## 1 Introduction
For this purpose, subbands of EEG signals are determined from discrete wavelet transform (DWT), and then classification is performed using ensemble classifiers fed with energy features extracted from the subbands. Results show that in the TLE (Temporal Lobe Epilepsy), PNES, and healthy epoch classification, performance evaluations were realized by using five-fold cross-validation method and the highest accuracy of 97.2%, the sensitivity of 97.9%, and specificity of 98.1% were achieved by applying the adaptive boosting method, and the highest accuracy of 87.1%, the sensitivity of 86.0%, and specificity of 93.6% were attained using random under sampling (RUS) boosting method in TLE patients, PNES patients, and healthy subject discrimination. The study presented in [12] investigates the quantitative electroencephalography (QEEG) features for PNES by evaluating the resting EEG spectral power changes during the periods between seizures. Using Fast Fourier transformation (FFT), a spectral power analysis was calculated for different EEG subbands from the EEG of 39 patients. As a result, six separate EEG band powers were found (C3-high beta, C3-gamma, C3-gamma-1, C3-gamma-2, P3-gamma, and P3 gamma-1) to be higher in the patients diagnosed with PNES than in the control group. Innovative diagnostic tools that exploit non-linear EEG analysis and deep learning (DL) could provide essential support to physicians for clinical diagnosis. In [13], 18 patients with new-onset ES and 18 patients with video-recorded PNES with normal interictal EEG at visual inspection were enrolled. None of them were taking psychotropic drugs. A convolutional neural network (CNN) scheme using DL classification was designed to classify the two categories of subjects (ES vs. PNES). The proposed architecture performs an EEG time-frequency transformation and a classification step with a CNN. The CNN was able to classify the EEG recordings of subjects with ES vs. subjects with PNES with 94.4% accuracy, and a leave-one-patient-out cross-validation, providing high performance in the assigned binary classification compared to standard learning algorithms. In addition, a theoretical information analysis was carried out to interpret how CNN achieved this performance. Specifically, the feature maps' permutation entropy (PE) was evaluated and compared in the two classes. In [14], the authors investigated the power spectrum density (PSD) in resting-state EEGs to evaluate the abnormalities in PNES-affected brains. Additionally, they used functional connectivity tools, such as phase lag index (PLI) and graph-derived metrics, to observe better the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regionalbrain areas. Three classification models, namely, support vector machine (SVM), linear discriminant analysis (LDA), and multilayer perceptron (MLP), were used to model the relationship between the functional connectivity features. The best performance for the participants' discrimination was obtained using the MLP classifier with an accuracy of 91.02% and a leave-one-out cross-validation. [PERSON] et al. stated that quantitative analysis from resting-state electroencephalogram (EEG) provides helpful information to support the diagnosis of seizure disorders [15]. Studies were selected from five databases and evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2. Results suggest that oscillations along the theta frequency (4-8 Hz) may have a relevant role in idiopathic epilepsy, whereas in PNES, there was no evident trend. However, by examining several studies, the authors put forth evidence that many were subject to several methodological limitations, potentially introducing bias. Specifically, they pointed out that studies using machine-learning methods should report information such as model architecture and training parameters to guarantee reproducibility. Moreover, it is necessary to analyze EEG recordings that did not occur a few hours before a seizure.