A novel system architecture for brain controlled IoT enabled environments

Abstract

Brain Computer Interface (BCI) has recently gained much popularity due to plethora of its applications. In this paper, we propose a novel system architecture to utilize brain signals for controlling Internet of Things enabled environments. The proposed architecture aids in translating brain signals to commands that interact with or control the environment using IoT actuation networks thus executing user desired actions. It comprises of novel, low complex and low power intelligent signal processing architecture for detection of voluntary eye blinks by isolating involuntary eye blinks and IoT enabled wireless actuation network for controlling the environment using commands generated from EEG signal. For the real time performance analysis of the proposed architecture, we developed a wearable device which acquires dual channel EEG using electrodes at Fp1 and Fp2 locations. From the acquired EEG data, the device detects the voluntary eye blinks of the patient and use this information in controlling the environment such as switching HVAC system, lighting or electric fan etc. Performance analysis shows that the proposed intelligent signal processing architecture detects the voluntary eye blinks with 95.2% accuracy when tested on 10 subjects with a low power consumption of 165 mW.