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Related Experiment Video

Updated: Jun 9, 2025

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

Published on: April 18, 2025

Real-Time Home Automation System Using BCI Technology.

Marius-Valentin Drăgoi¹, Ionuț Nisipeanu¹, Aurel Frimu¹

¹Faculty of Engineering in Foreign Languages, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania.

Biomimetics (Basel, Switzerland)

|October 25, 2024

Summary

This summary is machine-generated.

This study introduces an EEG-based Brain-Computer Interface (BCI) system for home automation. The developed system empowers individuals with disabilities to control home devices like doors and lights using brain signals.

Keywords:

BCI EEG Raspberry Pi biometric disabled people home security

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Area of Science:

Neuroscience
Biomedical Engineering
Computer Science

Background:

Brain-Computer Interfaces (BCIs) translate brain signals into commands for external devices.
BCIs are crucial for restoring or replacing functions lost due to neuromuscular disorders.
Existing BCIs control various devices, but home automation integration for disabled individuals requires further development.

Purpose of the Study:

To propose and develop an EEG-based BCI system for controlling home automation devices.
To enhance the independence and engagement of individuals with paralysis in their home environment.
To create a functional prototype for controlling a door and a light using brain signals.

Main Methods:

Utilized electroencephalography (EEG) via the EMOTIV Insight™ headset to capture brain signals.
Implemented a Raspberry Pi 4 for signal processing and device control.
Integrated a servo motor for door operation and an LED for lighting control.
Developed a Flutter application for smartphone notifications and system interaction.

Main Results:

Successfully demonstrated control of a door and a light using EEG-based BCI.
The prototype system effectively bypassed traditional neuromuscular pathways.
A companion Flutter application provided real-time status updates and notifications.

Conclusions:

EEG-based BCIs offer a viable solution for home automation control for individuals with disabilities.
This technology can significantly improve the quality of life for people with paralysis.
The developed system provides a foundation for more advanced and integrated smart home solutions for assistive technology.