brainda.algorithms.feature_analysis package

Submodules

brainda.algorithms.feature_analysis.freq_analysis module

class 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)

brainda.algorithms.feature_analysis.time_analysis module

class brainda.algorithms.feature_analysis.time_analysis.TimeAnalysis(data, meta, dataset, event, latency=0.0, channel=[0])

Bases: object

Chan_Neuroscan = ['FP1', 'FPZ', 'FP2', 'AF3', 'AF4', 'F7', 'F5', 'F3', 'F1', 'FZ', 'F2', 'F4', 'F6', 'F8', 'FT7', 'FC5', 'FC3', 'FC1', 'FCZ', 'FC2', 'FC4', 'FC6', 'FT8', 'T7', 'C5', 'C3', 'C1', 'CZ', 'C2', 'C4', 'C6', 'T8', 'M1', 'TP7', 'CP5', 'CP3', 'CP1', 'CPZ', 'CP2', 'CP4', 'CP6', 'TP8', 'M2', 'P7', 'P5', 'P3', 'P1', 'PZ', 'P2', 'P4', 'P6', 'P8', 'PO7', 'PO5', 'PO3', 'POZ', 'PO4', 'PO6', 'PO8', 'CB1', 'O1', 'OZ', 'O2', 'CB2']
Chan_Standard1020 = ['Fp1', 'Fpz', 'Fp2', 'AF3', 'AF4', 'F7', 'F5', 'F3', 'F1', 'Fz', 'F2', 'F4', 'F6', 'F8', 'FT7', 'FC5', 'FC3', 'FC1', 'FCz', 'FC2', 'FC4', 'FC6', 'FT8', 'T7', 'C5', 'C3', 'C1', 'Cz', 'C2', 'C4', 'C6', 'T8', 'M1', 'TP7', 'CP5', 'CP3', 'CP1', 'CPz', 'CP2', 'CP4', 'CP6', 'TP8', 'M2', 'P7', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6', 'P8', 'PO7', 'PO5', 'PO3', 'POz', 'PO4', 'PO6', 'PO8', 'I1', 'O1', 'Oz', 'O2', 'I2']
average_amplitude(data=[], time_start=0, time_end=1)

-author: Jiang Hanzhe -Created on: 2022-8-8 -updata log:

2022-8-15 by Jiang Hanzhe

Parameters:

  • data (ndarray) – the EEG data, by default []

  • time_start (int) – beginning of peak seeking, by default 0

  • time_end (int) – end of peak seeking, by default 1

Returns:

signal average amplitude within the specified time quantum

Return type:

ave_amp(float)

average_latency(data=[], time_start=0, time_end=1)

-author: Wu Jieyu -Created on: 2022-8-8 -updata log:

2022-8-15 by Wu Jieyu

Parameters:

  • data (ndarray) – the EEG data, by default []

  • time_start (int) – beginning of peak seeking, by default 0

  • time_end (int) – end of peak seeking, by default 1

Returns:

location of average amplitude ave_amp(float): signal average amplitude within the specified time quantum

Return type:

ave_loc(int)

get_chan_id(ch_name, channels)

-author: Wu Jieyu -Created on: 2022-8-8 -updata log:

2022-8-15 by Wu Jieyu

Parameters:

  • ch_name (list) – selected channels

  • channels (list) – standard channel list

Returns:

index of ch_name in channels

Return type:

Chan_ID(list)

peak_amplitude(data=[], time_start=0, time_end=1)

-author: Jiang Hanzhe -Created on: 2022-8-8 -updata log:

2022-8-15 by Jiang Hanzhe

Parameters:

  • data (ndarray) – the EEG data, by default []

  • time_start (int) – beginning of peak seeking, by default 0

  • time_end (int) – end of peak seeking, by default 1

Returns:

signal peak amplitude within the specified time quantum

Return type:

peak_amp(float)

peak_latency(data=[], time_start=0, time_end=1)

-author: Wu Jieyu -Created on: 2022-8-8 -updata log:

2022-8-15 by Wu Jieyu

Parameters:

  • data (ndarray) – the EEG data, by default []

  • time_start (int) – beginning of peak seeking, by default 0

  • time_end (int) – end of peak seeking, by default 1

Returns:

location of peak amplitude peak_amp(float): signal peak amplitude within the specified time quantum

Return type:

peak_loc(int)

plot_multi_trials(data, sample_num, axes=None)

-author: Wu Jieyu -Created on: 2022-8-8 -updata log:

2022-8-15 by Wu Jieyu

Parameters:

  • data (ndarray) – the EEG data

  • sample_num (int) – total number of sampling points in data

  • axes (axessubplot) – drawing area

Returns:

drawing area

Return type:

ax(axessubplot)

plot_single_trial(data, sample_num, axes=None, amp_mark=False, time_start=0, time_end=1)

