Authors:
Shyam Diwakar
1
;
Sandeep Bodda
1
;
Chaitanya Nutakki
2
;
Asha Vijayan
2
;
Krishnashree Achuthan
2
and
Bipin Nair
2
Affiliations:
1
Amrita Vishwa Vidyapeetham(Amrita University), India
;
2
Amrita University, India
Keyword(s):
Brain Computer Interface, Prosthetic Arm, Kinematics, EEG, Iterative Algorithm, Machine learning
Related
Ontology
Subjects/Areas/Topics:
Adaptive Architectures and Mechanisms
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Bio-Inspired and Humanoid Robotics
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Complex Artificial Neural Network Based Systems and Dynamics
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Higher Level Artificial Neural Network Based Intelligent Systems
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Network Hardware Implementation and Applications
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
There have been significant advancements in brain computer interface (BCI) techniques using EEG-like methods. EEG can serve as non-invasive BMI technique, to control devices like wheelchairs, cursors and robotic arm. In this paper, we discuss the use of EEG recordings to control low-cost robotic arms by extracting motor task patterns and indicate where such control algorithms may show promise towards the humanitarian challenge. Studies have shown robotic arm movement solutions using kinematics and machine learning methods. With iterative processes for trajectory making, EEG signals have been known to be used to control robotic arms. The paper also showcases a case-study developed towards this challenge in order to test such algorithmic approaches. Non-traditional approaches using neuro-inspired processing techniques without implicit kinematics have also shown potential applications. Use of EEG to resolve temporal information may, indeed, help understand movement coordination in robot
ic arm.
(More)