macro_eeg_model.evaluation.peak_tester

macro_eeg_model.evaluation.peak_tester#

Classes#

PeakTester

A class responsible for testing the significance of peak power values compared to other frequency ranges.

Module Contents#

class macro_eeg_model.evaluation.peak_tester.PeakTester(frequencies, peaks_range, others_range)[source]#

A class responsible for testing the significance of peak power values compared to other frequency ranges.

frequencies#

The array of frequencies corresponding to the power spectrum.

Type:

numpy.ndarray

peaks_range#

The range of frequencies where peaks are expected.

Type:

tuple

others_range#

The range of frequencies where other values are expected.

Type:

tuple

powers#

The epoched power spectrum of the simulated EEG data.

Type:

list

peak_values#

The mean power values in the peak range for each epoch.

Type:

list

other_values#

The mean power values in the other range for each epoch.

Type:

list

__init__(frequencies, peaks_range, others_range)[source]#

Initializes the PeakTester class with the provided frequency ranges.

Parameters:

  • frequencies (numpy.ndarray) – The array of frequencies corresponding to the power spectrum.

  • peaks_range (tuple) – The range of frequencies where peaks are expected.

  • others_range (tuple) – The range of frequencies where other values are expected.

compute_test_result(simulation_name, epoched_powers)[source]#

Computes the statistical test result for the peak power values compared to other frequency ranges.

Parameters:

  • simulation_name (str) – The name of the simulation. (should include “pink” if the data was simulated with pink noise)

  • epoched_powers (list) – The epoched power spectrum of the simulated EEG data.

Returns:

A tuple containing:

  • frequencies (numpy.ndarray): The array of frequencies corresponding to the power spectrum.

  • mean_power (numpy.ndarray): The mean power spectrum across epochs of the simulated EEG data.

  • p_value (float): The calculated p-value.

  • test_name (str): The name of the statistical test used.

Return type:

tuple

_separate_peaks(power)[source]#

Separates the power values in the peak and other frequency ranges.

Parameters:

power (numpy.ndarray) – The power spectrum of the simulated EEG data.

_detrend_data(powers, is_pink)[source]#

Detrend the pink noise in the power spectrum by fitting a power-law trend and removing it.

Parameters:

  • powers (numpy.ndarray) – The power spectrum of the simulated EEG data.

  • is_pink (bool) – A flag indicating whether the data was simulated with pink noise.

Returns:

A tuple containing:

  • non_zero_freqs (numpy.ndarray): The array of non-zero frequencies.

  • flattened_powers (numpy.ndarray): The corresponding detrended power spectrum

Return type:

tuple

_choose_and_run_test(paired=True)[source]#

Automatically selects and runs the correct statistical test based on data characteristics.

Parameters:

paired (bool) – A flag indicating whether the data is paired or independent.

Returns:

A tuple containing:

  • t_stat (float): The calculated t-statistic.

  • p_value (float): The calculated p-value.

  • test_name (str): The name of the statistical test used.

Return type:

tuple

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