Neuro Controlled Object Retrieval System
DOI:
https://doi.org/10.32628/CSEIT241051074Keywords:
Brain Computer Interface, EEG signals, Home AutomationAbstract
In this time of digitization and computerization, the life of individuals is getting more straightforward as nearly everything is programmed. This interconnection of the things can be used to help people with physical disabilities including paralysis. The brain signals of such people can be harnessed to create an object retrieval system. In this work, the concentration levels of such individuals are extracted using the EEG signals. These signals are then used to control the electronic devices. The designed system has been tested to control robotic car and arm using brain signals. These systems enable users to control external devices through neural signals, presenting significant potential in healthcare, robotics, and human-computer interaction. This paper reviews the development and implementation of a Neuro Controlled Object Retrieval System, which allows a user to control a robotic arm using brainwave patterns to pick up and retrieve objects. The system leverages Electroencephalography (EEG) signals to interpret neural activity, processed using machine learning algorithms to translate brain commands into physical actions. The objective of this review is to explore the existing methodologies, technologies, and challenges associated with such systems. The paper also discusses the potential applications, future advancements, and the significance of this technology in enhancing assistive robotics and rehabilitation tools.
Downloads
References
Sushmitha, M., Kolkar, N., Suman S.G., Kulkarni, K. “Morse Code Detector and Decoder using Eye Blinks”, Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021, 2021,pp. 651–658.
Sunny T.D, Aparna T, Neethu P, Venkateswaran J, Vishnupriya V, Vyas P.S “Robotic Arm with Brain - Computer Interfacing”, Procedia Technology, Volume 24, 2016, Pages 1089-1096, ISSN 2212-0173, https://doi.org/10.1016/j.protcy.2016.05.241. DOI: https://doi.org/10.1016/j.protcy.2016.05.241
“A Brain-Computer Interface for Robotic Arm Control”- Alexander Lenhardt, University Thesis, Bielefeld, Univ., Diss., 2011
Veeramma Yatnalli, B G Shivaleelavathi, Saroja S Bhusare, Aditi Prakash, Nithyashree D, “Wheelchair Control Using Brain Computer Interface”, International Journal of Future Generation Communication and Networking, Vol. 14, No. 1, (2021), pp.384–395
Jinwon An, Sungzoon Cho, “Hand Motion Identification of Grasp-and-Lift task from Electroencephalography Recordings using Recurrent Neural Networks”, 2016 International Conference on Big Data and Smart Computing (BigComp), Hong Kong, China, 2016, pp. 427- 429, doi: 10.1109/BIGCOMP.2016.7425963. DOI: https://doi.org/10.1109/BIGCOMP.2016.7425963
L Dongxue, T Jeffrey Too Chuan, Z Chi, D Feng, Design of an Online BCI System Based on CCA Detection Method”, 2015 34th Chinese Control Conference (CCC), Hangzhou, China, 2015, pp. 4728-4733 doi: 10.1109/ChiCC.2015.7260370. DOI: https://doi.org/10.1109/ChiCC.2015.7260370
Rohan Hundia “Brain Computer Interface- Controlling Devices Utilizing the Alpha Brain Waves”, International Journal of Scientific & Technolog Research Volume 4, Issue 01, January 2015, pp 281-285
Shyam Diwakar, Sandeep Bodda, Chaitanya Nutakki, Asha Vijayan, Krishnashree Achuthan and Bipin Nair, “Neural Control using EEG as a BCI Technique for Low-Cost Prosthetic Arms” -
Muhammad Yasir Latif, Laiba Naeem, Tehmina Hafeez, Aasim Raheel, “Brain Computer Interface based Robotic Arm Control” –
Khow Hong Way, “Design and Development of a Brain Computer Interface Controlled Robotic Arm” -
Vishwasi, R., Holla, S.K, Yogeshwar, D, Kulkarni K, “A Raspberry Pi based CNN model for Indian Currency detection for Visually Impaired People”, 3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022 - pp.1170–1175. DOI: https://doi.org/10.1109/ICESC54411.2022.9885317
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
