metabci.brainda.algorithms.feature_analysis.freq_analysis module

class metabci.brainda.algorithms.feature_analysis.freq_analysis.FrequencyAnalysis(data, meta, event, srate, latency=0, channel='all')

Bases: object

plot_topomap(data, ch_names, srate=-1, ch_types='eeg')

-author: Zhou hongzhan & He Jiatong -Create on:2022-8-9 -update log:

2022-8-31 by Zhou hongzhan

Parameters:

• data – np.array, 1D array eeg data. The default is [].

• ch_names – list interested channels

• srate – int sample rate. The default is -1.if set as default ,the initial sample ratio will be applied

• ch_types – string Type of channels,default value=’eeg’

power_spectrum_periodogram(x)

-author: Zhou hongzhan & He Jiatong -Create on:2022-8-9 -update log:

2022-8-31 by Zhou hongzhan

Parameters:

x – np.array 1D data.

Returns:

np.array

An array of frequencies

Pxx_dennp.array

The amplitude array respectively correspond to frequency array

Return type:

f

signal_noise_ratio(data=[], srate=-1, T=[], channel=[])

-author: Zhou hongzhan & He Jiatong -Create on:2022-8-9 -update log:

2022-8-31 by Zhou hongzhan

Parameters:

• data – np.array, 1D array eeg data. The default is [].

• srate – int sample rate. The default is -1.if set as default ,the initial sample ratio will be applied

• T – int, ms the during time of data. The default is [].

• channel – string interested channels

Returns:

np.array

frequency sequece.

snrnp.array

SNR sequence

Return type:

X1

stacking_average(data=[], _axis=0)

-author: Zhou hongzhan -Create on:2022-8-9 -update log:

2022-8-11 by Zhou hongzhan

Parameters:

• data – np.array (nTrials, nChannels, nTimes) EEG origin data. The default is [].

• _axis – int The dimension need to be stacked. The default is 0.

Returns:

np.array

The data after stacked.

Return type:

data_mean

sum_y(x, y, x_inf, x_sup)

-author: Zhou hongzhan -Create on:2022-8-9 -update log:

2022-8-11 by Zhou hongzhan

Parameters:

• x – np.array(1D) An array of frequencies

• y – np.array(1D,SAME TYPE WITH X) The amplitude array respectively correspond to frequency array

• x_inf – int Infimum of freq.

• x_sup – int Supremum of freq.

Returns:

int

freq parameter,topomap procedure needed

Return type:

np.mean(sum_A)