The Experts below are selected from a list of 2073 Experts worldwide ranked by ideXlab platform

Mohammad Soleimani - One of the best experts on this subject based on the ideXlab platform.

  • EEGsig machine learning-based Toolbox for End-to-End EEG Signal Processing.
    arXiv: Signal Processing, 2020
    Co-Authors: Fardin Ghorbani, Ali Abdolali, Soheil Hashemi, Mohammad Soleimani
    Abstract:

    In the quest to realize comprehensive EEG Signal Processing Toolbox, in this paper, we demonstrate the first Toolbox contain three states of EEG Signal Processing (preProcessing, feature extraction, classification) together. Our goal is to provide a comprehensive Toolbox for EEG Signal Processing. Using MATLAB software, we have developed an open-source Toolbox for end-to-end Processing of the EEG Signal. As we know, in many research work in the field of neuroscience and EEG Signal Processing, we first clear the Signal and remove noise, artifact, etc. Which we know as preProcessing, and then extract the feature from the relevant Signal, and finally Machine learning classifiers used to classification of Signal. We have tried to provide all the above steps in the form of EEGsig as a graphical user interface(GUI) so that there is no need for programming for all the above steps and reduce the time to complete these projects to a desirable level.

Fardin Ghorbani - One of the best experts on this subject based on the ideXlab platform.

  • EEGsig machine learning-based Toolbox for End-to-End EEG Signal Processing.
    arXiv: Signal Processing, 2020
    Co-Authors: Fardin Ghorbani, Ali Abdolali, Soheil Hashemi, Mohammad Soleimani
    Abstract:

    In the quest to realize comprehensive EEG Signal Processing Toolbox, in this paper, we demonstrate the first Toolbox contain three states of EEG Signal Processing (preProcessing, feature extraction, classification) together. Our goal is to provide a comprehensive Toolbox for EEG Signal Processing. Using MATLAB software, we have developed an open-source Toolbox for end-to-end Processing of the EEG Signal. As we know, in many research work in the field of neuroscience and EEG Signal Processing, we first clear the Signal and remove noise, artifact, etc. Which we know as preProcessing, and then extract the feature from the relevant Signal, and finally Machine learning classifiers used to classification of Signal. We have tried to provide all the above steps in the form of EEGsig as a graphical user interface(GUI) so that there is no need for programming for all the above steps and reduce the time to complete these projects to a desirable level.

Jinsheng Ning - One of the best experts on this subject based on the ideXlab platform.

  • butterworth low pass filter for Processing inertial navigation system raw data
    Journal of Surveying Engineering-asce, 2004
    Co-Authors: Hang Guo, Jingnan Liu, Jinsheng Ning
    Abstract:

    This article tries to apply the Butterworth low-pass filter to inertial navigation system (INS) data Processing. A set of INS heading data of a practical example has been calculated and the filtering tested. This filtering method is used to examine the correction of the results made by Guo and Wang. Based on the discrete Fourier transformation and the digital filter design technique, a Butterworth low-pass digital filter has been designed and performed to process INS heading raw data. The design of a special Butterworth filter includes determination of the technical specification of the filter and of the response function in the time domain or the frequency domain (determination of the order and cutoff frequency of the digital filter). For the high-frequency noise of INS raw data, a Butterworth low-pass filter has been designed to obtain a more accurate INS heading. The choice of the parameters and orders of the filter is also discussed in the paper. Finally, an example of INS heading data calculation is presented. The Signal-Processing Toolbox of MATLAB software is used for calculating and mapping the results. The result (RMS) is at the level of 0.01 degrees. The method described here is simpler than Kalman filtering or the smoothing method and has sufficient accuracy for INS Processing. It also provides ideas for some other series of raw data with high-frequency noise.

J H Mcclellan - One of the best experts on this subject based on the ideXlab platform.

  • chebyshev digital fir filter design
    Signal Processing, 1999
    Co-Authors: Lina J Karam, J H Mcclellan
    Abstract:

    Abstract A new multiple-exchange ascent algorithm is presented for designing optimal Chebyshev digital FIR filters with arbitrary magnitude and phase specifications. Compared to existing Chebyshev design techniques, the new design algorithm exhibits faster convergence while maintaining high accuracy, and is guaranteed to converge to the optimal solution. In addition, the proposed algorithm exactly reduces to the classic second Remez (Parks–McClellan) algorithm when real-only or imaginary-only filters are designed and is, therefore, a generalization of the classic Remez algorithm to the complex case. The described algorithm has been incorporated as part of the MATLAB Signal Processing Toolbox. Design examples are presented to illustrate the performance of the proposed algorithm.

Ali Abdolali - One of the best experts on this subject based on the ideXlab platform.

  • EEGsig machine learning-based Toolbox for End-to-End EEG Signal Processing.
    arXiv: Signal Processing, 2020
    Co-Authors: Fardin Ghorbani, Ali Abdolali, Soheil Hashemi, Mohammad Soleimani
    Abstract:

    In the quest to realize comprehensive EEG Signal Processing Toolbox, in this paper, we demonstrate the first Toolbox contain three states of EEG Signal Processing (preProcessing, feature extraction, classification) together. Our goal is to provide a comprehensive Toolbox for EEG Signal Processing. Using MATLAB software, we have developed an open-source Toolbox for end-to-end Processing of the EEG Signal. As we know, in many research work in the field of neuroscience and EEG Signal Processing, we first clear the Signal and remove noise, artifact, etc. Which we know as preProcessing, and then extract the feature from the relevant Signal, and finally Machine learning classifiers used to classification of Signal. We have tried to provide all the above steps in the form of EEGsig as a graphical user interface(GUI) so that there is no need for programming for all the above steps and reduce the time to complete these projects to a desirable level.