A Novel Approach for Non-Invasive, Brain-Computer Interaction Using Electroencephalogram Signals

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 8

Year of Publication : 2024

Authors : Gaurang Patkar, Maria Christina Barretto, Sweta Morajkar, Norman Dias, Vivek Jog

10.14445/23488549/IJECE-V11I8P120

How to Cite?

Gaurang Patkar, Maria Christina Barretto, Sweta Morajkar, Norman Dias, Vivek Jog, "A Novel Approach for Non-Invasive, Brain-Computer Interaction Using Electroencephalogram Signals," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 8, pp. 200-206, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I8P120

Abstract:

This paper presents a novel approach for non-invasive, brain-computer interaction using signals acquired from an end user. The proposed system employs a hybrid Machine Learning (ML) model to analyze EEG (Electroencephalogram) signals from the user’s brain, which are then digitized, processed, and mapped to appropriate outputs, ultimately enabling users to control a video game environment and play a game without needing any physical inputs. This paper also aims to develop a video game that seamlessly integrates with and masks the limitations of a BCI. This innovative approach opens up new possibilities for human-computer interaction, offering an intuitive and efficient means of control. By using P300 signals along with mu rhythms, the system maximizes functionality and provides a seamless and immersive user experience. The non-invasive nature of the sensors ensures comfort and ease of use.

Keywords:

Machine Learning, BCI, Electroencephalogram, OpenBCI.

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