Using normative modeling and machine learning for detecting mild traumatic brain injury from magnetoencephalography data
Fig 3
(A) Feature importance. The feature importance (horizontal axis) is defined as the reduction in accuracy when the feature is randomly permuted. The labels of the features indicate the cortical region, the index of the subarea within the subdivided region, and the hemisphere (L for left, R for right) that the feature corresponds to. Only the 30 features with the largest mean importance are shown. Values are sorted according to mean importance across folds, and the median values are shown in red. (B) Cortical sources contributing to the classification of patients and controls at two frequency bands. The spatial distribution of the average feature importance across folds, shown for the theta and alpha frequency bands. The values were calculated as the permutation feature importance.