IoBCT: A Brain Computer Interface using EEG Signals for Controlling IoT Devices

Abstract

For people with motor disabilities, completing simple tasks or processes such as turning on lights or directly controlling smart home devices can be tedious and requires considerable thought and effort. Unfortunately, recent advancements in the IoT and AI, which aim to simplify and enhance device interaction, have not been equally accessible to people with motor disabilities. As a result, individuals with severe motor disabilities caused by various conditions such as Spinal Cord Injury (SCI) or Anthropomorphic Lateral Sclerosis (ALS) may be unable to effectively interact with IoT devices or complete tasks without significant effort. To solve this challenge, in this research work, we present a novel brain-computer interface (BCI) framework called the Internet of Brain-Controlled Things (IoBCT) that enables an individual to interact or communicate with IoT devices directly and effectively. Our IoBCT framework uses human brain signals for BCI operations and an optimization methodology for effectively communicating with IoT devices using brain waves. Our experiments demonstrate the effectiveness and feasibility of employing EEG signals for controlling IoT devices with an accuracy rate of 95%.