were selected and tested to find the best algorithmsthat could be used for the development of hybrid classifiers. Two datasets were used,namely the BCI Competition dataset (which belonged to Graz University) and theEmotive EPOC dataset (which was collected in this study), with the former beingutilized in the development, evaluation, and validation of the GPRM models and thelatter being used for validation only. The research findings showed that GPRM modelsbased on the LR classifier were highly accurate in the time and time-frequency domainsin the range of 4 and 6 seconds and 4 and 7 seconds, respectively. In addition, GPRMmodels based on the MLP-LR classifier were highly accurate in the frequency domainin the range of 4 and 6 seconds. Furthermore, the validation of such models using theEmotive EPOC dataset showed that the LR-based GPRM model attained highclassification accuracies of 90.2% and 85.7% in the time domain and time-frequencydomain, respectively. The MLP-LR-based GPRM models achieved a classificationaccuracy of 84.2% in the frequency domain. In conclusion, the main findings showedthat GPRMs were highly adaptable when deployed in the real-time application of theEEG-MI-based wheelchair steering control system. The implication of this study is thatgeneric pattern recognition models based on EEG-MI Brain-Computer interfaces can beutilized to improve the effectiveness of wheelchair steering control. 2020 thesis https://ir.upsi.edu.my/detailsg.php?det=6509 https://ir.upsi.edu.my/detailsg.php?det=6509 text eng closedAccess Doctoral Universiti Pendidikan Sultan Idris Fakulti Seni, Komputeran dan Industri Kreatif Abdalsalam, M. E., Yusoff, M. Z., Kamel, N., Malik, A., & Meselhy, M. (2014).Mental task motor imagery classifications for noninvasive brain computer interface. Paperpresented at the Intelligent and Advanced Systems (ICIAS), 2014 5th International Conference on.Abiyev, R. H., Akkaya, N., Aytac, E., Gnsel, I., & a?man, A. (2016). Brain- ComputerInterface for Control of Wheelchair Using Fuzzy Neural Networks. BioMed research international,2016.Achic, F., Montero, J., Penaloza, C., & Cuellar, F. (2016). Hybrid BCI system tooperate an electric wheelchair and a robotic arm for navigation andmanipulation tasks. Paper presented at the Advanced Robotics and its Social Impacts (ARSO), 2016IEEE Workshop on.Adeli, H., Zhou, Z., & Dadmehr, N. (2003). Analysis of EEG records in an epileptic patient usingwavelet transform. Journal of neuroscience methods, 123(1), 69- 87.Al-Fahoum, A. S., & Al-Fraihat, A. A. (2014). Methods of EEG signal features extractionusing linear analysis in frequency and time-frequency domains. ISRN neuroscience, 2014.Amarasinghe, K., Wijayasekara, D., & Manic, M. (2014). EEG based brain activity monitoring usingArtificial Neural Networks. Paper presented at the 2014 7th International Conference on HumanSystem Interactions (HSI).Andronicus, S., Harjanto, N. C., & Widyotriatmo, A. (2015). Heuristic Steady State Visual EvokedPotential based Brain Computer Interface system for robotic wheelchair application. Paper presented at the Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), 2015 4th International Conference on.Ang, K. K., & Guan, C. (2017). EEG-based strategies to detect motor imagery forcontrol and rehabilitation. IEEE Transactions on Neural Systems and RehabilitationEngineering, 25(4), 392-401.Aydemir, O., & Kayikcioglu, T. (2014). Decision tree structure based classification of EEG signalsrecorded during two dimensional cursor movement imagery. Journal of neuroscience methods,229, 68-75.Azami, H., & Escudero, J. (2015). Combination of signal segmentation approaches using fuzzydecision making. Paper presented at the Engineering in Medicine and Biology Society (EMBC), 201537th Annual International Conference ofthe IEEE.Aziz, F., Arof, H., Mokhtar, N., & Mubin, M. (2014). HMM based automatedwheelchair navigation using EOG traces in EEG. Journal of neural engineering,11(5), 056018.Bahri, Z., Abdulaal, S., & Buallay, M. (2014). Sub-band-power-based efficient brain computerinterface for wheelchair control. Paper presented at the Computer Applications & Research(WSCAR), 2014 World Symposium on.Baig, M. Z., Aslam, N., & Shum, H. P. (2019). Filtering techniques for channelselection in motor imagery EEG applications: a survey. Artificial intelligence review, 1-26.Bastos-Filho, T., Ferreira, A., Cavalieri, D., Silva, R., Muller, S., & Prez, E. (2013).Multi-modal interface for communication operated by eye blinks, eye movements, head movements, blowing/sucking and brain waves. Paper presented at the Biosignals andBiorobotics Conference (BRC), 2013 ISSNIP.Bastos, T. F., Muller, S. M., Benevides, A. B., & Sarcinelli-Filho, M. (2011). Robotic wheelchaircommanded by SSVEP, motor imagery and word generation. Paper presented at the Engineering inMedicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEEBatres-Mendoza, P., Ibarra-Manzano, M. A., Guerra-Hernandez, E. I., Almanza-Ojeda,D. L., Montoro-Sanjose, C. R., Romero-Troncoso, R. J., & Rostro-Gonzalez,H. (2017). Improving EEG-based motor imagery classification for real-time applications using the QSA method. Computational intelligence and neuroscience, 2017.Belkacem, A. N., Hirose, H., Yoshimura, N., Shin, D., & Koike, Y. (2014).Classification of four eye directions from EEG signals for eye-movement-based communicationsystems. life, 1, 3.Belwafi, K., Djemal, R., Ghaffari, F., & Romain, O. (2014). An adaptive EEG filtering approach tomaximize the classification accuracy in motor imagery. Paper presented at the ComputationalIntelligence, Cognitive Algorithms, Mind, and Brain (CCMB), 2014 IEEE Symposium on.Benevides, A. B., Bastos, T. F., & Sarcinelli Filho, M. (2011). Proposal of Brain-Computer Interface architecture to command a robotic wheelchair. Paper presented at the Industrial Electronics (ISIE), 2011 IEEE International Symposium on.Bhuvaneswari, P., & Kumar, J. S. (2013). Support vector machine technique for EEG signals.International Journal of Computer Applications, 63(13).Borges, L. R., Martins, F. R., Naves, E. L., Bastos, T. F., & Lucena, V. F. (2016). Multimodalsystem for training at distance in a virtual or augmented reality environment for users ofelectric-powered wheelchairs. IFAC-PapersOnLine,49(30), 156-160.Budiharto, W., Gunawan, A. A. S., Parmonangan, I. H., & Santoso, J. Fast BrainControl Systems for Electric Wheelchair using Support Vector Machine.Caesarendra, W., Ariyanto, M., Lexon, S. U., Pasmanasari, E. D., Chang, C. R., & Setiawan, J. D. (2015). EEG based pattern recognition method for classification of differentmental tasking: Preliminary study for stroke survivors in Indonesia. Paper presented at theAutomation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT), 2015 International Conference onCao, L., Li, J., Ji, H., & Jiang, C. (2014). A hybrid brain computer interface system based on the neurophysiological protocol and brain-actuated switch for wheelchair control.Journal of neuroscience methods, 229, 33-43.Carlson, T., Leeb, R., Chavarriaga, R., & Milln, J. d. R. (2012). The birth of the brain-controlled wheelchair. Paper presented at the Intelligent Robots and Systems (IROS), 2012 IEEE/RSJInternational Conference on.Carlson, T., & Millan, J. d. R. (2013). Brain-controlled wheelchairs: a roboticarchitecture. IEEE Robotics & Automation Magazine, 20(1), 65-73.Carra, M., & Balbinot, A. (2013a). Evaluation of sensorimotor rhythms to control a wheelchair.Paper presented at the 2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals andRobotics for Better and Safer Living (BRC).Carra, M., & Balbinot, A. (2013b). Evaluation of sensorimotor rhythms to control a wheelchair.Paper presented at the Biosignals and Biorobotics Conference (BRC), 2013 ISSNIP.Carrera-Leon, O., Ramirez, J. M., Alarcon-Aquino, V., Baker, M., D'Croz-Baron, D., & Gomez-Gil,P. (2012). A motor imagery BCI experiment using wavelet analysis and spatial patternsfeature extraction. Paper presented at the 2012 Workshop on Engineering Applications.Cebolla, A.-M., Palmero-Soler, E., Leroy, A., & Cheron, G. (2017). EEG spectralgenerators involved in motor imagery: a swLORETA study. Frontiers in psychology, 8, 2133.Chai, R., Ling, S. H., Hunter, G. P., & Nguyen, H. T. (2012a). Mental non-motorimagery tasks classifications of brain computer interface for wheelchair commands usinggenetic algorithm-based neural network. Paper presented at the Proceedings of the International Joint Conference on NeuralNetworks,(IJCNN), Brisbane, Queensland, Australia, 10-15 June 2012.Chai, R., Ling, S. H., Hunter, G. P., & Nguyen, H. T. (2012b). Toward fewer EEGchannels and better feature extractor of non-motor imagery mental tasks classificationfor a wheelchair thought controller. Paper presented at the Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE.Chai, R., Ling, S. H., Hunter, G. P., Tran, Y., & Nguyen, H. T. (2014). Braincomputer interfaceclassifier for wheelchair commands using neural network with fuzzy particle swarm optimization. IEEE journal of biomedical and health informatics, 18(5), 1614-1624.Chandani, M., & Kumar, A. (2017). Classification of EEG Physiological Signal for the Detection ofEpileptic Seizure by Using DWT Feature Extraction and Neural Network. International Journal ofNeurologic Physsical Therapy, 3, 38-43.Choi, K. (2012). Control of a vehicle with EEG signals in real-time and systemevaluation. European journal of applied physiology, 112(2), 755-766.Choi, K., & Cichocki, A. (2008). Control of a wheelchair by motor imagery in real time. Paperpresented at the International Conference on Intelligent Data Engineering and AutomatedLearningCraig, D. A., & Nguyen, H. (2007). Adaptive EEG thought pattern classifier for advancedwheelchair control. Paper presented at the Engineering in Medicine and Biology Society, 2007. EMBS2007. 