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An Online BCI System Based in SSVEPs to Control IoT Devices

Abstract:

Internet of Things allows devices to communicate each other and with users. This way, people can control and monitor these devices using mobile applications, voice commands, gestures, among others. It is a technology that makes people’s life easier. However, people with severe physical disabilities cannot take advantage of this technology since the way of controlling smart devices were designed for people without disabilities. In this situation, it is necessary to find other means of communication such as Brain-Computer Interface, which is a technology that seeks to connect the user’s brain activity with any external applications. Steady State Visually Evoked Potentials have been one of the most widely used brain patterns due to its simplicity and precision. In this work, a Brain-Computer Interface system based on SSVEP was built to control IoT devices. The brainwaves generated by the SSVEP stimuli were acquired using a portable electroencephalography device and then classified using Canonical Correlation Analysis algorithm and translated into operational commands to navigate through an application to control different IoT devices using the MQTT protocol. The final prototype was tested on fifteen volunteers, achieving an average accuracy of 97.61%, requiring an average time of 9.7 s to turn on a smart light bulb and 16.68 s to turn it off.