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Type :thesis
Subject :TK Electrical engineering. Electronics Nuclear engineering
Main Author :Al-Qaysi, Ziadoon Tareq AbdulWahhab
Title :Generic pattern recognition models based on EEG-MI brain computer interfaces for wheelchair steering control
Place of Production :Tanjong Malim
Publisher :Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :2020
Corporate Name :Universiti Pendidikan Sultan Idris
PDF Guest :Click to view PDF file

Abstract : Universiti Pendidikan Sultan Idris
The purpose of this study was to develop generic pattern recognition models (GPRMs) based on two-class EEG–MI brain-computer interfaces for wheelchair steering control. Initially, a preprocessing procedure was performed to remove unwanted signals and to identify the optimal duration of MI feature components. Then, feature extraction based on five statistical features, namely min, max, mean, median, and standard deviation were utilized 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 algorithms that could be used for the development of hybrid classifiers. Two datasets were used, namely the BCI Competition dataset (which belonged to Graz University) and the Emotive EPOC dataset (which was collected in this study), with the former being utilized in the development, evaluation, and validation of the GPRM models and the latter being used for validation only. The research findings showed that GPRM models based on the LR classifier were highly accurate in the time and time-frequency domains in the range of 4 and 6 seconds and 4 and 7 seconds, respectively. In addition, GPRM models based on the MLP-LR classifier were highly accurate in the frequency domain in the range of 4 and 6 seconds. Furthermore, the validation of such models using the Emotive EPOC dataset showed that the LR-based GPRM model attained high classification accuracies of 90.2% and 85.7% in the time domain and time-frequency domain, respectively. The MLP-LR-based GPRM models achieved a classification accuracy of 84.2% in the frequency domain. In conclusion, the main findings showed that GPRMs were highly adaptable when deployed in the real-time application of the EEG-MI-based wheelchair steering control system. The implication of this study is that generic pattern recognition models based on EEG-MI Brain-Computer interfaces can be utilized to improve the effectiveness of wheelchair steering control.  

References

Abdalsalam,  M.  E.,  Yusoff,  M.  Z.,  Kamel,  N.,  Malik,  A.,  &  Meselhy,  M.  (2014).

Mental  task  motor  imagery  classifications  for  noninvasive  brain  computer interface. Paper 

presented at the Intelligent and Advanced Systems (ICIAS), 2014 5th International Conference on.

 

Abiyev,  R.  H.,  Akkaya,  N.,  Aytac,  E.,  Günsel,  I.,  &  Ça?man,  A.  (2016).  Brain- Computer 

Interface for Control of Wheelchair Using Fuzzy Neural Networks. BioMed research international, 

2016.

 

Achic,  F.,  Montero,  J.,  Penaloza,  C.,  &  Cuellar,  F.  (2016).  Hybrid  BCI  system  to 

operate   an   electric   wheelchair   and   a   robotic   arm   for   navigation   and 

manipulation tasks. Paper presented at the Advanced Robotics and its Social Impacts (ARSO), 2016 

IEEE Workshop on.

 

Adeli, H., Zhou, Z., & Dadmehr, N. (2003). Analysis of EEG records in an epileptic patient using 

wavelet transform. Journal of neuroscience methods, 123(1), 69- 87.

 

Al-Fahoum,  A.  S.,  &  Al-Fraihat,  A.  A.  (2014).  Methods  of  EEG  signal  features extraction 

using linear analysis in frequency and time-frequency domains. ISRN neuroscience, 2014.

 

Amarasinghe, K., Wijayasekara, D., & Manic, M. (2014). EEG based brain activity monitoring using 

Artificial Neural Networks. Paper presented at the 2014 7th International Conference on Human 

System Interactions (HSI).

 

Andronicus, S., Harjanto, N. C., & Widyotriatmo, A. (2015). Heuristic Steady State Visual  Evoked  

Potential 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  for 

control   and   rehabilitation.   IEEE   Transactions   on   Neural   Systems   and Rehabilitation 

Engineering, 25(4), 392-401.

 

Aydemir, O., & Kayikcioglu, T. (2014). Decision tree structure based classification of EEG  signals 

 recorded  during  two  dimensional  cursor  movement  imagery. Journal of neuroscience methods, 

229, 68-75.