29th Annual International Conference of the IEEECvetkovic, D., beyli, E. D., & Cosic, I. (2008). Wavelet transform feature extraction from humanPPG, ECG, and EEG signal responses to ELF PEMF exposures: A pilot study. Digital signal processing,18(5), 861-874.Devi, M. A., Sharmila, R., & Saranya, V. (2014). Hybrid brain computer interface in wheelchairusing voice recognition sensors. Paper presented at the Computer Communication and Informatics(ICCCI), 2014 International Conference on.Diez, P. F., Mller, S. M. T., Mut, V. A., Laciar, E., Avila, E., Bastos-Filho, T. F., &Sarcinelli-Filho, M. (2013). Commanding a robotic wheelchair with a high- frequencysteady-state visual evoked potential based braincomputer interface. Medical engineering & physics,35(8), 1155-1164.Duan, J., Li, Z., Yang, C., & Xu, P. (2014). Shared control of a brain-actuatedintelligent wheelchair. Paper presented at the Intelligent Control and Automation(WCICA), 2014 11th World Congress on.Ebrahimpour, R., Babakhani, K., & Mohammad-Noori, M. (2012). EEG-based motor imageryclassification using wavelet coefficients and ensemble classifiers. Paper presented at theThe 16th CSI International Symposium on ArtificialIntelligence and Signal Processing (AISP 2012).Fan, T. L., Ng, C., Ng, J., & Goh, S. (2008). A brain-computer interface with intelligentdistributed controller for wheelchair. Paper presented at the 4th Kuala LumpurInternational Conference on Biomedical Engineering 2008.Faria, B. M., Reis, L. P., & Lau, N. (2012a). Cerebral palsy eeg signals classification:Facial expressions and thoughts for driving an intelligent wheelchair. Paperpresented at the Data Mining Workshops (ICDMW), 2012 IEEE 12thInternational Conference on.Faria, B. M., Reis, L. P., & Lau, N. (2012b). Cerebral palsy eeg signals classification:Facial expressions and thoughts for driving an intelligent wheelchair. Paperpresented at the 2012 IEEE 12th International Conference on Data MiningWorkshops.Fernndez-Rodrguez, ., Velasco-lvarez, F., & Ron-Angevin, R. (2016). Review ofreal brain-controlled wheelchairs. Journal of neural engineering, 13(6),061001.Ferreira, A., Bastos Filho, T. F., Sarcinelli Filho, M., Sanchez, J. L. M., Garca, J. C.G., & Quintas, M. M. (2009). Evaluation of PSD Components and AARParameters as Input Features for a SVM Classifier Applied to a RoboticWheelchair. Paper presented at the BIODEVICES.Ferreira, A., Cavalieri, D. C., Silva, R. L., Bastos Filho, T. F., & Sarcinelli Filho, M.(2008). A Versatile Robotic Wheelchair Commanded by Brain Signals or EyeBlinks. Paper presented at the BIODEVICES (2).Galn, F., Nuttin, M., Lew, E., Ferrez, P. W., Vanacker, G., Philips, J., & Milln, J. d.R. (2008). A brain-actuated wheelchair: asynchronous and non-invasive braincomputer interfaces for continuous control of robots. Clinical neurophysiology,119(9), 2159-2169.Gandhi, V., Prasad, G., Coyle, D., Behera, L., & McGinnity, T. M. (2014). EEG-basedmobile robot control through an adaptive brainrobot interface. IEEETransactions on Systems, Man, and Cybernetics: Systems, 44(9), 1278-1285.Gentiletti, G., Gebhart, J., Acevedo, R., Yez-Surez, O., & Medina-Bauelos, V.(2009). Command of a simulated wheelchair on a virtual environment using abrain-computer interface. Irbm, 30(5-6), 218-225.Hamad, A., Houssein, E. H., Hassanien, A. E., & Fahmy, A. A. (2016). Featureextraction of epilepsy EEG using discrete wavelet transform. Paper presentedat the 2016 12th international computer engineering conference (ICENCO).Hamzah, N., Norhazman, H., Zaini, N., & Sani, M. (2016). Classification of EEGsignals based on different motor movement using multi-layer Perceptronartificial neural network. J Biol Sci, 16(7), 265-271.Hasan, M. R., Ibrahimy, M. I., Motakabber, S., & Shahid, S. (2015). Classification of multichannelEEG signal by linear discriminant analysis Progress in Systems Engineering (pp. 279-282): Springer.He, S., Zhang, R., Wang, Q., Chen, Y., Yang, T., Feng, Z., . . . Li, Y. (2017). A p300-based threshold-free brain switch and its application in wheelchair control. IEEETransactions on Neural Systems and Rehabilitation Engineering, 25(6), 715-725.Hema, C. R., Paulraj, M., Yaacob, S., Adom, A., & Nagarajan, R. (2009). Single trial motor imageryclassification for a four state brain machine interface. Paper presented at the Signal Processing& Its Applications, 2009. CSPA 2009. 5th International Colloquium on.Hjrungdal, R.-M., Sanfilippo, F., Osen, O., Rutle, A., & Bye, R. T. (2016). A game- based learningframework for controlling brain-actuated wheelchairs. Paper presented at the 30th EuropeanConference on Modelling and Simulation, Regensburg Germany, May 31stJune 3rd, 2016.Hossain, A. A., Rahman, M. W., & Riheen, M. A. (2015). Left and Right Hand MovementsEEG Signals Classification Using Wavelet Transform and Probabilistic Neural Network. International Journal of Electrical and Computer Engineering (IJECE), 5(1),92-101.Huang, D., Qian, K., Fei, D.