Table 7 Classification precision, recall and accuracy calculated by selected classifiers at different EEG data bands when the combination of occurrence (k), duration (k) and coverage (k) is considered as the discriminative (or input) feature for classification
From: EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features
| Â | Alpha | Beta | Delta | Theta |
|---|---|---|---|---|
Accuracy, Precision, Recall | ||||
 Random Forest | 0.516, 0.518, 0.516 | 0.728, 0.792, 0.728 | 0.492, 0.504, 0.492 | 0.594, 0.609, 0.594 |
 SVM (Linear) | 0.432, 0.259, 0.432 | 0.604, 0.565, 0.604 | 0.464, 0.341, 0.464 | 0.480, 0.361, 0.480 |
 SVM (RBF) | 0.504, 0.531, 0.504 | 0.530, 0.528, 0.530 | 0.514, 0.440, 0.514 | 0.498, 0.249, 0.498 |
 Decision Tree | 0.570, 0.575, 0.570 | 0.716, 0.738, 0.716 | 0.584, 0.592, 0.584 | 0.582, 0.587, 0.582 |
 kNN | 0.576, 0.584, 0.576 | 0.808, 0.839, 0.808 | 0.566, 0.591, 0.566 | 0.662, 0.670, 0.662 |
 Gradient Boost | 0.572, 0.575, 0.572 | 0.808, 0.831, 0.808 | 0.650, 0.663, 0.650 | 0.634, 0.645, 0.634 |