TY - GEN
T1 - Brain computer interface using motor imagery and facial expressions to control a mobile robot
AU - Kuffuor, James
AU - Samanta, Biswanath
N1 - Publisher Copyright:
Copyright © 2018 ASME.
PY - 2018
Y1 - 2018
N2 - A study is presented on brain computer interface (BCI) using motor imagery (MI) and facial expressions to control a mobile robot. Traditionally, only MI signals are used in BCI applications. In this paper a hybrid approach of using both MI and facial expression stimulations for BCI is proposed. Electroencephalography (EEG) signals were acquired using a sensor system and processed for several MI and facial expressions to extract characteristic features. The features were used to train support vector machine (SVM) based classifiers and the trained classifiers were used to recognize test signals for correct identification of MI and facial expressions. A system was developed to implement the BCI using MI and facial expressions to control a mobile robot. Results of training using MI and facial expressions, individually and together are presented for comparison. The combined features from MI and facial expression stimulations were found to give performance similar to facial expressions but better than MI only.
AB - A study is presented on brain computer interface (BCI) using motor imagery (MI) and facial expressions to control a mobile robot. Traditionally, only MI signals are used in BCI applications. In this paper a hybrid approach of using both MI and facial expression stimulations for BCI is proposed. Electroencephalography (EEG) signals were acquired using a sensor system and processed for several MI and facial expressions to extract characteristic features. The features were used to train support vector machine (SVM) based classifiers and the trained classifiers were used to recognize test signals for correct identification of MI and facial expressions. A system was developed to implement the BCI using MI and facial expressions to control a mobile robot. Results of training using MI and facial expressions, individually and together are presented for comparison. The combined features from MI and facial expression stimulations were found to give performance similar to facial expressions but better than MI only.
KW - Brain computer interface
KW - Common spatial patterns
KW - Electroencephalography
KW - Facial expression
KW - Independent component analysis
KW - Motor imagery
KW - Power spectral Density
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85057332286&partnerID=8YFLogxK
U2 - 10.1115/DSCC2018-9234
DO - 10.1115/DSCC2018-9234
M3 - Conference article
AN - SCOPUS:85057332286
T3 - ASME 2018 Dynamic Systems and Control Conference, DSCC 2018
BT - Advances in Control Design Methods; Advances in Nonlinear Control; Advances in Robotics; Assistive and Rehabilitation Robotics; Automotive Dynamics and Emerging Powertrain Technologies; Automotive Systems; Bio Engineering Applications; Bio-Mechatronics and Physical Human Robot Interaction; Biomedical and Neural Systems; Biomedical and Neural Systems Modeling, Diagnostics, and Healthcare
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2018 Dynamic Systems and Control Conference, DSCC 2018
Y2 - 30 September 2018 through 3 October 2018
ER -