Nowadays, Internet of Things (IoT) is becoming a major research area, since, its applications can improve people's life. Even when its purpose is to facilitate daily tasks for everyone, it does not fully consider people with disabilities. Even voice or gesture command technologies can help most of people with disabilities, Motor Neuron Diseases (MND) patients who have lost most of their movement cannot be benefitted of such technologies. For this reason, this research aims to design and develop a scalable BCI based home automation system for people with movement disabilities which is easy to use and comfortable for the user. The proposed solution gathers signals generated by voluntary eye blinks from the FP1 electrode position and classify them on short and composed long blinks. Then, a combination of these blinks allows the user to navigate in a Graphic User Interface (GUI) of an application created to control different devices of a smart home via. MQTT protocol. Test results obtained in this work shows that the proposed system could be used in real life solutions.

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Electroencephalography devices such as the OpenBCI Cyton Biosensing board create a noninvasive and inexpensive way of acquiring signals generated by the brain. These signals are influenced by different types of brain stimuli such as eye blinks but they are also includes a large amount of noise, e.g., generated by the board. However, the noise can be removed with the help of proven filters. In this aspect, the intention of this work is to demonstrate how using different type of filters, it is possible to clean the noise from the brain signals acquired using an encephalography devices (such as Cytonbiosensing board) which are generated when a user blinks his/her eyes and classify them in different type of blinks. We have chosen the study of eye blink brain signals, since, they present a wide range of real-life applications. Our model includes a simple algorithm that classifies user-generated eye blinks into short intended blinks and long composed blinks. Experimental results of the proposed model show an accuracy of 96% which enables the development of real-life applications that do not require real-time control such as IoT devices.

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Brain-computer interface is a technology which creates a new way of communication between a person's brain and the external world. To achieve this objective, the brainwaves of a person must be gathered by using specialized devices and then classified in different categories that are associated with specific commands. In the process of brainwave gathering, brain activities of a person can be influenced by different types of stimuli to get the desired results and one of the most important and popular stimuli used in this field is steady state visually evoked potential. Based on this background, this review seeks to show and analyze a series of articles that have been executed around the world related to brain-computer interface applications using steady state visually evoked potential. This review has been executed with the objective of identifying the advantages and limitations of utilizing steady state visually evoked potentials, its main areas of application and the future challenges. Additionally, this review analyzes the different technologies involved to the implementation of state visually evoked potential systems such as signal classification techniques, electroencephalography devices, channels, verification metrics and experimental environments used in the research projects. In summary, this review intends to guide the scientific community about the different aspects involved in conducting research on the development of brain-computer interface applications using electroencephalography devices and steady state visually evoked potential.