Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose

Abstract The paper proposes a novel approach toward EEG-driven position control of a robot arm by utilizing motor imagery, P300 and error-related potentials (ErRP) to align the robot arm with desired target position. In the proposed scheme, the users generate motor imagery signals to control the motion of the robot arm. The P300 waveforms are detected when the user intends to stop the motion of the robot on reaching the goal position. The error potentials are employed as feedback response by the user. On detection of error the control system performs the necessary corrections on the robot arm. Here, an AdaBoost-Support Vector Machine (SVM) classifier is used to decode the 4-class motor imagery and an SVM is used to decode the presence of P300 and ErRP waveforms. The average steady-state error, peak overshoot and settling time obtained for our proposed approach is 0.045, 2.8 % and 44 s, respectively, and the average rate of reaching the target is 95 %. The results obtained for the proposed control scheme make it suitable for designs of prosthetics in rehabilitative applications.

[1]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

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

[3]  Günther Eibl,et al.  Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example , 2001, ECML.

[4]  Shyamanta M. Hazarika,et al.  Motor imagery based BCI for a maze game , 2012, 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI).

[5]  Eric R. Ziegel,et al.  Analysis of Financial Time Series , 2002, Technometrics.

[6]  M. Nuttin,et al.  A brain-actuated wheelchair: Asynchronous and non-invasive Brain–computer interfaces for continuous control of robots , 2008, Clinical Neurophysiology.

[7]  David G. Stork,et al.  Pattern Classification , 1973 .

[8]  Benjamin C. Kuo,et al.  Automatic control systems (7th ed.) , 1991 .

[9]  G.E. Birch,et al.  A general framework for brain-computer interface design , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  Yuanqing Li,et al.  A Hybrid Brain Computer Interface to Control the Direction and Speed of a Simulated or Real Wheelchair , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[12]  Amit Konar,et al.  Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata , 2013, Medical & Biological Engineering & Computing.

[13]  Francois Routhier,et al.  Evaluation of the JACO robotic arm: Clinico-economic study for powered wheelchair users with upper-extremity disabilities , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[14]  Benjamin C. Kuo,et al.  AUTOMATIC CONTROL SYSTEMS , 1962, Universum:Technical sciences.

[15]  R Chavarriaga,et al.  Learning From EEG Error-Related Potentials in Noninvasive Brain-Computer Interfaces , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[16]  José del R. Millán,et al.  Simultaneous Real-Time Detection of Motor Imagery and Error-Related Potentials for Improved BCI Accuracy , 2008 .

[17]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[18]  G. Pfurtscheller,et al.  Information transfer rate in a five-classes brain-computer interface , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  R. Tsay.  Analysis of Financial Time Series: Tsay/Financial Time Series 3E , 2010 .

[20]  C. Neuper,et al.  Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges , 2010, Front. Neurosci..

[21]  Bin He,et al.  EEG Control of a Virtual Helicopter in 3-Dimensional Space Using Intelligent Control Strategies , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[22]  Yuanqing Li,et al.  A Hybrid BCI System Combining P300 and SSVEP and Its Application to Wheelchair Control , 2013, IEEE Transactions on Biomedical Engineering.

[23]  P. F. M. J. Verschure,et al.  Using a Hybrid Brain Computer Interface and Virtual Reality System to Monitor and Promote Cortical Reorganization through Motor Activity and Motor Imagery Training , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[24]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[25]  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.

[26]  G. Pfurtscheller,et al.  Self-Paced Operation of an SSVEP-Based Orthosis With and Without an Imagery-Based “Brain Switch:” A Feasibility Study Towards a Hybrid BCI , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[27]  G. Berns,et al.  BAD TO WORSE , 1975, The Lancet.

[28]  G Pfurtscheller,et al.  Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[29]  K. R. Ridderinkhof,et al.  Error-related brain potentials are differentially related to awareness of response errors: evidence from an antisaccade task. , 2001, Psychophysiology.

[30]  Xiao-Dong Zhang,et al.  A practical method for motor imagery based real-time prosthesis control , 2012, 2012 IEEE International Conference on Automation Science and Engineering (CASE).

[31]  D. N. Tibarewala,et al.  EEG controlled remote robotic system from motor imagery classification , 2012, 2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT'12).

[32]  José del R. Millán,et al.  Towards Brain-Computer Interfacing , 2007 .

[33]  Peng Hu,et al.  Experiment study of the relation between motion complexity and event-related desynchronization/synchronization , 2005, Proceedings. 2005 First International Conference on Neural Interface and Control, 2005..

[34]  E. Donchin,et al.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.