[Mne_analysis] Fwd: SNR estimate for real MEG data

[amit]

        External Email - Use Caution        

OK,
Thank you very much Eric.

On Fri, Oct 19, 2018 at 5:11 PM Eric Larson <larson.eric.d at gmail.com> wrote:

>         External Email - Use Caution
>
> The documentation rendering had a bug that has been fixed. Hopefully the
> math can help make things a bit clearer:
>
>
> http://mne-tools.github.io/dev/generated/mne.minimum_norm.estimate_snr.html#mne.minimum_norm.estimate_snr
>
> As to your specific questions, I don't have any answers offhand.
>
> Eric
>
>
> On Tue, Oct 16, 2018 at 3:51 PM amit <amit.neurosignal at gmail.com> wrote:
>
>>         External Email - Use Caution
>>
>> Hi everyone,
>> I am sending this mail again, sorry for repeated mail.
>> I am trying to estimate SNR value of evoked field MEG data. If I look
>> over MNE python code, the function for estimating SNR
>> (mne.minimum_norm.inverse.estimate_snr()) is:
>>
>> def estimate_snr(evoked, inv, verbose=None):
>>       .. versionadded:: 0.9.0
>>     """  # noqa: E501
>>     from scipy.stats import chi2
>>     _check_reference(evoked)
>>     _check_ch_names(inv, evoked.info)
>>     inv = prepare_inverse_operator(inv, evoked.nave, 1. / 9., 'MNE')
>>     sel = _pick_channels_inverse_operator(evoked.ch_names, inv)
>>     logger.info('Picked %d channels from the data' % len(sel))
>>     data_white = np.dot(inv['whitener'], np.dot(inv['proj'],
>> evoked.data[sel]))
>>     data_white_ef = np.dot(inv['eigen_fields']['data'], data_white)
>>     n_ch, n_times = data_white.shape
>>
>>     # Adapted from mne_analyze/regularization.c, compute_regularization
>>     n_zero = (inv['noise_cov']['eig'] <= 0).sum()
>>     logger.info('Effective nchan = %d - %d = %d'
>>                 % (n_ch, n_zero, n_ch - n_zero))
>>     signal = np.sum(data_white ** 2, axis=0)  # sum of squares across
>> channels
>>     noise = n_ch - n_zero
>>     snr = signal / noise
>>
>>     # Adapted from noise_regularization
>>     lambda2_est = np.empty(n_times)
>>     lambda2_est.fill(10.)
>>     remaining = np.ones(n_times, bool)
>>
>>     # deal with low SNRs
>>     bad = (snr <= 1)
>>     lambda2_est[bad] = 100.
>>     remaining[bad] = False
>>
>>     # parameters
>>     lambda_mult = 0.9
>>     sing2 = (inv['sing'] * inv['sing'])[:, np.newaxis]
>>     val = chi2.isf(1e-3, n_ch - 1)
>>     for n_iter in range(1000):
>>         # get_mne_weights (ew=error_weights)
>>         # (split newaxis creation here for old numpy)
>>         f = sing2 / (sing2 + lambda2_est[np.newaxis][:, remaining])
>>         f[inv['sing'] == 0] = 0
>>         ew = data_white_ef[:, remaining] * (1.0 - f)
>>         # check condition
>>         err = np.sum(ew * ew, axis=0)
>>         remaining[np.where(remaining)[0][err < val]] = False
>>         if not remaining.any():
>>             break
>>         lambda2_est[remaining] *= lambda_mult
>>     else:
>>         warn('SNR estimation did not converge')
>>     snr_est = 1.0 / np.sqrt(lambda2_est)
>>     snr = np.sqrt(snr)
>>     return snr, snr_est
>>
>> In the script while preparing an inverse operator for actually
>> computing the inverse ( inv = prepare_inverse_operator(inv,
>> evoked.nave, 1. / 9., 'MNE')), it assumes that the SNR value is 3. So
>> my questions are:
>>
>> 1. Isn't it biasing that we assume SNR value before estimating it?
>> 2. The 'snr' outputs series i.e. snr values at each time point of the
>> data. Then what to take snr value for whole evoked segment: mean, max
>> or the snr value for evoked peak?
>> 3. Can you please suggest any reference for this snr estimation method?
>>
>> Thank you very much.
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