Wireless mobile robot control through human machine interface using brain signals

Recent researches shows that Brain Computer Interface (BCI) technology provides effective way of communication between human and physical device. In this work, an EEG based wireless mobile robot is implemented for people suffer from motor disabilities can interact with physical devices based on Brain Computer Interface (BCI). An experimental model of mobile robot is explored and it can be controlled by human eye blink strength. EEG signals are acquired from NeuroSky Mind wave Sensor (single channel prototype) in non-invasive manner and Signal features are extracted by adopting Discrete Wavelet Transform (DWT) to amend the signal resolution. We analyze and compare the db4 and db7 wavelets for accurate classification of blink signals. Different classes of movements are achieved based on different blink strength of user. The experimental setup of adaptive human machine interface system provides better accuracy and navigates the mobile robot based on user command, so it can be adaptable for disabled people.

[1]  Yili Liu,et al.  EEG-Based Brain-Controlled Mobile Robots: A Survey , 2013, IEEE Transactions on Human-Machine Systems.

[2]  Müjdat Çetin,et al.  Brain Computer Interface based robotic rehabilitation with online modification of task speed , 2013, 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR).

[3]  Marco A. Meggiolaro,et al.  Activation of a mobile robot through a brain computer interface , 2010, 2010 IEEE International Conference on Robotics and Automation.

[4]  T. Martin McGinnity,et al.  EEG-Based Mobile Robot Control Through an Adaptive Brain–Robot Interface , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[5]  Chia-Wei Sun,et al.  A Brain-Wave-Actuated Small Robot Car Using Ensemble Empirical Mode Decomposition-Based Approach , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[6]  Zakaria Hussain,et al.  Preliminary study on analyzing EEG alpha brainwave signal activities based on visual stimulation , 2014 .

[7]  Yan Guozheng,et al.  EEG feature extraction based on wavelet packet decomposition for brain computer interface , 2008 .

[8]  Gamini Dissanayake,et al.  Choice modeling and the brain: A study on the Electroencephalogram (EEG) of preferences , 2012, Expert Syst. Appl..

[9]  Mário Sarcinelli-Filho,et al.  Commanding a robotic wheelchair with a high-frequency steady-state visual evoked potential based brain-computer interface. , 2013, Medical engineering & physics.

[10]  Y. Wongsawat,et al.  Semi-automatic P300-based brain-controlled wheelchair , 2012, 2012 ICME International Conference on Complex Medical Engineering (CME).

[11]  Fotis Liarokapis,et al.  Evaluation of commercial brain-computer interfaces in real and virtual world environment: A pilot study , 2014, Comput. Electr. Eng..

[12]  N.A. Md Norani,et al.  A review of signal processing in brain computer interface system , 2010, 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).

[13]  John Tudor,et al.  Real time eye blink noise removal from EEG signals using morphological component analysis , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[14]  Brice Rebsamen,et al.  A brain controlled wheelchair to navigate in familiar environments. , 2010, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[15]  Urbano Nunes,et al.  Assisted navigation for a brain-actuated intelligent wheelchair , 2013, Robotics Auton. Syst..

[16]  M. J. E. Salami,et al.  EEG signal classification for real-time brain-computer interface applications: A review , 2011, 2011 4th International Conference on Mechatronics (ICOM).

[17]  Stéphane Bonnet,et al.  Eye blink characterization from frontal EEG electrodes using source separation and pattern recognition algorithms , 2014, Biomed. Signal Process. Control..

[18]  Andrés Úbeda,et al.  Shared control architecture based on RFID to control a robot arm using a spontaneous brain-machine interface , 2013, Robotics Auton. Syst..

[19]  Andrés Úbeda,et al.  Classification method for BCIs based on the correlation of EEG maps , 2013, Neurocomputing.

[20]  Jaeseung Jeong,et al.  Toward Brain-Actuated Humanoid Robots: Asynchronous Direct Control Using an EEG-Based BCI , 2012, IEEE Transactions on Robotics.

[21]  Kenneth Revett,et al.  EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks , 2006, IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA'06).

[22]  D. Borghetti,et al.  A low-cost interface for control of computer functions by means of eye movements , 2007, Comput. Biol. Medicine.

[23]  José del R. Millán,et al.  Noninvasive brain-actuated control of a mobile robot by human EEG , 2004, IEEE Transactions on Biomedical Engineering.