###### Abstract  
Epileptic seizures result from abnormal brain activity, posing significant health risks due to their sudden and unpredictable nature. Accurate seizure prediction is crucial for improving patient outcomes and enabling timely interventions. Recent advancements in artificial intelligence (AI), particularly in machine learning (ML) and deep learning (DL), have significantly enhanced seizure detection and prediction. This review provides a comprehensive overview of seizure prediction models that integrate temporal and spectral features as inputs or enhanced representations for ML and DL models. Emphasizing convolutional neural networks (CNNs) and other deep architectures, we explore the role of time-domain and frequency-domain features, such as wavelet transforms, short-time Fourier transforms, and spectrogram representations, in improving model performance. Additionally, the review discusses common challenges, including feature interpretability, generalizability across datasets, and computational efficiency. By highlighting recent advancements and limitations, this study provides insights into optimizing spectral and temporal feature integration for seizure prediction, paving the way for more robust and clinically viable AI-based solutions. seizure prediction; deep learning; EEG signals; CNN; feature extraction; machine learning; STFT; analysis +
Footnote †: 2020: _Applied sciences_  
intelligence (AI) techniques, particularly machine learning (ML) and deep learning (DL), have been increasingly applied to automate and enhance seizure prediction from EEG signals [5; 6; 7]. These methods can assist clinicians by offering fast, consistent, and scalable diagnostic tools. Classical ML models such as decision trees [8], support vector machines (SVMs) [9; 10], k-nearest neighbors (KNNs) [11], and random forests [12] were among the first approaches used to classify epileptic events based on engineered features extracted from EEG. More recently, DL models have demonstrated superior performance due to their ability to automatically extract complex patterns from raw or minimally preprocessed signals. Recurrent neural networks (RNNs), especially long short-term memory (LSTM) networks, are particularly effective in capturing temporal dependencies in EEG signals, making them suitable for modeling the dynamic nature of seizures [13; 14; 15]. Convolutional neural networks (CNNs), on the other hand, are adept at extracting spatial and spectral features from EEG spectrograms and have been successfully employed in seizure recognition tasks [16]. Despite the growing volume of research, the role and impact of temporal (e.g., statistical time-domain features, autocorrelation) and spectral (e.g., power spectral density, wavelet coefficients) features remain underexplored in many reviews [17; 18; 19; 20]. These two categories of features are essential for understanding seizure dynamics, as they capture the timing and frequency characteristics of abnormal brain activity. Temporal features offer insight into signal variation over time, while spectral features decompose EEG into frequency bands that are often linked with seizure onset and propagation. By focusing on the interplay between ML/DL models and temporal/spectral EEG features, this review aims to provide a comprehensive and comparative understanding of how different algorithms leverage these features to predict seizures effectively. This scoping review categorizes existing approaches based on the type of learning model and the nature of the features used, as shown in Figure 1, identifies key trends and limitations, and discusses future directions. In doing so, it seeks to highlight not only the strengths of current methods but also the potential for innovation in seizure prediction based on feature representation and the learning strategy. The primary objectives of this study are threefold:  
Figure 1: Graphical summary of this scoping review on EEG-based seizure prediction.