Cover Page January-2019-Advanced Materials Letters

Advanced Materials Letters

Volume 10, Issue 1, Pages 35-40, January 2019
About Cover

The cover photo describes the graphical representation of a wearable device. Recently, the interest in wearable devices is rapidly growing with its wide applications in the wide area of disease detection and monitoring, wellness monitoring, implantable electronics, military operations etc.


Hybrid EEG-EMG based brain computer interface (BCI) system for real-time robotic arm control

Saad Abdullah, Muhammad A. Khan*, Mauro Serpelloni, Emilio Sardini

Department of Information Engineering, University of Brescia, Via Branze 38, Brescia, 25123, Italy

Adv. Mater. Lett., 2019, 10 (1), pp 35-40

DOI: 10.5185/amlett.2019.2171

Publication Date (Web): Dec 10, 2018

E-mail: m.khan004@unibs.it, engineerahmedkhan@gmail.com

Abstract

Nowadays, bio-signal based BCI systems are widely being used in healthcare systems and hence proven to be an effective tool in rehabilitation engineering to assist disabled people in improving their quality of life [1]. In this research work, handicapped people with above hand amputee have been targeted and hence non-invasive EEG and EMG biosensors are used to design wireless hybrid BCI system. The presented hybrid system is able to control real-time movement of robotic arm via combined effect of brain waves (attention and meditation mind states) and wrist muscles movements of healthy arm as command signal. The system operates the robotic arm within 3 degree of freedom (DOF) motion which corresponds to movement of shoulder (internal and external rotation), elbow (flexion and extension) and wrist (Gripper open and close) joint. It has been experimentally tested on 4 subjects with upper limb amputee (having one healthy arm) after training period of one day. On receiving the input signals from EEG and EMG sensors, subjects have successfully controlled the movements of the robotic arm with accuracy of 70% to 90%. In order to validate the obtained results, a potentiometer has been fixed on robotic arm and angular motion of shoulder and elbow joint is recorded (actual motion) and compared with results of the BCI system (required motion). The comparison shows high resemblance between actual and required motion which reflects the reliability of the system. In addition, apart from robotic prototype, its 2D modelled is also designed on visual studio. The presented preliminary experimental results show that the motorized prosthetic prototype movement due to mind and muscle control is in accordance with the 2D modelled virtual arm permitting to improve its real-time adoption for rehabilitation. 

Keywords

Rehabilitation Engineering, Electroencephalogram (EEG), Electromyogram (EMG), Brain Computer Interface (BCI) system, robotic arm.

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