pybispectra.cfc.AAC

class pybispectra.cfc.AAC(data: ndarray, freqs: ndarray, sampling_freq: int | float, verbose: bool = True)

Class for computing amplitude-amplitude coupling (AAC).

Parameters:

datandarray, shape of [epochs, channels, frequencies, times]

Amplitude (power) of the time-frequency representation of data.

freqsndarray, shape of [frequencies]

Frequencies (in Hz) in data. Frequencies are expected to be evenly spaced.

sampling_freqint | float

Sampling frequency (in Hz) of the data from which data was derived.

verbosebool (default True)

Whether or not to report the progress of the processing.

Attributes:

resultstuple of ResultsCFC

Return the results.

datandarray, shape of [epochs, channels, frequencies, times]

Amplitude (power) of the time-frequency representation of data.

freqsndarray, shape of [frequencies]

Frequencies (in Hz) in data.

sampling_freqint | float

Sampling frequency (in Hz) of data.

verbosebool

Whether or not to report the progress of the processing.

Methods

compute([indices, f1s, f2s, n_jobs])	Compute AAC, averaged over epochs.
copy()	Return a copy of the object.

compute(indices: tuple[tuple[int]] | None = None, f1s: tuple[int | float] | None = None, f2s: tuple[int | float] | None = None, n_jobs: int = 1) → None

Compute AAC, averaged over epochs.

Parameters:

indicestuple of tuple of int, length of 2 | None (default None)

Indices of the seed and target channels, respectively, to compute AAC between. If None, coupling between all channels is computed.

f1stuple of int or float, length of 2 | None (default None)

Start and end lower frequencies to compute AAC on, respectively. If None, all frequencies are used.

f2stuple of int or float, length of 2 | None (default None)

Start and end higher frequencies to compute AAC on, respectively. If None, all frequencies are used.

n_jobsint (default 1)

Number of jobs to run in parallel. If -1, all available CPUs are used.

Notes

AAC is computed as the Pearson correlation coefficient across times for each frequency in each epoch, with coupling being averaged across epochs [1].

AAC is computed between all values of f1s and f2s. If any value of f1s is higher than f2s, a numpy.nan value is returned.

References

[1]

Janet Giehl, Nima Noury, and Markus Siegel. Dissociating harmonic and non-harmonic phase-amplitude coupling in the human brain. NeuroImage, 227:117648, 2021. doi:10.1016/j.neuroimage.2020.117648.

copy()

Return a copy of the object.

property results: ResultsCFC

Return the results.

Returns:

resultsResultsCFC

The results of the AAC computation.