 

Azami, H., & Escudero, J. (2015). Combination of signal segmentation approaches using fuzzy 

decision making. Paper presented at the Engineering in Medicine and Biology Society (EMBC), 2015 

37th Annual International Conference of

the IEEE.

 

Aziz,  F.,  Arof,  H.,  Mokhtar,  N.,  &  Mubin,  M.  (2014).  HMM  based  automated

wheelchair   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 computer  

interface  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  channel 

selection in motor imagery EEG applications: a survey. Artificial intelligence review, 1-26.

 

Bastos-Filho, T., Ferreira, A., Cavalieri, D., Silva, R., Muller, S., & Pérez, 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 and 

Biorobotics Conference (BRC), 2013 ISSNIP.

 

Bastos, T. F., Muller, S. M., Benevides, A. B., & Sarcinelli-Filho, M. (2011). Robotic wheelchair 

commanded by SSVEP, motor imagery and word generation. Paper presented at the Engineering in 

Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE

 

Batres-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 communication 

systems. life, 1, 3.

 

Belwafi, K., Djemal, R., Ghaffari, F., & Romain, O. (2014). An adaptive EEG filtering approach  to  

maximize  the  classification  accuracy  in  motor  imagery.  Paper presented at the Computational 

Intelligence, 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). Multimodal 

system for training at  distance in  a virtual  or augmented reality environment for users of 

electric-powered wheelchairs. IFAC-PapersOnLine,

49(30), 156-160.

 

Budiharto,  W.,  Gunawan,  A.  A.  S.,  Parmonangan,  I.  H.,  &  Santoso,  J.  Fast  Brain

Control 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 different 

mental tasking: Preliminary study for stroke survivors in Indonesia. Paper presented at the 

Automation, Cognitive Science, Optics, Micro      Electro-Mechanical      System,      and      

Information      Technology (ICACOMIT), 2015 International Conference on

 

Cao, 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., & Millán, J. d. R. (2012). The birth of the brain- 

controlled wheelchair. Paper presented at the Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ 

International Conference on.

 

Carlson,  T.,  &  Millan,  J.  d.  R.  (2013).  Brain-controlled  wheelchairs:  a  robotic 

architecture. 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 and 

Robotics 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 patterns 

feature extraction. Paper presented at the 2012 Workshop on Engineering Applications.

 

Cebolla,  A.-M.,  Palmero-Soler,  E.,  Leroy,  A.,  &  Cheron,  G.  (2017).  EEG  spectral 

generators  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-motor 

imagery  tasks  classifications  of  brain  computer  interface  for  wheelchair commands using 

genetic algorithm-based neural network. Paper presented at the    Proceedings    of    the    

International    Joint    Conference    on    Neural

Networks,(IJCNN), Brisbane, Queensland, Australia, 10-15 June 2012.

 

Chai, R., Ling, S. H., Hunter, G. P., & Nguyen, H. T. (2012b). Toward fewer EEG

channels  and  better  feature  extractor  of  non-motor  imagery  mental  tasks classification  

for  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). Brain–computer interface 

classifier 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 of 

Epileptic Seizure by Using DWT Feature Extraction and Neural Network. International Journal of 

Neurologic Physsical Therapy, 3, 38-43.

 

Choi,  K.  (2012).  Control  of  a  vehicle  with  EEG  signals  in  real-time  and  system 

evaluation. European journal of applied physiology, 112(2), 755-766.

Choi, K., & Cichocki, A. (2008). Control of a wheelchair by motor imagery in real time.  Paper  

presented  at  the  International  Conference  on  Intelligent  Data Engineering and Automated 

Learning

 

Craig,  D.  A.,  &  Nguyen,  H.  (2007).  Adaptive  EEG  thought  pattern  classifier  for advanced 

wheelchair control. Paper presented at the Engineering in Medicine and Biology Society, 2007. EMBS 

2007. 29th Annual International Conference of the IEEE

 

Cvetkovic, D., Übeyli, E. D., & Cosic, I. (2008). Wavelet transform feature extraction from human 

PPG, 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 wheelchair 

using voice recognition sensors. Paper presented at the Computer Communication and Informatics 

(ICCCI), 2014 International Conference on.

