Review on Brain-Controlled Device Using EEG Signals

Abstract

Brain–Computer Interface (BCI) technology enables direct communication between the human brain and external devices without muscular involvement. This paper presents the design and implementation of a brain-controlled device using Electroencephalogram (EEG) signals. Brain signals are acquired through an EEG sensor and processed using signal filtering and feature extraction techniques

Introduction

A Brain–Computer Interface (BCI) enables users to control external devices using brain signals, with EEG-based systems being popular due to their non-invasive, portable, and cost-effective nature. Such systems are especially beneficial for individuals with motor disabilities, allowing them to operate wheelchairs, robotic arms, and home appliances through brain activity. This paper presents the design and implementation of a simplified, affordable EEG-based brain-controlled device.

The system architecture includes an EEG sensor for signal acquisition, a signal processing unit, a microcontroller, and a controlled device. EEG signals are captured from the scalp, filtered to remove noise, and processed to extract features such as alpha and beta frequency bands. These features are classified using threshold-based or machine learning algorithms to determine user intent. The resulting commands are sent to a microcontroller (e.g., Arduino) to control external devices.

Implementation is carried out using MATLAB or Python for signal analysis. Results show that the system can successfully interpret brain patterns with acceptable accuracy and response time suitable for assistive applications. Performance improves with user training and optimized signal processing.

Applications include wheelchair control, robotic arm operation, and gaming/virtual reality. The system enhances user independence, reduces reliance on caregivers, and improves confidence. However, limitations include sensitivity to noise, need for user training, limited command options, user-to-user variability, and dependence on effective signal processing and classification techniques. Future improvements may involve advanced machine learning and deep learning models such as CNNs and LSTMs to enhance accuracy and reliability.

Conclusion

This paper presents a functional EEG-based brain-controlled device that demonstrates the potential of BCI technology in assistive applications.

References

[1] Wolpaw, J. R., et al., “Brain–Computer Interfaces for Communication and Control,” IEEE, 2002. [2] Nicolas-Alonso, L. F., Gomez-Gil, J., “Brain Computer Interfaces, a Review,” Sensors, 2012. [3] Rao, R. P. N., Brain–Computer Interfacing: An Introduction, Cambridge University Press

Copyright

Copyright © 2026 Ashiba Siddique, Smriti Godishela, Nisha Suryanvanshi, Khushi Barange, Balvir Chand, Sandhya Meshram. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.