# A Novel Approach for Emotion Recognition Based on EEG Signal Using Deep Learning
## 4 Application Results and Discussion  
In this study, the data belonging to the GAMEEMO dataset were used and emotion analysis was performed by classifying both discrete emotion model, and dimensional emotion model. Three machine learning and one deep learning algorithm were used for the classification process. The kNN algorithm is an algorithm that works based on supervised learning. It is simple and easy to implement. It is generally used effectively in classification and regression problems [46]. The algorithm is basically based on similarity between neighbors. It is a method based on transferring data that are close to each other to the same class or cluster. Each new sample that comes to the data space is compared with the data in the dataset and placed in the class or cluster it belongs to according to similarity. kNN used in various fields such as data mining, pattern recognition and security. One of the biggest advantages of the kNN algorithm is that it gives successful results on data containing noise. Furthermore, it has been observed that as the number of data increases, the algorithm gives successful results [47]. Among the disadvantages, it can be shown that the success of the classification varies depending on the selected distance calculation method and the \(k\) value [47]. As the distance between data is calculated one by one, there may be a decrease in the performance of the classification. SVM is a machine learning algorithm based on statistical learning theory and developed with the technique of supervised learning. It was first introduced in 1995 and aimed to solve classification problems with pattern recognition [48]. In the early days, it was used only to distinguish binary-class linear data, but nowadays it can be used to distinguish both non-linear and multi-class data. The basis of the classification process with SVM lies in the separation of data belonging to two different classes with a specified boundary, namely the hyperplane. When the data are not linearly separated, nonlinear SVMs are used. Various kernels are used for this, such as radial base, polynomial, and sigmoid. SVM is effective in situations where the distinction between data is clear [49]. Similarly, it is effective in higher dimensional spaces. On the other hand, if the dataset is large, it is not an appropriate classification algorithm [49]. RF is a classification algorithm formed by combining multiple decision trees. Each individual tree in the random forest splits out a class prediction and the class with the most votes become the model's prediction. The RF algorithm is mentioned as one of the ensembles learning methods. Today, it is used effectively in many areas and produces considerable results as a classification performance. One of the biggest advantages of RF is that it runs effectively on large datasets [50]. In addition, it can predict which variable is more important in a classification process. It produces successful results in predicting missing data. Furthermore, as with every classifier, it also has disadvantages. One of its biggest limitations is that the algorithm works slowly when the number of trees is high, and it is ineffective in real-time prediction. Another cutoff is that the training time is long. This situation is caused by the large number of trees in the algorithm. In addition to machine learning algorithms, one deep learning has been designed and used in this study. The designed deep learning method is based on bidirectional long-short term memory architecture. BiLSTMs can be expressed as an extension of traditional LSTM architectures. The main purpose of developing this architecture is to improve the performance in sequence classification [51,52]. BiLSTMs train two LSTMs instead of one in the input sequence. The first on the input sequence as-is and the second on a reversed copy of the input sequence. This can provide additional context to the network and result in faster and even fuller learning on the problem.