 

Diez, P. F., Müller, 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- frequency 

steady-state visual evoked potential based brain–computer interface. Medical engineering & physics, 

35(8), 1155-1164.

 

Duan,  J.,  Li,  Z.,  Yang,  C.,  &  Xu,  P.  (2014).  Shared  control  of  a  brain-actuated 

intelligent   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 imagery  

classification  using  wavelet  coefficients  and  ensemble  classifiers. Paper  presented  at  the 

 The  16th  CSI  International  Symposium  on  Artificial

Intelligence and Signal Processing (AISP 2012).

 

Fan, T. L., Ng, C., Ng, J., & Goh, S. (2008). A brain-computer interface with intelligent

distributed controller for wheelchair. Paper presented at the 4th Kuala Lumpur

International 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. Paper

presented at the Data Mining Workshops (ICDMW), 2012 IEEE 12th

International 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. Paper

presented at the 2012 IEEE 12th International Conference on Data Mining

Workshops.

 

Fernández-Rodríguez, Á., Velasco-Álvarez, F., & Ron-Angevin, R. (2016). Review of

real brain-controlled wheelchairs. Journal of neural engineering, 13(6),

061001.

 

Ferreira, A., Bastos Filho, T. F., Sarcinelli Filho, M., Sanchez, J. L. M., García, J. C.

G., & Quintas, M. M. (2009). Evaluation of PSD Components and AAR

Parameters as Input Features for a SVM Classifier Applied to a Robotic

Wheelchair. 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 Eye

Blinks. Paper presented at the BIODEVICES (2).

 

Galán, F., Nuttin, M., Lew, E., Ferrez, P. W., Vanacker, G., Philips, J., & Millán, J. d.

R. (2008). A brain-actuated wheelchair: asynchronous and non-invasive brain–

computer 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-based

mobile robot control through an adaptive brain–robot interface. IEEE

Transactions on Systems, Man, and Cybernetics: Systems, 44(9), 1278-1285.

 

Gentiletti, G., Gebhart, J., Acevedo, R., Yáñez-Suárez, O., & Medina-Bañuelos, V.

(2009). Command of a simulated wheelchair on a virtual environment using a

brain-computer interface. Irbm, 30(5-6), 218-225.

 

Hamad, A., Houssein, E. H., Hassanien, A. E., & Fahmy, A. A. (2016). Feature

extraction of epilepsy EEG using discrete wavelet transform. Paper presented

at the 2016 12th international computer engineering conference (ICENCO).

 

Hamzah, N., Norhazman, H., Zaini, N., & Sani, M. (2016). Classification of EEG

signals based on different motor movement using multi-layer Perceptron

artificial neural network. J Biol Sci, 16(7), 265-271.

 

Hasan, M. R., Ibrahimy, M. I., Motakabber, S., & Shahid, S. (2015). Classification of multichannel 

EEG 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. IEEE 

Transactions 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 imagery 

classification for a four state brain machine interface.  Paper presented at the Signal Processing 

& Its Applications, 2009. CSPA 2009. 5th International Colloquium on.

 

Hjørungdal, R.-M., Sanfilippo, F., Osen, O., Rutle, A., & Bye, R. T. (2016). A game- based learning 

framework for controlling brain-actuated wheelchairs. Paper presented  at  the  30th  European  

Conference  on  Modelling  and  Simulation, Regensburg Germany, May 31st–June 3rd, 2016.

 

Hossain,  A.  A.,  Rahman,  M.  W.,  &  Riheen,  M.  A.  (2015).  Left  and  Right  Hand Movements  

 EEG   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 brain–computer interface (BCI): A 2-D virtual           

wheelchair           control           based           on           event-related 

desynchronization/synchronization  and  state  control.  IEEE  Transactions  on Neural Systems and 

Rehabilitation Engineering, 20(3), 379-388.

 

Hurtado-Rincon, J., Rojas-Jaramillo, S., Ricardo-Cespedes, Y., Alvarez-Meza, A. M., &  

Castellanos-Domínguez,  G.  (2014).  Motor  imagery  classification  using feature relevance 

analysis: An Emotiv-based BCI system. Paper presented at the 2014 XIX Symposium on Image, Signal 

Processing 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 epilepsy 

localization: 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-actuated 

wheelchair  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   and

vigation. IEEE transactions on robotics, 25(3), 614-627.

