1. A, Model patterns: interpreting coefficients of a machine learning model is not trivial and a high coefficient value does not necessitate a high signal value in the MEG data (for details, see Haufe et al., 2014). “Model patterns” are a way to highlight the ...
A, Model patterns: interpreting coefficients of a machine learning model is not trivial and a high coefficient value does not necessitate a high signal value in the MEG data (for details, see Haufe et al., 2014). “Model patterns” are a way to highlight the signal in a neurophysiological sensible way that is directly interpretable compared to the raw coefficients (Haufe et al., 2014). We show top and bottom 5% of the patterns in the γ-low band from 222 to 238 ms. Blue colors are areas of activation able to predict real words and yellow/red are areas used to predict pseudo word. B, Average top and bottom 5% of ITPC difference; blue colors indicate higher ITPC for real words and yellow/red colors indicate higher ITPC for pseudo word γ-low band from 222 to 238 ms. C, Average ITPC over time; solid lines are the average of the selected features, dashed lines are the average of all vertices in the source space. Time 0 is the divergence point, when stimuli could be recognized from the available acoustic information.
Mads Jensen et al. eNeuro 2019;6:ENEURO
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