A System For P300 Detection Applied To Vehicle Navigation

Brain-machine interface (BMI) systems are used to classify biological signals from the brain, such as electroencephalogram (EEG) data, to determine control commands. There are several different signals that can be used for the interface. Among them, one finds the P300 signal. The P300 signal is a potential signal that is passively produced when a user observes, hears or pays attention to a desired stimulus. This signal has been used in conjunction with a graphical user interface (GUI) to allow a person to choose commands from a list of possible actions. Traditionally, the visual stimuli are repeated and averaged to increase classification accuracy, which, in turn, reduces the maximum possible command rate. In order to improve command rate, this paper describes a system wherein feature extraction and classifier training could be tested offline. Then, live testing in a mobile robot steering simulation was carried out. Finally, a live experiment is reported. The features to be used in classification are selected using a genetic algorithm (GA). Using the chosen features, 78.3% signal detection accuracy was achieved for single epochs. Using multiple-epochs to improve classifier performance in simulated and real-world steering experiments we were able to successfully navigate a simple maze while maintaining classifier accuracy (Sim: $79.9 \pm 5.3$%, Real: $88.8\pm 10.1$%).

[1]  Wanderley Cardoso Celeste,et al.  Brain-computer Interface Based on Visual Evoked Potentials to Command Autonomous Robotic Wheelchair , 2010 .

[2]  N. Birbaumer,et al.  Brain–computer interfaces for communication and rehabilitation , 2016, Nature Reviews Neurology.

[3]  Z J Koles,et al.  The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG. , 1991, Electroencephalography and clinical neurophysiology.

[4]  K. Lafleur,et al.  Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface , 2013, Journal of neural engineering.

[5]  Sidney N. Givigi,et al.  A system based on genetic algorithms for on-line single-trial P300 detection , 2016, 2016 Annual IEEE Systems Conference (SysCon).

[6]  Thierry Pun,et al.  Analysis of bit-rate definitions for Brain-Computer Interfaces , 2005, CSREA HCI.

[7]  Emanuele Menegatti,et al.  Towards a Brain-Robot Interface for children , 2019, 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC).

[8]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[9]  Yasuharu Koike,et al.  Hybrid Control of a Vision-Guided Robot Arm by EOG, EMG, EEG Biosignals and Head Movement Acquired via a Consumer-Grade Wearable Device , 2016, IEEE Access.

[10]  Kocsis Zoltán Tamás,et al.  IEEE World Congress on Computational Intelligence , 2019, IEEE Computational Intelligence Magazine.

[11]  Thierry Dutoit,et al.  Performance of the Emotiv Epoc headset for P300-based applications , 2013, Biomedical engineering online.

[12]  Yue Zhao,et al.  A Wireless BCI and BMI System for Wearable Robots , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[13]  E Donchin,et al.  The mental prosthesis: assessing the speed of a P300-based brain-computer interface. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[14]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[15]  Manuel Mazo,et al.  Improvements of a Brain-Computer Interface Applied to a Robotic Wheelchair , 2009, BIOSTEC.

[16]  O. Bai,et al.  Electroencephalography (EEG)-Based Brain–Computer Interface (BCI): A 2-D Virtual Wheelchair Control Based on Event-Related Desynchronization/Synchronization and State Control , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[17]  E. W. Sellers,et al.  Toward enhanced P300 speller performance , 2008, Journal of Neuroscience Methods.

[18]  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.

[19]  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.

[20]  Sidney N. Givigi,et al.  A genetic algorithm for single-trial P300 detection with a low-cost EEG headset , 2015, 2015 Annual IEEE Systems Conference (SysCon) Proceedings.

[21]  K. A. Colwell,et al.  Channel selection methods for the P300 Speller , 2014, Journal of Neuroscience Methods.

[22]  Luca T. Mainardi,et al.  A genetic algorithm for automatic feature extraction in P300 detection , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[23]  Iñaki Iturrate,et al.  A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation , 2009, IEEE Transactions on Robotics.

[24]  Yuanqing Li,et al.  Control of a Wheelchair in an Indoor Environment Based on a Brain–Computer Interface and Automated Navigation , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[25]  Roland Siegwart,et al.  Article in Press Robotics and Autonomous Systems ( ) – Robotics and Autonomous Systems Brain-coupled Interaction for Semi-autonomous Navigation of an Assistive Robot , 2022 .

[26]  Daniela De Venuto,et al.  Real-time P300-based BCI in mechatronic control by using a multi-dimensional approach , 2018, IET Softw..

[27]  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.