 

Izzuddin, T. A., Ariffin, M., Bohari, Z. H., Ghazali, R., & Jali, M. H. (2015). Movement

intention 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 using

wavelet feature extraction and neural networks. Paper presented at the Modern

Computing, 2006. JVA'06. IEEE John Vincent Atanasoff 2006 International

Symposium 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 imaginary

signals for machine commmunication-a novel approach for brain machine

interface design. Paper presented at the 2009 International Conference on

Signal Acquisition and Processing.

 

Jia, W., Huang, D., Bai, O., Pu, H., Luo, X., & Chen, X. (2012). Reliable planning and

execution of a human-robot cooperative system based on noninvasive braincomputer

interface with uncertainty. Paper presented at the Intelligent Robots

and Systems (IROS), 2012 IEEE/RSJ International Conference on.

 

Jiang, L., Tham, E., Yeo, M., & Phu, O. G. (2012). iPhone-based portable brain control

wheelchair. 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 controlled

wheelchair system. Paper presented at the Industrial Electronics and

Applications (ICIEA), 2014 IEEE 9th Conference on.

 

Kaneswaran, K., Arshak, K., Burke, E., & Condron, J. (2010). Towards a brain

controlled assistive technology for powered mobility. Paper presented at the

Engineering in Medicine and Biology Society (EMBC), 2010 Annual

International Conference of the IEEE.

 

Kaufmann, T., Herweg, A., & Kübler, A. (2014). Toward brain-computer interface

based 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 interface

platform for semi real-time commanding electrical wheelchair simulator

movement. 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 a

motor imagery based brain-computer interface. Paper presented at the 2013

International Winter Workshop on Brain-Computer Interface (BCI).

 

Kim, K.-T., Carlson, T., & Lee, S.-W. (2013b). Design of a robotic wheelchair with a

motor 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 driving 

system with vibro-tactile stimuli. Paper presented at the Systems, Man, and Cybernetics (SMC), 2016 

IEEE 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 Systems 

and 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 Systems 

and Rehabilitation Engineering, 26(3), 654- 665.

 

Kim, Y., Ryu, J., Kim, K. K., Took, C. C., Mandic, D. P., & Park, C. (2016). Motor imagery  

classification  using  mu  and  beta  rhythms  of  EEG  with  strong uncorrelating     transform    

 based     complex     common     spatial     patterns. Computational intelligence and 

neuroscience, 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), 2013 

International Conference on.

 

Koepsell, K., Wang, X., Hirsch, J., & Sommer, F. T. (2010). Exploring the function of neural 

oscillations 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 maybe 

characterized by Event Related Potential (P300) during virtual wheelchair  navigation.  Computer  

methods  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 a

wheelchair 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 in 

service of handicap assistance: Wheelchair navigation. Paper presented at the Individual and 

Collective 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 of  

a  hybrid brain  computer  interface  for  real  wheelchair  parallel control with multi-degree of 

freedom. International journal of neural systems,

24(04), 1450014.

 

Li, J., Liang, J., Zhao, Q., Li, J., Hong, K., & Zhang, L. (2013). Design of assistive

wheelchair 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 adaptation  

techniques  in  brain–computer  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  its  

application  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 intelligent 

 wheelchair.  Paper  presented  at  the  Robotics  and  Biomimetics (ROBIO), 2013 IEEE 

International Conference on.

 

Li, Z., Zhao, S., Duan, J., Su, C.-Y., Yang, C., & Zhao, X. (2017). Human cooperative wheelchair 

with  brain–machine  interaction based  on shared control strategy. IEEE/ASME Transactions on 

Mechatronics, 22(1), 185-195.

 

Lin, J.-S., Chen, K.-C., & Yang, W.-C. (2010). EEG and eye-blinking signals through a  

Brain-Computer  Interface  based  control  for  electric  wheelchairs  with wireless scheme. Paper 

presented at the New Trends in Information Science and Service Science (NISS), 2010 4th 

International Conference on.

