# Classification of EEG Signals for Prediction of Epileptic Seizures
## 2 Related Work  
Researchers [36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55] have proposed several techniques to predict seizures, including traditional machine learning approaches and deep learning techniques. EEG signals are generally susceptible to noise, especially scalp EEG, where electrodes that acquire the EEG signals are placed far from the source, i.e., on the scalp. Multiple types of noise affect the SNR of EEG signals, including powerline noise [56] between 50-60 Hz, baseline noise [57] that occurs because of the electrical interference of electrodes with each other, and artifacts that are generated because of human movements such as eye blink, pulse, etc. Researchers have proposed preprocessing techniques for increasing the signal-to-noise ratio (SNR) of electroencephalagram signals. The authors of [36, 46, 58] removed noise using bandpass filters. The authors of [38] transformed the time domain signals to frequency domain using fast Fourier transform (FFT). Researchers [39, 59] have also used short time Fourier transform (STFT) to preprocess the EEG signals. STFT is very useful in preprocessing because of the non-stationary nature of EEG signals. The authors of [45] broke the signals down into multiple intrinsic mode functions (IMFs) on the basis of the frequency components using empirical mode decomposition (EMD) [60, 61] as well as discrete wavelet transform (DWT) [62] for the purpose of preprocessing. In [41], the authors proposed DWT for preprocessing. Making a surrogate channel [63], using a common spatial pattern (CSP) [64], local mean decomposition (LMD) [60], or adaptive filtering [65] are some of the other ways of removing noise from EEGs. Data after preprocessing is usually in large quantities and of a higher dimension. In this form, EEG signals are not suitable to be passed to classifier for classification. Therefore, a feature set is required, which is a subset of data that has lower dimensions and is not redundant. The process of converting data into a feature set is called feature extraction and extracts distinct features for classification. Both handcrafted and automated features have been extracted in existing methods. Handcrafted features of EEGs usually include univariate [66] and multivariate features [67]. Time domain features include Lyapunov exponent PCA [68], Hjorth parameters [69], approximate entropy [70], and statistical moments [71] and include the mean, also know as the average, variance, which is the deviation from the mean; skewness, which can be called distortion or asymmetry; kurtosis, which is the sharpness of the peak; and entropy, which is the measure of randomness in data [72]. Hjorth parameters are _complexity_ and _mobility_. These parameters are helpful in the classification of EEG signals. Variance of the EEG signal through time is called Hjorth activity. The following equations give the mathematical representation of _activity_, _mobility_, and _complexity_, respectively. \[Activity=var(y(t)) \tag{1}\]  
\[Mobility(y(t))=\sqrt{\frac{Activity(\frac{dy(t)}{dt})}{Activity(y(t))}} \tag{2}\]  
\[Complexity(y(t))=\frac{Mobility(\frac{dy(t)}{dt})}{Mobility(y(t))}. \tag{3}\]  
Average frequency is given by _mobility_, whereas variation in frequency is given by _complexity_. Spectral features, which are frequency domain features, include spectral moments, spectral skewness, spectral centroid, variational coefficients, and power spectral density. Handcrafted features such as zero-crossings intervals [36], phase-locking values [45, 46], bag of waves [37], and common spatial pattern filtering [44], have been extracted. Convolutional neural networks (CNNs) are used by [41, 73] for feature extraction. A convolutional neural network extracts features in such a way that it keeps the class information; by doing this, features that have high inter-class variance are extracted. Ref. [74] used Hilbert vibration decomposition for the extraction of amplitude modulation/frequency modulation subcomponents of signals of non-stationary nature.