mne_nirs.preprocessing.quantify_mayer_fooof#

mne_nirs.preprocessing.quantify_mayer_fooof(raw, num_oscillations=1, centre_frequency=0.01, extra_df_fields=None, fmin=0.001, fmax=1, tmin=0, tmax=None, n_fft=400, n_overlap=200, peak_width_limits=(0.5, 12.0))[source]#

Quantify Mayer wave properties using FOOOF analysis.

The Fitting Oscillations & One Over F (FOOOF) [1] is utilised to estimate Mayer wave oscillation parameters as described in [2].

The FOOOF algorithm is applied to the mean PSD estimate of the data, the oscillation closest to the centre_frequency is assumed to be the Mayer wave oscillation. The parameters for this oscillation are returned as a dataframe. You can return multiple closest oscillations to the centre_frequency by increasing the num_oscillations parameter.

Parameters:
rawinstance of Raw

The haemoglobin data.

num_oscillationsnumber

Number of parameterised oscilations to be returned. These are selected in increasing distance from the centre_frequency.

centre_frequencynumber

Centre frequency of the Mayer wave.

extra_df_fieldsnumber

Dictionary of values to be appended to the dataframe.

fminfloat

Min frequency of interest.

fmaxfloat

Max frequency of interest.

tminfloat | None

Min time of interest.

tmaxfloat | None

Max time of interest.

n_fftint

The length of FFT used, must be >= n_per_seg (default: 256). The segments will be zero-padded if n_fft > n_per_seg. If n_per_seg is None, n_fft must be <= number of time points in the data.

n_overlapint

The number of points of overlap between segments. Will be adjusted to be <= n_per_seg. The default value is 0.

peak_width_limitstuple of (float, float), optional, default: (0.5, 12.0)

Limits on possible peak width, in Hz, as (lower_bound, upper_bound). As used by FOOOF.

Returns:
dfDataFrame

Dataframe with columns.

References

Examples using mne_nirs.preprocessing.quantify_mayer_fooof#

Mayer Wave Parametrisation

Mayer Wave Parametrisation