In robotics research, the electroencephalograph (EEG) based brain-computer interface (BCI) as a control input has been used in designing prosthesis, wheelchairs and virtual navigation. The paper presents the research work on BCI development that communicates between an operator and a robotic gripping device. The control of a BCI robotic hand is broken down into two main subsystems. The first subsystem acquires a signal from the brain through the Emotiv EPOC EEG headset, extracts features and translates them into an input to the control system. The second subsystem incorporates kinematics and feedback from sensors, to control the multiple degrees of freedom used in the gripping device depending on the action specified by the higher-level BCI control. The BCI is trained to filter and extract features relating to the different hand motions from the data sets. Machine learning is used in conjunction with data filtering, feature extraction, and feature classification techniques to create a more accurate and personalized BCI hand control system. The system analyzes the EEG data, compares with the EEG data patterns from previous attempts. The test results demonstrate the movement functions of the gripper using the BCI, and the success rate for each function are presented in this paper.
Skip Nav Destination
ASME 2018 International Mechanical Engineering Congress and Exposition
November 9–15, 2018
Pittsburgh, Pennsylvania, USA
Conference Sponsors:
- ASME
ISBN:
978-0-7918-5203-3
PROCEEDINGS PAPER
Brain-Computer Interface Application in Robotic Gripper Control
Briana Landavazo,
Briana Landavazo
California State University Northridge, Northridge, CA
Search for other works by this author on:
Vidya K. Nandikolla
Vidya K. Nandikolla
California State University Northridge, Northridge, CA
Search for other works by this author on:
Briana Landavazo
California State University Northridge, Northridge, CA
Vidya K. Nandikolla
California State University Northridge, Northridge, CA
Paper No:
IMECE2018-86274, V04AT06A012; 8 pages
Published Online:
January 15, 2019
Citation
Landavazo, B, & Nandikolla, VK. "Brain-Computer Interface Application in Robotic Gripper Control." Proceedings of the ASME 2018 International Mechanical Engineering Congress and Exposition. Volume 4A: Dynamics, Vibration, and Control. Pittsburgh, Pennsylvania, USA. November 9–15, 2018. V04AT06A012. ASME. https://doi.org/10.1115/IMECE2018-86274
Download citation file:
32
Views
Related Proceedings Papers
Related Articles
Brain Computer Interface Classifiers for Semi-Autonomous Wheelchair Using Fuzzy Logic Optimization
ASME J of Medical Diagnostics (May,2020)
An Advanced Patient Lift and Transfer Device for the Home
J. Med. Devices (March,2010)
Design and Implementation of a Behavioral Sequence Framework for Human–Robot Interaction Utilizing Brain-Computer Interface and Haptic Feedback
ASME J of Medical Diagnostics (November,2023)
Related Chapters
Feature Extraction and Classification of EEG Signal
International Conference on Mechanical Engineering and Technology (ICMET-London 2011)
Feedback-Aided Minimum Joint Motion
Robot Manipulator Redundancy Resolution
QP Based Encoder Feedback Control
Robot Manipulator Redundancy Resolution