 

Lin,  J.-S.,  &  Yang,  W.-C.  (2012).  Wireless  brain-computer  interface  for  electric 

wheelchairs 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  of 

Anisomerous  Motor  Imagery  EEG  Signals  Based  on  Complex  Algorithms. Computational 

intelligence and neuroscience, 2017.

 

Long, J., Li, Y., Wang, H., Yu, T., Pan, J., & Li, F. (2012). A hybrid brain computer interface to 

control the direction and speed of a simulated or real wheelchair. IEEE Transactions on Neural 

Systems 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  International

Conference on.

 

Lopes, A. C., Pires, G., & Nunes, U. (2013). Assisted navigation for a brain-actuated

intelligent 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 imagery 

EEG signals with support vector machines and particle swarm optimization. Computational and 

mathematical methods in medicine, 2016.

 

Mandel, C., Lüth, T., Laue, T., Röfer, T., Gräser, A., & Krieg-Brückner, B. (2009). Navigating a 

smart  wheelchair  with a brain-computer  interface interpreting steady-state visual evoked 

potentials. Paper presented at the Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ 

International Conference on.

 

Martinez-Leon, J.-A., Cano-Izquierdo, J.-M., & Ibarrola, J. (2016). Are low cost Brain Computer  

Interface  headsets  ready  for  motor  imagery  applications?  Expert Systems with Applications, 

49, 136-144.

 

Millán, J. d. R., Galán, F., Vanhooydonck, D., Lew, E., Philips, J., & Nuttin, M. (2009). 

Asynchronous non-invasive brain-actuated control of an intelligent wheelchair. Paper  presented  at 

 the  Engineering  in  Medicine  and  Biology  Society, 2009. EMBC 2009. Annual International 

Conference 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 arduino 

microcontroller. Paper presented at the Technologies for Sustainable Development (ICTSD), 2015 

International 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 its 

Applications, 3(4), 116-119.

 

Müller, S. M. T., Bastos-Filho, T. F., & Sarcinelli-Filho, M. (2011). Using a SSVEP- BCI  to  

command  a  robotic  wheelchair.  Paper  presented  at  the  Industrial Electronics (ISIE), 2011 

IEEE International Symposium on.

 

Müller, S. T., Celeste, W. C., Bastos-Filho, T. F., & Sarcinelli-Filho, M. (2010). Brain- computer 

interface based on visual evoked potentials to command autonomous robotic wheelchair. Journal of 

Medical 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 depressive 

disorder (MDD). Biomedical Signal Processing and

Control, 31, 108-115.

 

Naijian, C., Xiangdong, H., Yantao, W., Xinglai, C., & Hui, C. (2016a). Coordination

control strategy between human vision and wheelchair manipulator based on

BCI. Paper presented at the 2016 IEEE 11th Conference on Industrial

Electronics and Applications (ICIEA).

 

Naijian, C., Xiangdong, H., Yantao, W., Xinglai, C., & Hui, C. (2016b). Coordination

control strategy between human vision and wheelchair manipulator based on

BCI. 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 and

classification 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 for

automated epileptic seizure detection using genetic algorithm. Journal of

natural science, biology, and medicine, 8(2), 159.

 

Nguyen, C. H., & Artemiadis, P. (2018). EEG feature descriptors and discriminant

analysis under Riemannian Manifold perspective. Neurocomputing, 275, 1871-

1883.

 

Nguyen, H. T., Trung, N., Toi, V., & Tran, V.-S. (2013). An autoregressive neural

network 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 extraction

from EEG signal for brain computer interface design. Przegl D

Elektrotechniczny.

 

Oikonomou, V. P., Georgiadis, K., Liaros, G., Nikolopoulos, S., & Kompatsiaris, I.

(2017). A comparison study on EEG signal processing techniques using motor

imagery EEG data. Paper presented at the 2017 IEEE 30th international

symposium on computer-based medical systems (CBMS).

 

Parmonangan, I. H., Santoso, J., Budiharto, W., & Gunawan, A. A. S. (2016). Fast

brain 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., & Millán, J. d. R. (2010). Braincoupled

interaction 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 P300 

paradigm. Paper presented at the Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ 

International 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- controlled 

wheelchair. Paper presented at the Complex Medical Engineering (CME), 2012 ICME International 

Conference on.

