Design and Implementation of Brain-Based Home Automation System

Abstract

This paper supports the utilization of EEG signals to control a smart home automation system. The study involves calculating the human brain's attention level using EEG data and subsequently employing this information to operate various devices based on the attention value obtained. The process commences with multichannel EEG recordings, which are then processed using MATLAB software. The first channel (FP1) is isolated from the multichannel EEG data, and subsequent steps involve noise and artifact removal through a bandpass filter ranging from 0.3 to 100 Hz. The Alpha and Beta sub-bands of the EEG data are computed, and the Power Spectral Density is derived from the Alpha and Beta waves. By analyzing the intensities of the Alpha and Beta PSD signals, the subject's attention level is computed and categorized. This attention level indicator is then used to control the operation of smart home electrical devices. The study demonstrates the viability and effectiveness of the proposed EEG-based system for controlling domestic appliances, confirming its successful functionality.