Neuro Controlled Object Retrieval System

Bhuvan H S; Manjudarshan G N; Aditya S M; Dr. Keerti Kulkarni

doi:10.32628/CSEIT241051074

Authors

Bhuvan H S Department of ECE, B N M Institute of Technology, Bangalore, Karnataka, India Author
Manjudarshan G N Department of ECE, B N M Institute of Technology, Bangalore, Karnataka, India Author
Aditya S M Department of ECE, B N M Institute of Technology, Bangalore, Karnataka, India Author
Dr. Keerti Kulkarni Department of ECE, B N M Institute of Technology, Bangalore, Karnataka, India Author

DOI:

https://doi.org/10.32628/CSEIT241051074

Keywords:

Brain Computer Interface, EEG signals, Home Automation

Abstract

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.

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