A. Abutaleb, H. Abdelaleem, K. Hewedy



Stochastic Models for the EEG Frequencies

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Vehicle radar has significant importance in autonomous vehicles and modern intelligent transportation network applications. The range and velocity estimation with high precision are one of the challenging tasks in this modern era. This work proposes some valuable improvements in the context of radar signal processiFor each of the frequency bands of the EEG, a stochastic model is assumed. The stochastic model assumes that the frequency follows an Ornstein-Uhlenbeck stochastic differential equation. Using the extended Kalman filter and pseudo maximum likelihood, the parameters of the model are first estimated for the simulated data and proved to be accurate. We then applied the same models, to estimate the frequencies, to real data of EEG signals that were obtained from 8 patients suffering from Epilepsy. The estimated frequencies showed that there is no difference between the right lobe and the left lobe signals, except for the beta band, which agrees with physiological findings.ng, emphasizes the range and Doppler estimation of moving targets which help to estimate the surroundings efficiently. At first, orthogonal frequency division multiplexing (OFDM) waveform is used for data transmission and radar processing while target parameters are efficiently estimated. In the previous research, authors used the approximations for velocity estimation of moving targets while this paper suggests the improvement in Doppler measurement by analyzing the frequencies of all the subcarriers of received echo with extended FFT and the maximum likelihood (ML) estimation algorithm. Moreover, cramer-rao lower bound (CRLB) is utilized to analyze the performance of the estimator. For practical analysis, MR3003 automotive radar is used which follows the frequency division multiplexing (FMCW) signal to detect and analyze the targets. In another context i.e. For some scenarios, the radar sensor leads to the false detection while camera output could not work in harsh weather. To overcome this problem, the amalgamation of both the sensors are used to extract the precise information. In order to perform this task, a deep learning algorithm includes a convolutional neural network (CNN) is implemented for detection and identification of vehicles in an optical video which further accumulates with the MR3003 radar image to identify the moving targets in both scenarios. Furthermore, the performance of the radar algorithms is analyzed in MATLAB while CNN is implemented in python. Theoretical study and experimental results reveal that the derived methods can attain the velocity accuracy and achieve a high resolution- velocity estimation of moving targets.


EEG, Epilepsy, Stochastic Differential Equation, Ornstein-Uhlenbeck Process, Frequency Estimation, Fourier Transform, Extended Kalman Filter


Cite this paper

A. Abutaleb, H. Abdelaleem, K. Hewedy. (2021) Stochastic Models for the EEG Frequencies. International Journal of Signal Processing, 6, 14-32


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