Frequency identification for MIT detection of EEG based on wavelet energy

In current applications of magnetic induction tomography (MIT) in brain functional imaging, brain tissue is assumed as medium with homogeneous conductivity, and the electroencephalogram (EEG) is neglected. In order to study the effects of EEG signals on MIT detection results, a 2-D four-layer brain model is established by finite element simulation. Izhikevich neuron model is employed to simulate electrical activities of neurons. Three kinds of typical neurons electrical activities are discussed as internal signals. They are regular spiking, fast-spiking, thalamo-cortical for depolarization, respectively. Wavelet energy combined with FFT method is used to analyze the identification of the detection results for the signal with different patterns. The results show that, EEG is detectable by MIT. According to processing results by the wavelet energy combined with FFT method, the patterns of neurons activity signals can be identified through the frequency components.