 

Qidwai, U., Hassan, E. M., Al Halabi, R. M., & Shakir, M. (2013). Device interface for people with 

mobility 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 visual 

joystick. 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 asynchronous 

wheelchair navigation. Expert Systems with Applications, 42(5), 2451-2463.

 

Reaz, M., Hussain, M., Ibrahimy, M., & Mohd-Yasin, F. (2007). EEG signal analysis and 

characterization 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 an 

intelligent wheelchair in an indoor environment. Paper presented at   the   Robotics   and   

Biomimetics   (ROBIO),   2012   IEEE   International Conference on.

 

Redelico,  F.,  Traversaro,  F.,  García,  M.,  Silva,  W.,  Rosso,  O.,  &  Risk,  M.  (2017). 

Classification of normal and pre-ictal eeg signals using permutation entropies and a generalized 

linear model as a classifier. Entropy, 19(2), 72.

 

Reshmi, G., & Amal, A. (2013). Design of a BCI system for piloting a wheelchair using five  class  

MI  Based  EEG.  Paper  presented  at  the  2013  Third  International Conference on Advances in 

Computing and Communications (ICACC).

 

Rojas, D. A., Góngora, L. A., & Ramos, O. L. (2016). EEG signal analysis related to speech  process 

 through  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  in

multiclass   brain   computer   interface.   Paper   presented   at   the   2016   5th 

International 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  Fourier 

transform. brain, 2, 3.

 

Shenoy, H. V., & Vinod, A. P. (2014). An iterative optimization technique for robust channel  

selection  in  motor  imagery  based  Brain  Computer  Interface.  Paper presented at the  2014 

IEEE International  Conference  on Systems,  Man,  and Cybernetics (SMC).

 

Shin,  B.-G.,  Kim,  T.,  &  Jo,  S.  (2010).  Non-invasive  brain  signal  interface  for  a 

wheelchair navigation. Paper presented at the Int. Conf. on Control Automation and Systems.

 

Shinde, N., & George, K. (2016). Brain-controlled driving aid for electric wheelchairs. Paper  

presented  at  the  Wearable  and  Implantable  Body  Sensor  Networks (BSN), 2016 IEEE 13th 

International Conference on.

 

Siddiqi, A., SEVINDIR, H. K., Yazici, C., Kutlu, A., &  Aslan, Z. (2014). Spectral Analysis of Eeg 

Signals by using Wavelet and Harmonic Transforms. ?stanbul Ayd?n Üniversitesi Dergisi, 3(9), 1-20.

 

Siuly, S., & Li, Y. (2012). 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, 20(4), 526-538.

 

Sivakami,  A.,  &  Devi,  S.  S.  (2015).  Analysis  of  EEG  for  motor  imagery  based 

classification   of   hand   activities.   International   Journal   of   Biomedical Engineering 

and Science, 2(3), 11-22.

 

Subasi, A., & Ercelebi, E. (2005). Classification of EEG signals using neural network and logistic 

regression. 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  based  

motor  imagery  classification.  Biomedical  Signal  Processing  and

Control, 41, 1-9.

 

Su, Z., Xu, X., Ding, J., & Lu, W. (2016). Intelligent wheelchair control system based

on  BCI  and  the  image  display  of  EEG.  Paper  presented  at  the  Advanced Information 

Management, Communicates, Electronic and Automation Control Conference (IMCEC), 2016 IEEE.

 

Swee,  S.  K.,  &  You,  L.  Z.  (2016).  Fast  fourier  analysis  and  EEG  classification 

brainwave 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  electrical 

wheelchair. Paper presented at the MATEC Web of Conferences.

 

 

Szuflitowska, B., & Or?owski, P. (2017). Comparison of the EEG Signal Classifiers LDA,   NBC   and  

 GNBC   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 in 

Robotics (ICBR), 2013 International Conference on.

 

Taher, F. B., Amor, N. B., & Jallouli, M. (2015). A multimodal wheelchair control system based on 

EEG 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 for 

an electric wheelchair control. Paper presented at the Intelligent Systems (IS), 2016 IEEE 8th 

International Conference on.

