Graduation Year


Document Type




Degree Name

MS in Electrical Engineering (M.S.E.E.)

Degree Granting Department

Electrical Engineering

Major Professor

Anna Pyayt, Ph.D.

Co-Major Professor

Stephen E. Saddow, Ph.D.

Committee Member

Sylvia Thomas, Ph.D.


Analog Processing, Bio-electronics, Concussion, Electroencephalogram, Medical Device


In the United States, approximately 2.8 million Traumatic Brain Injuries (TBI) occur annually. Out of these 2.8 million occurrences, 280,000 injuries are caused by sports and recreational activities. The actual number can be significantly higher since mild TBI are often unrecorded. These injuries not only cause physical damage to players, but they are also one of the leading causes of a player’s retirement from a sports career. While working with TBIs, it is vital to detect the concussion at its first occurrence, termed as a “primary concussion.” If the primary concussion goes undetected, the player may continue to play the game, which makes the player highly susceptible for a secondary concussion. Secondary concussions may lead to “second impact syndrome” which is a major cause of deaths resulting from TBI. Thus, it is vital to precisely detect and diagnose primary concussions within strict time constraints. Currently, Symptoms Check Lists are used as a tool to assess head impact for the possibility of concussion occurrence. Apart from Symptoms Checklist, the Computerized Tomography (CT), Magnetic Resonance Imaging (MRI) scans and standardized EEG tests are used for advanced assessment and diagnosis of a brain injury. The symptoms checklist can only provide the likelihood of a TBI occurrence upon sustained head impact. On the other hand, imaging tests are typically carried out using the advanced medical diagnostic equipment which are available only in hospitals and specialized labs. Hence, state of the art techniques used for brain function monitoring fail to provide real-time detection and diagnosis of a TBI. To counter this issue, we propose a novel system which can monitor the EEG signals of a user in real-time and alert a sports physiologist of any significant disturbances in EEG signal characteristics upon sustaining a head impact event. Even though the developed system is still in the design prototype stage, upon integration as a whole system, the proposed system can be a promising step in real-time detection and diagnosis of a primary concussion in sports players. This is, especially true for American football players who are known to suffer multiple events such as this, often without any awareness to either themselves or their health care professionals. Thus this system has been specifically designed for use by American football players and is to be integrated into their helmet, thus allowing for real-time detection of TBI.