Rutgers ECE Capstone – Spring 2014 Design Abstract
Utilizing EEG Signals for Wireless BCI Applications
Group Members: David Andres Pacheco, Timothy Joo
Group Advisor: Laleh Najafizadeh
Motivation:
Our interest in this design project stems from a desire to help disabled people that have
difficulty in performing simple tasks in their homes. In order to fulfill this, we want to create a
user-friendly, mind-control device that does just that. We also find this field of study to be an
exciting one, where we can combine what we learned last semester in our Introduction to
Neuroimaging class with our background in the study of electronics. We aspire to further explore
the area of neuroscience in an engineering aspect as well was what it could mean for future
engineers.
In recent years, there are many researchers who have been investigating new
techniques to control electronic devices without the use of hands, or any physical movement for
that matter. There are many companies who are already in the midst of developing products
that do this such as EmotivEPOC and OpenViBE. This concept is known as a Brain-Computer
Interface (BCI), which creates a direct communication between the mind and an external device.
In our BCI, we are going to attempt to use a non-invasive method, called
electroencephalography (EEG) that enables us to record differences in voltages between
neurons. The collected data allows us to analyze and process brain activity to be utilized in
“telling” what an external device should do. In order to process this data, we will use already
developed software such as MATLAB and the Arduino software that can be found on their
website. This will be further discussed later.
Design:
In our design, we utilize EEG activity made from eye blinking to cause an action from an
external device. Over the studies and recently conducted projects we reviewed, we found it is
believed that eye-blinks are one of the mechanisms that most researchers observe to help
disabled people do their everyday activities. Essentially, most disabled people are still able to
control their blinking. According to one paper, eye-blinks can be classified into three types:
reflexive, voluntary and spontaneous. It is understood that voluntary eye blinking creates the
most clear and detectable signals. This will be useful to us in planning how we can initiate a
certain action. Eye-blinks are typically characterized by peaks with relatively strong voltages.
They can also be located by setting a threshold and classifying any activity exceeding the
threshold value as eye-blinks. Three blinks made within a one second time interval by the user
will toggle on or off a green LED. This green LED will symbolize any external device that a
disabled person may have trouble turning on and off.
Goals:
Ultimately, our goal is to generate more interest among other people to further develop
this idea. Hopefully, others will find motivation from our project to create more advanced BCIs
that are capable to help disabled people in useful ways beyond just for televisions or home
lighting systems.