Table 6 Classification precision, recall and accuracy calculated by selected classifiers at different EEG data bands when 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 | 0.478, 0.487, 0.478 | 0.794, 0.840, 0.794 | 0.508, 0.543, 0.508 | 0.556, 0.563, 0.556 |
 SVM (Linear) | 0.492, 0.487, 0.492 | 0.668, 0.708, 0.668 | 0.416, 0.410, 0.416 | 0.546, 0.553, 0.546 |
 SVM (RBF) | 0.474, 0.479, 0.474 | 0.656, 0.688, 0.656 | 0.396, 0.383, 0.396 | 0.442, 0.433, 0.442 |
 Decision Tree | 0.502, 0.498, 0.502 | 0.764, 0.783, 0.764 | 0.578, 0.586, 0.578 | 0.620, 0.631, 0.620 |
 kNN | 0.564, 0.571, 0.564 | 0.776, 0.814, 0.776 | 0.548, 0.551, 0.548 | 0.596, 0.612, 0.596 |
 Gradient Boost | 0.546, 0.549, 0.546 | 0.788, 0.826, 0.788 | 0.636, 0.652, 0.636 | 0.602, 0.614, 0.602 |