-author: Wu Jieyu -Created on: 2022-8-8 -updata log:

2022-8-15 by Wu Jieyu

Parameters:

  • data (ndarray) – the EEG data

  • sample_num (int) – total number of sampling points in data

  • axes (axessubplot) – drawing area

  • amp_mark (string) – ‘peak’ or ‘average’, call different peak marking methods, by default False (not marked)

  • time_start (int) – beginning of peak seeking, by default 0

  • time_end (int) – end of peak seeking, by default 1

Returns:

location of amplitude amp(float): signal amplitude within the specified time quantum ax(axessubplot): drawing area

Return type:

loc(int)

plot_topomap(data, point, channels, fig, srate=-1, ch_types='eeg', axes=None)

-author: Wu Jieyu -Created on: 2022-8-8 -updata log:

2022-8-15 by Wu Jieyu

Parameters:

  • data (ndarray) – the EEG data

  • point (int) – selected sampling point

  • channels (list) – selected channels

  • fig (figure) – figure

  • srate (int) – sampling rate, by dafault self.fs

  • ch_types (list of str | str) – channel types, by default ‘eeg’

  • axes (axessubplot) – drawing area, by default none

Returns:

drawing area

Return type:

aximage(axessubplot)

stacking_average(data=[], _axis=[0])

-author: Jiang Hanzhe -Created on: 2022-8-8 -updata log:

2022-8-15 by Jiang Hanzhe

Parameters:

  • data (ndarray) – the EEG data, by default []

  • _axis (list) – selected the dimensions, by default [0]

Returns:

array of averaged signals

Return type:

data_mean(ndarray)

brainda.algorithms.feature_analysis.time_freq_analysis module

class brainda.algorithms.feature_analysis.time_freq_analysis.TimeFrequencyAnalysis(fs)

Bases: object

fun_hilbert(X, N=None, axis=-1)

-author: Li Xiaoyu -Created on: 2022-8-8 -updata log:

2022-8-11 by Li Xiaoyu

Parameters:

  • X (ndarray) – the input data

  • N (int) – length of the hilbert used.

Returns:

discrete-time analytic signal realEnv(ndarray): the real part of the discrete-time analytic signal. imagEnv(ndarray): the imaginary part of the discrete-time analytical signal. angle(ndarray): the angle of the discrete-time analytical signal. envModu(ndarray): the envelope of the discrete-time analytical signal

Return type:

analytic_signal(ndarray)

fun_stft(data, fs=None, window='hann', nperseg=256, noverlap=None, nfft=None, detrend=False, return_onesided=True, boundary='zeros', padded=True, axis=-1)

-author: Xin Fengran -Created on: 2022-8-8 -updata log:

2022-8-11 by Xin Fengran

Parameters:

  • data (but for complex) – the EEG data

  • fs (float) – the rasampling rate

  • window (str or tuple or ndarray) – desired window to use.

  • nperseg (int) – length of each segment.

  • noverlap (int) – number of points to overlap between segments.

  • nfft (int) – length of the FFT used.

  • detrend (str or function or False) – specifies how to detrend each segment.

  • return_onesided (bool) – If True, return a one-sided spectrum for real data. If False return a two-sided spectrum.

  • True (Defaults to) –

  • data –

  • returned. (a two-sided spectrum is always) –

  • boundary (str) – Specifies whether the input signal is extended at both ends, and how to generate the new values,

  • point. (in order to center the first windowed segment on the first input) –

  • padded (bool) – Specifies whether the input signal is zero-padded at the end.

  • axis (int) – axis along which the STFT is computed

Returns:

array of sample frequencies t(ndarray): array of segment times Zxx(ndarray): the STFT of the EEG data

Return type:

f(ndarray)

fun_topoplot(X, chan_names, sfreq=None, ch_types='eeg')

-author: Li Xiaoyu -Created on: 2022-8-8 -updata log:

2022-8-11 by Li Xiaoyu

Parameters:

  • X (ndarray) – the input data

  • chan_names (list) – the name of channels

  • sfreq (float) – the sampling rate

  • ch_types (str) – the type of channel

func_morlet_wavelet(data, xtimes, omega, sigma, fs=None)

-author: Xin Fengran -Created on: 2022-8-8 -update log:

2022-8-11 by Xin Fengran

Parameters:

  • data – ndarray(Nchannel, Ntimes), the EEG data

  • xtimes – ndarray(N,), timeline of the EEG data

  • omega – float.

  • sigma – float.

  • fs – float, the resampling rate

Returns:

ndarray(Nchannel, N, nTimes), square amplitude of Morlet wavelet transform S: ndarray(Nchannel, N, nTimes), complex values of Morlet wavelet transform

Return type:

P

Module contents