## 1 Introduction
However, it is challenging to distinguish IEDs often due to the muscle artefacts that overlap the signal during the EEG crisis. In addition, prolonged EEG monitoring over an extended period, such as several hours or days, can increase the likelihood of capturing both typical and atypical events. This long-term monitoring can provide a more comprehensive assessment of the EEG patterns associated with PNES and aid in distinguishing them from epilepsy. Nevertheless, long-term monitoring and recording are expensive and time-consuming. Machine-learning methodologies can play a crucial role in optimizing EEG analysis and reducing the need for long recording sessions while minimizing the risk of inducing seizures in subjects. One of the purpose of machine-learning techniques is to extract relevant features from the EEG signals, capturing important patterns and characteristics associated with specific conditions. These features can be used to create concise representations of the EEG data, allowing for efficient analysis and reducing the need for prolonged recording sessions. By focusing on informative features, machine learning can help identify key aspects of the EEG signals without requiring excessive recording time. Moreover, machine-learning methods, such as for instance some data augmentation approaches, can artificially expand the available dataset by generating synthetic EEG samples. This approach can be used to simulate different scenarios, including seizure activity, without the need to induce actual seizures in subjects. By incorporating augmented data into the training process, machine-learning models can learn to recognize and classify patterns associated with seizures, potentially reducing the dependence on inducing seizures during recording sessions. Regarding identifying new methods to support medical decisions, the quantitative analysis sought to highlight differences between PNES and epileptics and healthy subjects. Specifically, some studies are based on analyzing and classifying the semiology of PNES and epilepsy [6; 7]. An example is reported in [8], where the authors describe a new method for diagnosing PNESs. Fifty-five PNES video-EEG recordings were retrospectively analyzed by four epileptologists and one psychiat blindly and classified into four distinct groups: Hypermotor (H), Akinetic (A), Focal Motor (FM), and Subjective Symptoms(SS). Eleven signs and symptoms frequently found in PNESs were chosen for statistical validation of the classification. Agreement between ANN classification and visual classification reached 83.3%. In [9], the researchers present a pilot feasibility study with \"Digital Semiology\" (DS), a novel seizure encoding software. It allows semi-automated annotation of the videos of suspected events from a predetermined, hierarchical set of options, with highly detailed semiologic descriptions, somatic localization, and timing. Sixty episodes from a mixed adult and pediatric cohort from one level 4 epilepsy centre VEM archives were analyzed using DS. The reports were compared with the standard free-form ones written by the same epileptologists. The behavioural characteristics in the DS and free-form reports overlapped by 78-80%. The present study represents an important step toward forming an annotated video archive for machine-learning purposes. Many other literature studies focus on EEG signal processing and machine learning. In [10], the authors analyze short-term EEG data for classifying epilepsy and PNES subjects, functional network, and EEG microstate features. Their results showed that the beta-band is the most helpful EEG frequency sub-band as it performs best for classifying subjects. The work was evaluated through 25 pairs of cross-validation. In [11], an automated discrimination method from EEG signals is proposed to eliminate the misdiagnosis and long inspection time of EEG recordings in PNES diagnosis.