-Y., Jia, W., Chen, X., & Bai, O. (2012).Electroencephalography (EEG)-based braincomputer interface (BCI): A 2-D virtual wheelchair control based on event-relateddesynchronization/synchronization and state control. IEEE Transactions on Neural Systems andRehabilitation Engineering, 20(3), 379-388.Hurtado-Rincon, J., Rojas-Jaramillo, S., Ricardo-Cespedes, Y., Alvarez-Meza, A. M., &Castellanos-Domnguez, G. (2014). Motor imagery classification using feature relevanceanalysis: An Emotiv-based BCI system. Paper presented at the 2014 XIX Symposium on Image, SignalProcessing and Artificial Vision.Hussein, A. F., Arunkumar, N., Gomes, C., Alzubaidi, A. K., Habash, Q. A.,Santamaria-Granados, L., . . . Ramirez-Gonzalez, G. (2018). Focal and non- focal epilepsylocalization: A review. IEEE Access, 6, 49306-49324.Instruments, T. (2013). FFT Implementation on the TMS320VC5505, TMS320C5505,and TMS320C5515 DSPs. Tech. Rep.Iturrate, I., Antelis, J., & Minguez, J. (2009). Synchronous EEG brain-actuatedwheelchair with automated navigation. Paper presented at the Robotics and Automation,2009. ICRA'09. IEEE International Conference on.Iturrate, I., Antelis, J. M., Kubler, A., & Minguez, J. (2009). A noninvasive brain-actuated wheelchair based on a P300 neurophysiological protocol andvigation. IEEE transactions on robotics, 25(3), 614-627.Izzuddin, T. A., Ariffin, M., Bohari, Z. H., Ghazali, R., & Jali, M. H. (2015). Movementintention detection using neural network for quadriplegic assistive machine.Paper presented at the Control System, Computing and Engineering (ICCSCE),2015 IEEE International Conference on.Jahankhani, P., Kodogiannis, V., & Revett, K. (2006). EEG signal classification usingwavelet feature extraction and neural networks. Paper presented at the ModernComputing, 2006. JVA'06. IEEE John Vincent Atanasoff 2006 InternationalSymposium on.Jayabhavani, G., Raajan, N., & Rubini, R. (2013). Brain mobile interfacing (BMI)system embedded with wheelchair. Paper presented at the Information &Communication Technologies (ICT), 2013 IEEE Conference on.Jeyabalan, V., Samraj, A., & Kiong, L. C. (2009). Classification of motor imaginarysignals for machine commmunication-a novel approach for brain machineinterface design. Paper presented at the 2009 International Conference onSignal Acquisition and Processing.Jia, W., Huang, D., Bai, O., Pu, H., Luo, X., & Chen, X. (2012). Reliable planning andexecution of a human-robot cooperative system based on noninvasive braincomputerinterface with uncertainty. Paper presented at the Intelligent Robotsand Systems (IROS), 2012 IEEE/RSJ International Conference on.Jiang, L., Tham, E., Yeo, M., & Phu, O. G. (2012). iPhone-based portable brain controlwheelchair. Paper presented at the Industrial Electronics and Applications(ICIEA), 2012 7th IEEE Conference on.Jiang, L., Tham, E., Yeo, M., Wang, Z., & Jiang, B. (2014). Motor imagery controlledwheelchair system. Paper presented at the Industrial Electronics andApplications (ICIEA), 2014 IEEE 9th Conference on.Kaneswaran, K., Arshak, K., Burke, E., & Condron, J. (2010). Towards a braincontrolled assistive technology for powered mobility. Paper presented at theEngineering in Medicine and Biology Society (EMBC), 2010 AnnualInternational Conference of the IEEE.Kaufmann, T., Herweg, A., & Kbler, A. (2014). Toward brain-computer interfacebased wheelchair control utilizing tactually-evoked event-related potentials.Journal of neuroengineering and rehabilitation, 11(1), 7.Kaysa, W. A., & Widyotriatmo, A. (2013). Design of Brain-computer interfaceplatform for semi real-time commanding electrical wheelchair simulatormovement. Paper presented at the Instrumentation Control and Automation(ICA), 2013 3rd International Conference on.Kim, K.-T., Carlson, T., & Lee, S.-W. (2013a). Design of a robotic wheelchair with amotor imagery based brain-computer interface. Paper presented at the 2013International Winter Workshop on Brain-Computer Interface (BCI).Kim, K.-T., Carlson, T., & Lee, S.-W. (2013b). Design of a robotic wheelchair with amotor imagery based brain-computer interface. Paper presented at the Brain- Computer Interface(BCI), 2013 International Winter Workshop on.Kim, K.-T., & Lee, S.-W. (2016). Towards an EEG-based intelligent wheelchair drivingsystem with vibro-tactile stimuli. Paper presented at the Systems, Man, and Cybernetics (SMC), 2016IEEE International Conference on.Kim, K.-T., Suk, H.-I., & Lee, S.-W. (2016). Commanding a brain-controlled wheelchair using steady-state somatosensory evoked potentials. IEEE Transactions on Neural Systemsand Rehabilitation Engineering.Kim, K.-T., Suk, H.-I., & Lee, S.