## 3 Methodology
### Identification Mechanism-Categorical Classifications  
Practical experience has demonstrated that the performance of epileptic seizure detection depends not only on feature extraction but also on the quality of features selected from EEG data. This procedure is often hidden in the categorical classification. The correlation among features is a good break point to achieve the reduction of feature dimension [8; 15; 16; 32; 41; 43; 52; 53; 54]. Recognition, also called classification, can be considered an important research field, whose common and traditional methods mainly include support vector machine (SVM), K-nearest neighbor (KNN), neural network (NN) classifiers, forest classifiers, and decision trees (DTs) [8; 9; 10; 14; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60].  
In the present work, a series of coefficient features, calculated using the MFCC algorithm, were gathered for classification using a wide range of classifiers. The recognition of EEG signals was perfectly achieved. In order to test and verify the effectiveness and correctness of our proposed hypothesis, multiple classification categories were employed in the current study, such as back-propagation neural network (BPNN), SVM, and KNN.  
* **Back-Propagation Neural Network (BPNN)** is a feed-forward neural network that can be considered a category of artificial neural networks (ANNs). Generally, the structure of BPNN contains three layers: an input layer, a hidden layer, and an output layer. The steepest descent algorithm is often employed in the learning rule, and the weights and thresholds can be gradually adjusted, so the minimum summation of squared errors can be reached [5; 26; 32].
* **Support Vector Machine (SVM)** is a potential methodology used for solving the classification problem. It is not only regarded as a linear classifier, but also as a nonlinear classifier. Usually, SVM can be employed to minimize the operational error and maximize the margin hyperplane to maximize the classification performance [5; 7; 9; 10; 14; 25; 27; 35].
* **Linear Discriminant Analysis (LDA)** has been successfully used in multidimensional reduction and pattern recognition [26; 41]. LDA can be defined as classifying the dependence by separating the \(m\)-dimensional descriptor space into two areas that are separated by a hyperplane. Concerning classification issues, LDA can be used to minimize the distances among vectors of the same class and maximize the distance of inconsistent class centers [26; 41].
* **K-nearest neighbour (KNN)** is often used to identify different EEG signals, and can be considered as a supervised and non-parametric machine learning technique, which is relatively simple, has low computational burden, and is easy to implement [5; 11; 26; 40].
* **Decision Tree (DT)** has been verified to have a high interpretability, and the rules, obtained by splitting the training dataset into subsets by testing attributes, are also readily compressible [39].  
In this paper, the recognition accuracy is calculated and simplified first as a summation and then as the average value, which can be described as follows:  
\[\textit{Aver.}=\frac{1}{N}\sum_{i=1}^{N}y_{i}, \tag{18}\]  
where \(y_{i}\) represents the classification result of the \(i\)-th classifier, and \(N\) denotes the number of classifiers, and _Aver_. denotes the recognition accuracy.  
The goal of simultaneously using different types of classifiers involves two points: to attempt to mix several classifiers to obtain a stable result, and to verify the super generalization ability of EEG signal analysis. Once all recognition results were calculated for each testing dataset, and the average value for all was calculated, the result was more credible with universal significance.