A Brain-Computer Interface for Controlling IoT Devices using EEG Signals

Motor disability is the loss of the ability to move a limb of the body. Motor disabilities make difficult the interaction between a disabled person and her/his environment. Recent research has focused on developing innovative technologies that could be used by disabled people to improve their life quality. In this paper, a brain-computer interface for controlling IoT devices is proposed. This system is based on the use of the Muse-Headband sensor which captures EEG signals when a person blinks. This sensor is placed on the forehead of the user of the system. The EEGs are preprocessed and then classified into short and long blinks by computing their signal envelopes and using the k-NN algorithm. The classified blinks are then used to form control commands that are sent to a mosquitto server hosted in the cloud. This server is responsible for sending the control action to the connected IoT devices. The accuracy of the classifier designed in this work is 99.53%. Usability tests show that the probability of a user sending a wrong command, with the proposed system, is 4.83%. However, this probability decreases when the time of use of the system proposed in this work increases.