-W. (2018). Commanding a brain-controlled wheelchair using steady-state somatosensory evoked potentials. IEEE Transactions on Neural Systemsand Rehabilitation Engineering, 26(3), 654- 665.Kim, Y., Ryu, J., Kim, K. K., Took, C. C., Mandic, D. P., & Park, C. (2016). Motor imageryclassification using mu and beta rhythms of EEG with strong uncorrelating transform based complex common spatial patterns. Computational intelligence andneuroscience, 2016, 1.Kodi, A., Kumar, D., Kodali, D., & Pasha, I. (2013). EEG-controlled Wheelchair for ALS Patients.Paper presented at the Communication Systems and Network Technologies (CSNT), 2013International Conference on.Koepsell, K., Wang, X., Hirsch, J., & Sommer, F. T. (2010). Exploring the function of neuraloscillations in early sensory systems. Frontiers in neuroscience, 3, 10.Lamti, H. A., Gorce, P., Ben Khelifa, M. M., & Alimi, A. M. (2016). When mental fatigue maybecharacterized by Event Related Potential (P300) during virtual wheelchair navigation. Computermethods in biomechanics and biomedical engineering, 19(16), 1749-1759.Lamti, H. A., Khelifa, M. M. B., Gorce, P., & Alimi, A. M. (2012). The command of awheelchair using thoughts and gaze. Paper presented at the Electrotechnical Conference (MELECON),2012 16th IEEE Mediterranean.Lamti, H. A., Khelifa, M. M. B., Gorce, P., & Alimi, A. M. (2013). The use of brain and thought inservice of handicap assistance: Wheelchair navigation. Paper presented at the Individual andCollective Behaviors in Robotics (ICBR), 2013 International Conference on.Li, J., Ji, H., Cao, L., Zang, D., Gu, R., Xia, B., & Wu, Q. (2014). Evaluation and application ofa hybrid brain computer interface for real wheelchair parallel control with multi-degree offreedom. International journal of neural systems,24(04), 1450014.Li, J., Liang, J., Zhao, Q., Li, J., Hong, K., & Zhang, L. (2013). Design of assistivewheelchair system directly steered by human thoughts. International journal of neural systems,23(03), 1350013.Li, M., Xu, H., Liu, X., & Lu, S. (2018). Emotion recognition from multichannel EEG signals using K-nearest neighbor classification. Technology and Health Care(Preprint), 1-11.Li, M., Zhang, Y., Zhang, H., & Hu, H. S. (2013). An EEG based control system for intelligent wheelchair. Paper presented at the Applied Mechanics and Materials.Li, Y., Kambara, H., Koike, Y., & Sugiyama, M. (2010). Application of covariate shift adaptationtechniques in braincomputer interfaces. IEEE Transactions on Biomedical Engineering, 57(6),1318-1324.Li, Y., Pan, J., Wang, F., & Yu, Z. (2013). A hybrid BCI system combining P300 and SSVEP and itsapplication to wheelchair control. IEEE Transactions on Biomedical Engineering, 60(11),3156-3166.Li, Z., Lei, S., Su, C.-Y., & Li, G. (2013). Hybrid brain/muscle-actuated control of an intelligentwheelchair. Paper presented at the Robotics and Biomimetics (ROBIO), 2013 IEEEInternational Conference on.Li, Z., Zhao, S., Duan, J., Su, C.-Y., Yang, C., & Zhao, X. (2017). Human cooperative wheelchairwith brainmachine interaction based on shared control strategy. IEEE/ASME Transactions onMechatronics, 22(1), 185-195.Lin, J.-S., Chen, K.-C., & Yang, W.-C. (2010). EEG and eye-blinking signals through aBrain-Computer Interface based control for electric wheelchairs with wireless scheme. Paperpresented at the New Trends in Information Science and Service Science (NISS), 2010 4thInternational Conference on.Lin, J.-S., & Yang, W.-C. (2012). Wireless brain-computer interface for electricwheelchairs with EEG and eye-blinking signals. Int. J. Innov. Comput. Inf. Control, 8(9),6011-6024.Liu, R., Zhang, Z., Duan, F., Zhou, X., & Meng, Z. (2017). Identification ofAnisomerous Motor Imagery EEG Signals Based on Complex Algorithms. Computationalintelligence and neuroscience, 2017.Long, J., Li, Y., Wang, H., Yu, T., Pan, J., & Li, F. (2012). A hybrid brain computer interface tocontrol the direction and speed of a simulated or real wheelchair. IEEE Transactions on NeuralSystems and Rehabilitation Engineering, 20(5), 720-729.Lopes, A. C., Pires, G., & Nunes, U. (2012). Robchair: experiments evaluating brain-computer interface to steer a semi-autonomous wheelchair. Paper presented at the Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ InternationalConference on.Lopes, A. C., Pires, G., & Nunes, U. (2013). Assisted navigation for a brain-actuatedintelligent wheelchair. Robotics and Autonomous Systems, 61(3), 245-258.Ma, Y., Ding, X., She, Q., Luo, Z., Potter, T., & Zhang, Y. (2016). Classification of motor imageryEEG signals with support vector machines and particle swarm optimization. Computational andmathematical methods in medicine, 2016.Mandel, C., Lth, T., Laue, T., Rfer, T., Grser, A., & Krieg-Brckner, B. (2009). Navigating asmart wheelchair with a brain-computer interface interpreting steady-state visual evokedpotentials. Paper presented at the Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJInternational Conference on.Martinez-Leon, J.-A., Cano-Izquierdo, J.