Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/234311
Title: Charecterization Of Ocularity In Electroencephalogram
Researcher: JOHN WILLIAM CAREY MEDITHE
Guide(s): Usha Rani Nelakuditi
University: Vignans Foundation for Science Technology and Research
Completed Date: 2017
Abstract: Brain is a control center for various sensory organs in human system. It consists of millions of neurons that coordinate emotion, movement and sensation. The electrical activity produced due to firing of neurons is accumulate over the scalp can be measured using electrodes, known as Electroencephalogram (EEG). EEG is a significant medical imaging tool to interpret the brain activity in form of Alpha, Beta, Theta, Delta and Gamma frequencies. Acquired EEG potentials are in an order of microvolt, which are very much prone to the contaminating with other bio signals and external parameters such as light etc. These undesired bio signals are originated from human organs like eye, muscle and heart etc. are overlapped over the true EEG and forms contaminated EEG. The contaminates present in the EEG are known as artifacts. newlineIn the present research, impact of Electro-Oculo-Gram (EOG) from ocular sensor, light, powered glasses and Visually Evoked Potentials (VEP) on EEG is analyzed using experimental and subjective analysis. Further, the results also verified with the mathematical models and results are cross validated using MEDICAID system. newlineAs ocular sensor is very much nearer to the brain, and also due to involuntary movement of eye, there is lot of possibility in creating artifacts in EEG by EOG. In this connection, existing artifact removal techniques such as ICA, PCA and Wavelet methods are analyzed. But, existing techniques are post processing, doubles the examination time. Hence, a hardware system using NI myRIO processor to detect the ocular artifact in EEG is developed and patented. It can be used with to any bio-potential amplifier with on board LEDs to detect EOG artifacts (blinks), which can be interfaced to any existing standard medical systems. This blink detector is validated on 80subjects with 96.2% accuracy. The blink detector can be used as Driver safety system and Blink controlled home appliance system newline newline
Pagination: 126
URI: http://hdl.handle.net/10603/234311
Appears in Departments:Department of Electronics and Communication Engineering

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