A hybrid EEG and head motion system for smart home control for disabled people

This paper presents a hybrid electroencephalography (EEG)-based brain-computer interface system combined with head motion sensing for smart home control to assist the elderly and disabled. The system mainly includes an EMOTIV Insight headset used to extract the user’s EEG data and head motion, an Android application, and an Arduino Uno that controls the appliances. The Android application is wirelessly connected via Bluetooth to the headset and the Arduino Uno through an HC-06 module. The application uses the blink, attention level, and head motion data to allow the user to turn on and off the desired appliance. Various analyses are performed to evaluate the effect of attention and blink on the extracted brain wave signals. In addition, the accelerometer’s data was used to detect the head motion and control the application in combination with the EEG data. Double blink detection achieved an accuracy of 90% whereas the active attention level detection achieved a 75% accuracy. A 100% accuracy was achieved when detecting upward and downward motion whereas an 85% accuracy was achieved for the left and right motions. Finally, as a proof of concept, the developed system was successfully used to control four different home appliances. The successful outcomes of the proposed system demonstrate that it can be easily implemented into home automation to assist disabled and elderly people due to its ease of use, portability, low cost, and expandable circuitry. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.