-M., & Ibarrola, J. (2016). Are low cost Brain ComputerInterface headsets ready for motor imagery applications? Expert Systems with Applications,49, 136-144.Milln, J. d. R., Galn, F., Vanhooydonck, D., Lew, E., Philips, J., & Nuttin, M. (2009).Asynchronous non-invasive brain-actuated control of an intelligent wheelchair. Paper presented atthe Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual InternationalConference of the IEEE.Mirza, I. A., Tripathy, A., Chopra, S., D'Sa, M., Rajagopalan, K., D'Souza, A., &Sharma, N. (2015). Mind-controlled wheelchair using an EEG headset and arduinomicrocontroller. Paper presented at the Technologies for Sustainable Development (ICTSD), 2015International Conference on.Mu, Z., Xiao, D., & Hu, J. (2009). Classification of Motor Imagery EEG Signals Based on Time Frequency Analysis. International Journal of Digital Content Technology and itsApplications, 3(4), 116-119.Mller, S. M. T., Bastos-Filho, T. F., & Sarcinelli-Filho, M. (2011). Using a SSVEP- BCI tocommand a robotic wheelchair. Paper presented at the Industrial Electronics (ISIE), 2011IEEE International Symposium on.Mller, S. T., Celeste, W. C., Bastos-Filho, T. F., & Sarcinelli-Filho, M. (2010). Brain- computerinterface based on visual evoked potentials to command autonomous robotic wheelchair. Journal ofMedical and Biological Engineering, 30(6), 407- 415.Mumtaz, W., Xia, L., Ali, S. S. A., Yasin, M. A. M., Hussain, M., & Malik, A. S. (2017). Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressivedisorder (MDD). Biomedical Signal Processing andControl, 31, 108-115.Naijian, C., Xiangdong, H., Yantao, W., Xinglai, C., & Hui, C. (2016a). Coordinationcontrol strategy between human vision and wheelchair manipulator based onBCI. Paper presented at the 2016 IEEE 11th Conference on IndustrialElectronics and Applications (ICIEA).Naijian, C., Xiangdong, H., Yantao, W., Xinglai, C., & Hui, C. (2016b). Coordinationcontrol strategy between human vision and wheelchair manipulator based onBCI. Paper presented at the Industrial Electronics and Applications (ICIEA),2016 IEEE 11th Conference on.Nandish, M., Stafford, M., Kumar, P., & Ahmed, F. (2012). Feature extraction andclassification of EEG signal using neural network based techniques.International Journal of Engineering and Innovative Technology (IJEIT), 2(4),1-5.Nanthini, B. S., & Santhi, B. (2017). Electroencephalogram signal classification forautomated epileptic seizure detection using genetic algorithm. Journal ofnatural science, biology, and medicine, 8(2), 159.Nguyen, C. H., & Artemiadis, P. (2018). EEG feature descriptors and discriminantanalysis under Riemannian Manifold perspective. Neurocomputing, 275, 1871-1883.Nguyen, H. T., Trung, N., Toi, V., & Tran, V.-S. (2013). An autoregressive neuralnetwork for recognition of eye commands in an EEG-controlled wheelchair.Paper presented at the Advanced Technologies for Communications (ATC),2013 International Conference on.Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012). Brain computer interfaces, a review.Sensors, 12(2), 1211-1279.Odziej, M. K., Majkowski, A., & Rak, R. J. (2010). A new method of feature extractionfrom EEG signal for brain computer interface design. Przegl DElektrotechniczny.Oikonomou, V. P., Georgiadis, K., Liaros, G., Nikolopoulos, S., & Kompatsiaris, I.(2017). A comparison study on EEG signal processing techniques using motorimagery EEG data. Paper presented at the 2017 IEEE 30th internationalsymposium on computer-based medical systems (CBMS).Parmonangan, I. H., Santoso, J., Budiharto, W., & Gunawan, A. A. S. (2016). Fastbrain control systems for electric wheelchair using support vector machine.Paper presented at the First International Workshop on Pattern Recognition.Perrin, X., Chavarriaga, R., Colas, F., Siegwart, R., & Milln, J. d. R. (2010). Braincoupledinteraction for semi-autonomous navigation of an assistive robot.Robotics and Autonomous Systems, 58(12), 1246-1255.Persson, I. (2017). Feature selection of EEG-signal data for cognitive load.Pires, G., & Nunes, U. (2009). A Brain Computer Interface methodology based on a visual P300paradigm. Paper presented at the Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJInternational Conference on.Princy, R., Thamarai, P., & Karthik, B. (2015). Denoising EEG signal using wavelet transform. International Journal of Advanced Research in Computer Engineering & Technology, 3.Puanhvuan, D., & Wongsawat, Y. (2012). Semi-automatic P300-based brain- controlledwheelchair. Paper presented at the Complex Medical Engineering (CME), 2012 ICME InternationalConference on.Qidwai, U., Hassan, E. M., Al Halabi, R. M., & Shakir, M. (2013). Device interface for people withmobility impairment. Paper presented at the GCC Conference and Exhibition (GCC), 2013 7th IEEE.Rabhi, Y., Mrabet, M., & Fnaiech, F. (2018). Intelligent control wheelchair using a new visualjoystick. Journal of healthcare engineering, 2018.Ramli, R., Arof, H., Ibrahim, F., Mokhtar, N., & Idris, M. Y. I. (2015). Using finite state machine and a hybrid of EEG signal and EOG artifacts for an asynchronouswheelchair navigation. Expert Systems with Applications, 42(5), 2451-2463.Reaz, M., Hussain, M., Ibrahimy, M., & Mohd-Yasin, F. (2007). EEG signal analysis andcharacterization for the aid of disabled people. WIT Trans. Biomed. Health, 12, 287-294.Rechy-Ramirez, E.