# AI-Based Electroencephalogram Analysis in Rodent Models of Epilepsy: A Systematic Review
## 2 Fundamental Concepts and Background
### Machine Learning and Deep Learning Techniques  
ML and DL are artificial intelligence (AI) branches implementing mathematical models and algorithms [80]. The ML algorithms receive historical data as inputs, learn from the data, and make decisions without explicit programming. DL is also a data-driven learning technique where computation is carried out through multi-layer neural networks. With AI, it has been possible to analyse and discover patterns not obvious to the human eye from vast amounts of data. Developing ML and DL models involves a series of steps exemplified in Figure 2. Preprocessing the signals, or signal processing, is a mandatory phase in the ML and DL frameworks after acquiring the raw EEG signal. EEG signals have a high temporal resolution, making them susceptible to noise and artefacts [81]. Extrinsic and intrinsic (physiological) factors cause artefacts. Extrinsic factors such as faulty electrodes, line noise (50 Hz or 60 Hz interference for European and North American standards, respectively), and high impedance can be avoided by adhering to precise and strict recording procedures [82]. Physiological artefacts, which include eye movements, eye blinks, and cardiac and muscle activities, are more challenging to avoid [83]. The interference of these artefacts with neural information may render misleading inferences when used in practice to study varying neural disorders [84; 85]. Following the proximity to the brain, the signals measured through invasive methods have better resolution with higher amplitude [64] and less susceptibility to physiological artefacts [62; 65]. However, the signal processing phase is primarily concerned with attenuating artefacts that may skew the signal content [86] and, sometimes, normalising data into a uniform range. The choice of preprocessing method is based on the preferred characteristics of the signal and the artefacts of concern. Generally, most studies use bandpass filters to filter out the required signal frequency. The feature engineering phase comprises feature extraction and dimension reduction. In the feature extraction stage, preprocessed signals are transformed into relevant attributes of the signals that preserve the physiological activities in the preprocessed signals [87]. EEG signal feature extraction can be performed in the frequency, time, or time-frequency domains [88]. Time domain (TD) analysis is computed on the raw EEG signal involving the amplitude over time. Unlike other domains, TD does not require a feature extraction tool. In contrast, the frequency domain (FD) analysis provides an intuitive grasp of the frequency or spectral components of the EEG signal and is computed on the discrete Fourier transform. Short-time Fourier transform (STFT) spectrograms and discrete wavelet transform (DWT) are commonly applied to decompose signals in the time-frequency domain (TFD) to study the changes in frequency over time [88]. Regardless of the domain, EEG signal features are generally categorised into linear and nonlinear features [89]. The brain is a chaotic, dynamic biological system. The generated EEG signals produce amplitude that varies arbitrarily with respect to time. The underlying neural subsystems that produce the EEG signals are generally considered nonlinear [90]. After feature extraction, the curse of dimension is a critical issue when applying machine learning algorithms to high-dimensional data [91]. Dimension reduction is a technique to reduce high-dimensionalfeatures in a dataset into a low-dimensional space while preserving the original information; this process can be achieved through feature selection or feature transformation. Feature selection involves selecting a subset of discriminative features from the universal set of features extracted [92; 93]. In contrast, feature transformation converts the initial extracted features to produce new features that more accurately capture the underlying relationships or patterns in the data.