 

Tangermann, M., Müller, 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., Müller, S., Ferreira, A., & Bastos-Filho, T. F. (2015).  

Development  of  a  human  machine  interface  for  control  of  robotic wheelchair and smart 

environment. IFAC-PapersOnLine, 48(19), 136-141.

 

Tomari, R., Hassan, R. R. A., Zakaria, W. N. W., & Ngadengon, R. (2015). Analysis of  Optimal  

Brainwave  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 time 

classification of SSVEP brain activity with adaptive feedforward neural networks.  Paper  presented 

 at  the  2016  3rd  International  Conference  on

Information Technology, Computer, and Electrical Engineering (ICITACEE).

 

Turnip, A., Simbolon, A. I., Amri, M. F., & Suhendra, M. A. (2015). Utilization of

EEG-SSVEP   method   and   ANFIS   classifier   for   controlling   electronic wheelchair.  Paper  

presented  at  the  Technology,  Informatics,  Management, Engineering & Environment (TIME-E), 2015 

International Conference on.

 

Turnip, A., Soetraprawata, D., & Tamba, T. A. (2015). EEG-SSVEP signals extraction with nonlinear 

adaptive filter for brain-controlled wheelchair. Paper presented at  the  Control,  Automation  and 

 Systems  (ICCAS),  2015  15th  International Conference on.

 

Turnip, A., Soetraprawata, D., Turnip, M., & Joelianto, E. (2016a). EEG-based brain- controlled 

wheelchair 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 Indonesia 

Journal, 8, 65-69.

 

Turnip, A., Suhendra, M. A., & WS, M. S. (2015a). Brain-controlled wheelchair based EEG-SSVEP 

signals classified by nonlinear adaptive filter. Paper presented at the 2015 IEEE International 

Conference on Rehabilitation Robotics (ICORR).

 

Turnip, A., Suhendra, M. A., & WS, M. S. (2015b). Brain-controlled wheelchair based EEG-SSVEP 

signals 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 computer  

interface.  Paper  presented  at  the  Engineering  in  Medicine  and Biology Society, 2009. EMBC 

2009. Annual International Conference of the IEEE.

 

Venkatasubramanian,  V.,  &  Balaji,  R.  K.  (2009).  Non  invasive  brain  computer interface  

for  movement  control.  Paper  presented  at  the  Proceedings  of  the World Congress on 

Engineering and Computer Science.

 

Wang,  H.,  Li,  Y.,  Long,  J.,  Yu,  T.,  &  Gu,  Z.  (2014).  An asynchronous  wheelchair 

control    by    hybrid    EEG–EOG    brain–computer    interface.    Cognitive

neurodynamics, 8(5), 399-409.

 

Wang, H., & Zhang, Y. (2016). Detection of motor imagery EEG signals employing

Naïve Bayes based learning process. Measurement, 86, 148-158.

 

Wang,  L.,  &  Ayaz, H. Comparing  machine  learning approaches  for  motor-activity- related  

brain  computer  interfaces.  Frontiers  in  Human  Neuroscience.  doi: 

10.3389/conf.fnhum.2018.227.00135

 

Widyotriatmo, A., & Andronicus, S. (2015). A collaborative control of brain computer interface and 

robotic 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  Systems  

and  Applications,  2009.  PESA  2009.  3rd  International Conference on.

 

Xie,  Y.,  &  Li,  X.  (2015).  A  brain  controlled  wheelchair  based  on  common  spatial 

pattern. Paper presented at the Bioelectronics and Bioinformatics (ISBB), 2015 International 

Symposium on. Ahangi, A., Karamnejad, M., Mohammadi, N., Ebrahimpour,  R.,  &  Bagheri,  N. (2013). 

 Multiple  classifier  system for  EEG signal  classification  with  application  to  

brain–computer  interfaces.  Neural Computing and Applications, 23(5), 1319-1327.

 

 

Yaacob, H., Abdul, W., & Kamaruddin, N. (2013). Classification of EEG signals using MLP  based  on  

categorical  and  dimensional  perceptions  of  emotions.  Paper presented  at  the  2013  5th  

International  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-kernel  

extreme   learning  machine   for  EEG  classification  in  brain-

computer interfaces. Expert Systems with Applications, 96, 302-310.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


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