-J., Hu, H., & McDonald-Maier, K. (2012). Head movements based control of anintelligent wheelchair in an indoor environment. Paper presented at the Robotics and Biomimetics (ROBIO), 2012 IEEE International Conference on.Redelico, F., Traversaro, F., Garca, M., Silva, W., Rosso, O., & Risk, M. (2017).Classification of normal and pre-ictal eeg signals using permutation entropies and a generalizedlinear model as a classifier. Entropy, 19(2), 72.Reshmi, G., & Amal, A. (2013). Design of a BCI system for piloting a wheelchair using five classMI Based EEG. Paper presented at the 2013 Third International Conference on Advances inComputing and Communications (ICACC).Rojas, D. A., Gngora, L. A., & Ramos, O. L. (2016). EEG signal analysis related to speech processthrough BCI device EMOTIV, FFT and statistical methods.ARPN Journal of Engineering and Applied Sciences, 3074-3080.Saha, S., Ahmed, K. I., & Mostafa, R. (2016). Unifying sensorimotor dynamics inmulticlass brain computer interface. Paper presented at the 2016 5thInternational Conference on Informatics, Electronics and Vision (ICIEV).Sa?abun, W. (2014). Processing and spectral analysis of the raw EEG signal from the MindWave.Przeglad Elektrotechniczny, 90(2), 169-174.Salvaris, M., & Haggard, P. (2014). Decoding intention at sensorimotor timescales.PloS one, 9(2), e85100.Shaker, M. M. (2006). EEG waves classifier using wavelet transform and Fouriertransform. brain, 2, 3.Shenoy, H. V., & Vinod, A. P. (2014). An iterative optimization technique for robust channelselection in motor imagery based Brain Computer Interface. Paper presented at the 2014IEEE International Conference on Systems, Man, and Cybernetics (SMC).Shin, B.-G., Kim, T., & Jo, S. (2010). Non-invasive brain signal interface for awheelchair navigation. Paper presented at the Int. Conf. on Control Automation and Systems.Shinde, N., & George, K. (2016). Brain-controlled driving aid for electric wheelchairs. Paperpresented at the Wearable and Implantable Body Sensor Networks (BSN), 2016 IEEE 13thInternational Conference on.Siddiqi, A., SEVINDIR, H. K., Yazici, C., Kutlu, A., & Aslan, Z. (2014). Spectral Analysis of EegSignals by using Wavelet and Harmonic Transforms. ?stanbul Ayd?n niversitesi Dergisi, 3(9), 1-20.Siuly, S., & Li, Y. (2012). Improving the separability of motor imagery EEG signals using a crosscorrelation-based least square support vector machine for brain computer interface. IEEETransactions on Neural Systems and Rehabilitation Engineering, 20(4), 526-538.Sivakami, A., & Devi, S. S. (2015). Analysis of EEG for motor imagery basedclassification of hand activities. International Journal of Biomedical Engineeringand Science, 2(3), 11-22.Subasi, A., & Ercelebi, E. (2005). Classification of EEG signals using neural network and logisticregression. Computer methods and programs in biomedicine, 78(2), 87-99.Sun, L., Feng, Z., Chen, B., & Lu, N. (2018). A contralateral channel guided model for EEG basedmotor imagery classification. Biomedical Signal Processing andControl, 41, 1-9.Su, Z., Xu, X., Ding, J., & Lu, W. (2016). Intelligent wheelchair control system basedon BCI and the image display of EEG. Paper presented at the Advanced InformationManagement, Communicates, Electronic and Automation Control Conference (IMCEC), 2016 IEEE.Swee, S. K., & You, L. Z. (2016). Fast fourier analysis and EEG classificationbrainwave controlled wheelchair. Paper presented at the Control Science and Systems Engineering(ICCSSE), 2016 2nd International Conference on.Swee, S. K., You, L. Z., & Kiang, K. T. (2016). Brainwave controlled electricalwheelchair. Paper presented at the MATEC Web of Conferences.Szuflitowska, B., & Or?owski, P. (2017). Comparison of the EEG Signal Classifiers LDA, NBC andGNBC Based on Time-Frequency Features. Pomiary Automatyka Robotyka, 21.Taher, F. B., Amor, N. B., & Jallouli, M. (2013). EEG control of an electric wheelchair for disabled persons. Paper presented at the Individual and Collective Behaviors inRobotics (ICBR), 2013 International Conference on.Taher, F. B., Amor, N. B., & Jallouli, M. (2015). A multimodal wheelchair control system based onEEG signals and Eye tracking fusion. Paper presented at the Innovations in Intelligent SysTems and Applications (INISTA), 2015 International Symposium on.Taher, F. B., Amor, N. B., & Jallouli, M. (2016). A self configured and hybrid fusion approach foran electric wheelchair control. Paper presented at the Intelligent Systems (IS), 2016 IEEE 8thInternational Conference on.Tangermann, M., Mller, K.