LECTURER I (PHD), EBONYI STATE UNIVERSITY ABAKALIKI NIGERIA
HOW TO USE THE EEG SIGNALS(BRAIN WAVES) CAPTURED FROM THE BRAIN TO DIAGNOSE AND TREAT STROKE.
The increasing number of deaths related to stroke diseases in Nigeria is growing at an alarming rate. This could be attributed to poor diagnosis as well as lack of financial power to administer and undergo appropriate test as may be recommended by the physician. Most of the neurologists lack access to high-tech tools that are supposed to assist them in diagnosing and treatment of stroke diseases. The scarcely available tools such as Computerized Axial Tomography (CAT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET) are expensive for most of the patients to access. Recent researches have shown that Electroencephalography (EEG), which is highly available and affordable, is valuable in diagnosing stroke disease. The interpretation of the outcome of this reading to the patient is a challenge faced by experts in this field. This research developed a system that helps in interpretation of the EEG reading to the understanding of an ordinary person. Pre-knowledge of the brain situation will alert patient on possible danger and ignite preventive culture that will prevent worse case scenarios. The end result will enable early diagnosis of stroke disease that will help mitigate the death rate of stroke patients in Nigeria. A survey was carried out to ascertain the possibility of interpreting the result of EEG recordings to the patient or ordinary person. A questionnaire was designed and distributed to scholars in computer science, computer related fields of study and medical sciences. Brain signals were captured using Brain Computer Interface (BCI) machine. The result was then interpreted using Visual Studio 2012. The result suggests that the developed application will be a veritable tool in interpretation of EEG recordings with emphasis in its usage for diagnosis and treatment of stroke disease. Results: (i)The survey conducted suggested a need for creating more awareness on the ravaging effect of stroke diseases in our locality and also on the use of EEG technology in aiding diagnosis of stroke diseases. (ii)The brain signals were successfully captured from both the stroke patient and the control subject. (iii)Artificial Neural Network was created, trained and launched using MATLAB 2013a software and the EEG recordings classified into five distinct five bands of delta, theta, alpha, beta and gamma waves based on their frequencies.(iv)EEG classification result was collected for interpretation and the result can be saved for future usage.