Data Acquisition System Design Using a Microcontroller and Digital Elliptic Filters that are able to Remove Noise from EEG signals

Since Electroencephalogram (EEG) signals have very small magnitude, it's very hard to capture these signals without having noise (produced by surrounding artifacts) affect the real EEG signals, so it is necessary to use Filters to remove noise. This work proposes a design of an electronic circuit using a microcontroller, an instrumentation amplifier and an operational amplifier able to capture EEG signals, convert the captured signals from analog state to digital one and send the converted signal (digital signal) to a group of three digital filters. This paper gives a design of three digital elliptic filters ready to be used in real time filtering of EEG signals (which preliminary represents the condition of the brain) making the software part which complements the hardware part in the EEG signals capturing system. Finally we are going to show the way of using the designed electronic circuit with the three designed digital filters, demonstrate and discuss the results of this work. We have used Eagle 6.6 software to design and draw the circuit, CodeVision AVR 3.12 software to write the program downloaded on the microcontroller, Mathworks MATLAB 2014a software to design the three digital filters and Mathworks MATLAB 2014a Simulink tool to make the appropriate experiments and get the results.

Using DWT and ANN. for the Automated Detection of Epileptic Seizures in Scalp EEG

Epilepsy is a chronic neurological disorder that occurs in the brain، and affects approximately 2% of people around the world، where epilepsy patients face a lot of difficulties in everyday life due to the occurrence of seizures. Electroencephalogram (EEG) is used in the automated detection of epileptic seizures، which has Characteristics of non-linear and non-stationary. In this research، we conducted automated detection of the seizures from the scalp EEG signals using a Level 5 Discrete Wavelet Transforms DWT to analyze the signal and extracting statistical features (maximum، minimum، mean، average ، standard deviation، the ratio between the mean values) and Categorizing using artificial neural networks ANN for classification. The suggested detection method has 89.85% detection accuracy with 90.60% sensitivity ، and 89.1% specificity.