-R., Aertsen, A., Birbaumer, N., Braun, C., Brunner, C., .. . Mueller-Putz, G. (2012). Review of the BCI competition IV. Frontiers in neuroscience, 6, 55.Tello, R. J., Bissoli, A. L., Ferrara, F., Mller, S., Ferreira, A., & Bastos-Filho, T. F. (2015).Development of a human machine interface for control of robotic wheelchair and smartenvironment. IFAC-PapersOnLine, 48(19), 136-141.Tomari, R., Hassan, R. R. A., Zakaria, W. N. W., & Ngadengon, R. (2015). Analysis of OptimalBrainwave Concentration Model for Wheelchair Input Interface. Procedia Computer Science, 76,336-341.Turnip, A., Rizgyawan, M. I., Esti, K. D., Yanyoan, S., & Mulyana, E. (2016). Real timeclassification of SSVEP brain activity with adaptive feedforward neural networks. Paper presentedat the 2016 3rd International Conference onInformation Technology, Computer, and Electrical Engineering (ICITACEE).Turnip, A., Simbolon, A. I., Amri, M. F., & Suhendra, M. A. (2015). Utilization ofEEG-SSVEP method and ANFIS classifier for controlling electronic wheelchair. Paperpresented at the Technology, Informatics, Management, Engineering & Environment (TIME-E), 2015International Conference on.Turnip, A., Soetraprawata, D., & Tamba, T. A. (2015). EEG-SSVEP signals extraction with nonlinearadaptive filter for brain-controlled wheelchair. Paper presented at the Control, Automation andSystems (ICCAS), 2015 15th International Conference on.Turnip, A., Soetraprawata, D., Turnip, M., & Joelianto, E. (2016a). EEG-based brain- controlledwheelchair with four different stimuli frequencies. Internetworking Indonesia Journal, 8.Turnip, A., Soetraprawata, D., Turnip, M., & Joelianto, E. (2016b). EEG-Based Brain- Controlled Wheelchair with Four Different Stimuli Frequencies. Internetworking IndonesiaJournal, 8, 65-69.Turnip, A., Suhendra, M. A., & WS, M. S. (2015a). Brain-controlled wheelchair based EEG-SSVEPsignals classified by nonlinear adaptive filter. Paper presented at the 2015 IEEE InternationalConference on Rehabilitation Robotics (ICORR).Turnip, A., Suhendra, M. A., & WS, M. S. (2015b). Brain-controlled wheelchair based EEG-SSVEPsignals classified by nonlinear adaptive filter. Paper presented at the Rehabilitation Robotics(ICORR), 2015 IEEE International Conference on.Turnip, M., Dharma, A., Pasaribu, H. H., Harahap, M., Amri, M. F., Suhendra, M., & Turnip, A.(2015). An application of online ANFIS classifier for wheelchair based brain computer interface.Paper presented at the Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT), 2015 International Conference on.Tyagi, A., & Nehra, V. (2016). Classification of motor imagery EEG signals using SVM, k-NN and ANN.CSI transactions on ICT, 4(2-4), 135-139.Valsan, G., Grychtol, B., Lakany, H., & Conway, B. A. (2009). The strathclyde brain computerinterface. Paper presented at the Engineering in Medicine and Biology Society, 2009. EMBC2009. Annual International Conference of the IEEE.Venkatasubramanian, V., & Balaji, R. K. (2009). Non invasive brain computer interfacefor movement control. Paper presented at the Proceedings of the World Congress onEngineering and Computer Science.Wang, H., Li, Y., Long, J., Yu, T., & Gu, Z. (2014). An asynchronous wheelchaircontrol by hybrid EEGEOG braincomputer interface. Cognitiveneurodynamics, 8(5), 399-409.Wang, H., & Zhang, Y. (2016). Detection of motor imagery EEG signals employingNave Bayes based learning process. Measurement, 86, 148-158.Wang, L., & Ayaz, H. Comparing machine learning approaches for motor-activity- relatedbrain computer interfaces. Frontiers in Human Neuroscience. doi:10.3389/conf.fnhum.2018.227.00135Widyotriatmo, A., & Andronicus, S. (2015). A collaborative control of brain computer interface androbotic wheelchair. Paper presented at the Control Conference (ASCC), 2015 10th Asian.Winod, A., & Cheng, K. (2009). Towards a Brain-Computer Interface based control for next generation electric wheelchairs. Paper presented at the Power Electronics Systemsand Applications, 2009. PESA 2009. 3rd International Conference on.Xie, Y., & Li, X. (2015). A brain controlled wheelchair based on common spatialpattern. Paper presented at the Bioelectronics and Bioinformatics (ISBB), 2015 InternationalSymposium on. Ahangi, A., Karamnejad, M., Mohammadi, N., Ebrahimpour, R., & Bagheri, N. (2013).Multiple classifier system for EEG signal classification with application tobraincomputer interfaces. Neural Computing and Applications, 23(5), 1319-1327.Yaacob, H., Abdul, W., & Kamaruddin, N. (2013). Classification of EEG signals using MLP based oncategorical and dimensional perceptions of emotions. Paper presented at the 2013 5thInternational Conference on Information and Communication Technology for the Muslim World(ICT4M).Zhang, Y., Wang, Y., Zhou, G., Jin, J., Wang, B., Wang, X., & Cichocki, A. (2018). Multi-kernelextreme learning machine for EEG classification in brain-computer interfaces. Expert Systems with Applications, 96, 302-310.

Generic pattern recognition models based on EEG-MI brain computer interfaces for wheelchair steering control

Similar Items