UC Irvine

Over the past years, technology using electroencephalography (EEG) as a means of controlling

electronic devices has become more innovative. Today, people are able to measure their own brain

waves and patterns outside of medical laboratories. Furthermore, besides analyzing brain signals,

these brain signals can be used as a means of controlling everyday electronic devices, which is also

know as brain-computer interface. Brain-computer interface along with the ``Internet of Things,``

are growing increasingly popular; more and more people have adapted to utilizing wearables and

smart homes. For this thesis, I attempt to explore EEG for an IOT environment, investigate EEG signal, and build a smart applications able to detect different mental tasks using machine learning algorithms. In order to achieve this, this thesis used low cost EEG headset ``NeuroSky Mindwave Mobile,`` Intel Edison and Raspberry Pi 2 as a controller. This thesis uses WuKong IoT framework, in which WuKong application framework provides interoperability of things and Wukong Edge Framework provides reliable streaming support for building intelligence on the edge.