EEG-Based Brain-Controlled Smart System for Human Safety and Automation

This paper presents an innovative Brain-Computer Interface (BCI) based system designed for human safety and smart automation using Electroencephalography (EEG) signals. The proposed system enables direct communication between the human brain and external devices without the need for physical interaction. EEG signals are captured using a non-invasive headset and processed using a threshold-based approach implemented on Arduino Uno. The system interprets brain signals such as attention and relaxation levels and converts them into control commands for safety and automation applications. For example, a high attention level can trigger emergency alerts or activate safety mechanisms, while different signal patterns can control home appliances. This system is especially beneficial for physically disabled individuals, allowing them to interact with their environment independently. Unlike complex machine learning-based BCI systems, the proposed model uses a simple and cost-effective approach, ensuring real-time performance with low computational requirements. Experimental results demonstrate that the system provides reliable response and practical usability in real-world scenarios. This research highlights the potential of EEG-based BCI systems in enhancing human safety, smart living, and assistive technologies.