this was just to make the comparison easier during debugging, the matlab code used nansum. it does not make a difference, I'm remove it then

> On Jan 24, 2014, at 7:21 AM, Alexandre Gramfort <notifications@github.com> wrote:
> 
> In mne/decoding/ems.py:
> 
> > +        objective_function = _ems_diff
> > +
> > +    from sklearn.cross_validation import LeaveOneOut
> > +
> > +    for train_indices, epoch_idx in LeaveOneOut(n_epochs):
> > +        logger.info('.. processing epoch %i' % epoch_idx)
> > +        d = objective_function(data, conditions, train_indices)
> > +        # take norm over channels (matlab uses 2-norm)
> > +        for time_idx in np.arange(n_times):
> > +            d[:, time_idx] /= norm(d[:, time_idx], ord=2)
> > +
> > +        # update spatial filter
> > +        spatial_filter += d
> > +
> > +        # compute surrogates
> > +        surrogate_trials[epoch_idx] = np.nansum(np.squeeze(data[epoch_idx])
> why nansum?
> 
> —
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