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Templin, Sara Elizabeth - One of the best experts on this subject based on the ideXlab platform.

  • Perceptions of Correlations Between Multiple Cues
    1
    Co-Authors: Templin, Sara Elizabeth
    Abstract:

    195 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.Previous research has shown that subjects assess inaccurately correlations between discrete variables. A set of experiments was designed to extend this scenario to the assessment of a third non-focal correlation in a trivariate distribution when the first two focal correlations are provided. Four experiments were designed to: (1) assess the perception of the correlation between cues when subjects are not explicitly shown the cues in a pairwise situation, (2) assess the perception of the correlation between cues when given in a pairwise scenario, (3) assess the perception of the correlation between cues in a pairwise scenario where content is present, and (4) assess the perception of the correlation between cues when altering the Marginal Frequency of the criterion. Results indicate that subjects generally underestimate both the focal and non-focal correlations. An overestimation of the correlation can be induced when the focal correlation is large and the Marginal Frequency favors "Large" values of the criterion. A predictive model was fit to the results, indicating that prediction of the non-focal correlation is assessed not by the largest focal correlation, as expected, but by the focal correlation estimated closest to 0.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Janis E. Johnston - One of the best experts on this subject based on the ideXlab platform.

Messaoud Benidir - One of the best experts on this subject based on the ideXlab platform.

  • Comparative Study of Time Frequency Analysis Application on Abnormal EEG Signals
    2016
    Co-Authors: A. Ridouh, Daoud Boutana, Messaoud Benidir
    Abstract:

    This paper presents a time-Frequency analysis for some pathological Electroencephalogram (EEG) signals. The proposed method is to characterize some pathological EEG signals using some time-Frequency distributions (TFD). TFDs are useful tools for analyzing the non-stationary signals such as EEG signals. We have used spectrogram (SP), Choi-Williams Distribution (CWD) and Smoothed Pseudo Wigner Ville Distribution (SPWVD) in conjunction with Rényi entropy (RE) to calculate the best value of their parameters. The study is conducted on some case of epileptic seizure of EEG signals collected on a known database. The best values of the analysis parameters are extracted by the evaluation of the minimization of the RE values. The results have permit to visualize in time domain some pathological EEG signals. Also, the Rényi Marginal entropy (RME) has been used in order to identify the peak seizure. The characterization is achieved by evaluating the Frequency bands using the Marginal Frequency (MF).

Paul W. Mielke - One of the best experts on this subject based on the ideXlab platform.

A. Ridouh - One of the best experts on this subject based on the ideXlab platform.

  • Comparative Study of Time Frequency Analysis Application on Abnormal EEG Signals
    2016
    Co-Authors: A. Ridouh, Daoud Boutana, Messaoud Benidir
    Abstract:

    This paper presents a time-Frequency analysis for some pathological Electroencephalogram (EEG) signals. The proposed method is to characterize some pathological EEG signals using some time-Frequency distributions (TFD). TFDs are useful tools for analyzing the non-stationary signals such as EEG signals. We have used spectrogram (SP), Choi-Williams Distribution (CWD) and Smoothed Pseudo Wigner Ville Distribution (SPWVD) in conjunction with Rényi entropy (RE) to calculate the best value of their parameters. The study is conducted on some case of epileptic seizure of EEG signals collected on a known database. The best values of the analysis parameters are extracted by the evaluation of the minimization of the RE values. The results have permit to visualize in time domain some pathological EEG signals. Also, the Rényi Marginal entropy (RME) has been used in order to identify the peak seizure. The characterization is achieved by evaluating the Frequency bands using the Marginal Frequency (MF).