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IEEE Latin America Transactions Submission 9006

The repository accessed is a repository dedicated to storing all the codes used in the work published as "Comparison of sequential test strategies based on Monte Carlo simulations in the detection of auditory steady-state responses"

Authors: Victor H. de S. S. Ragazzi (https://orcid.org/0009-0007-0262-1002) Alexandre G. Caldeira (https://orcid.org/0000-0002-4851-3417) Patrícia N. Vaz (https://orcid.org/0000-0003-4503-6288) Felipe Antunes (https://orcid.org/0000-0001-5913-9777) Leonardo B. Felix (https://orcid.org/0000-0002-6184-2354)

If you have any questions, please contact the first author by email: [email protected]

parametros2015 - Generates the possible Mstep variations for optimizing the parameters of Mario Cebulla's 2015 strategy

paretoFront - Calculates the Pareto curve in the "eeg signal" script

protocolo_deteccao - Applies the 2005 Sturzbecher method to the eeg data during the "eeg signal" script

protocolo_deteccao_qsample - Applies the 2006 Mario Cebulla method to the eeg data during the "eeg signal" script

protocolo_deteccao_qsample_2013 - Applies the 2013 Sturzbecher method to the eeg data during the "eeg signal" script

protocolo_deteccao_qsample_2015 - Applies the 2015 Mario Cebulla method to the eeg data during the "eeg signal" script eeg". In the script, it is possible to choose whether or not to apply the proposed modification to the study by commenting on the lines of code shown.

sinal_eeg - Script responsible for running the main code, processing the data, applying the methods and generating the results.

teste_esta - Application of statistical tests to the results, the results must be saved from the "sinal eeg" script, this step was done manually.

q_sample - Script that generates the critical values ​​used in the methods from the simulated data "Rm" and "ray"

SOME VARIABLE SAVING PROCESSES WERE DONE MANUALLY FOR EXECUTING THE SCRIPTS, THEREFORE, TO RUN SOME CODES IT IS NECESSARY TO SAVE THE VARIABLES AND CHECK IF THEIR NAMES ARE CORRESPONDING ALL CODES WERE DEVELOPED IN MATLAB VERSION 2020a, THE AUTHORS DO NOT GUARANTEE THAT THE CODES WILL WORK IN OTHER VERSIONS

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Python notebooks I used for active learning / test stuff out while learning statistical signal processing

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