In MNE time frequency is improtant for any further analysis for MEG EEG and fMRI
here I address some from the toturials just for a remark.

1. Time Frequency functions in MNE


all the output data will be the following two format  you could get the details by X.data:
AverageTFR(info, data, times, freqs, nave[, …])	Container for Time-Frequency data.
EpochsTFR(info, data, times, freqs[, …])	Container for Time-Frequency data on epochs.

%% here most we will use psd_welch for psd and tfr_morlet and ftr_multitaper for time frequency analysis. 
csd_epochs(epochs[, mode, fmin, fmax, fsum, …])	Estimate cross-spectral density from epochs.
psd_welch(inst[, fmin, fmax, tmin, tmax, …])	Compute the power spectral density (PSD) using Welch’s method.
psd_multitaper(inst[, fmin, fmax, tmin, …])	Compute the power spectral density (PSD) using multitapers.
fit_iir_model_raw(raw[, order, picks, tmin, …])	Fit an AR model to raw data and creates the corresponding IIR filter.
tfr_morlet(inst, freqs, n_cycles[, use_fft, …])	Compute Time-Frequency Representation (TFR) using Morlet wavelets.
tfr_multitaper(inst, freqs, n_cycles[, …])	Compute Time-Frequency Representation (TFR) using DPSS tapers.
tfr_stockwell(inst[, fmin, fmax, n_fft, …])	Time-Frequency Representation (TFR) using Stockwell Transform.


%% the ourput of the functions could be 'pow' 'itc''complex' 'csd'

PSD plot

epochall['left'].plot_psd(fmin=2., fmax=40.)  %% 2-40 hz

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epochall['left'].plot_psd_topomap(ch_type='eeg', normalize=True)

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power.plot([7], baseline=(-0.5, 0), mode='logratio', title=power.ch_names[7])
power.plot([11], baseline=(-0.5, 0), mode='logratio', title=power.ch_names[11])
power.plot_topo(baseline=(-0.5, 0), mode='logratio', title='Average power')

fig, axis = plt.subplots(1, 2, figsize=(7, 4))
power.plot_topomap(ch_type='eeg', tmin=0.5, tmax=1.5, fmin=8, fmax=12,
                   baseline=(0,0.5), mode='logratio', axes=axis[0],
                   title='Alpha', vmax=0.45, show=False)
power.plot_topomap(ch_type='eeg', tmin=0.5, tmax=1.5, fmin=13, fmax=25,
                   baseline=(0, 0.5), mode='logratio', axes=axis[1],
                   title='Beta', vmax=0.45, show=False)
mne.viz.tight_layout()
plt.show()

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itc.plot_topo(title='Inter-Trial coherence', vmin=0., vmax=1., cmap='Reds')

Marks:

when there isa parameter: average ==True

we will get the time frequency data shape as(22, 24, 417) (channel fres timepoint)

if they are FALSe the time freq datashape will be (9, 22, 24, 417) here 9 means epochs

then we have a general idear how to put the time frequency data in to cnn and rnn for deep learning part.

for (9, 22, 24, 417)

here we have 9 samples
each sample with 22channels
each channels has 24 freq unit with 417 time length.

in keras the LSTM model has the input as [batch_szie, timestep,input_dim], how could we map the time freq into LSTM cell ?

we have one more dim than the standerd input data.

anyway we have lot of choices such like a stacked RNN just as the brain singel is going on there.

I had tried some RNN in ERP data, sorry that the result is terrible far less than LDA SVM with CSP.

anyway keep diging.