## 2 Materials and Methods
### Classification Methods  
The methods in this study used to classify the data are RBF, DNN, and Grammatical Evolution variant, named GenClass, which are also depicted in which are also depicted visually in Section 2.6. The RBF is a kind of artificial neural network which has been widely used for a range of tasks, including classification, regression, and clustering with effectiveness in problems with high-dimensional input spaces and complex patterns [77; 78; 79]. The RBF network has several advantages over other neural network architectures, including its ability to handle high-dimensional data, fast training and testing times, and the ability to approximate any continuous function with arbitrary precision. The RBF network has three layers, according to [79] (input, hidden, and output). An input comes from a variable in Table 1. The hidden layer uses radial basis functions as activation functions to transform the input data into a new representation. This representation is then used for further processing in the output layer. The output of the network is computed as a linear combination of the transformed inputs. Thus, the output is a binary decision in the form of 0 or 1 (TRUE or FALSE), representing the two outcomes of each of the six classes of the study. Figure 2 presents the RBF Neural Network flowchart. Figure 1: _Cont._  
DNNs [80] consist of many artificial neural networks formed as layers, each composed of a specific number of neurons. The received input is transformed nonlinearly at each layer, and the outputs are then passed on to the layer above it until the network's output. Their architecture (Figure 3) allows them to learn highly complex representations of input data and link them to the desired output, rendering them a suitable and effective tool for a wide range of applications, including but not limited to image recognition, speech recognition, natural language processing, and classification. Figure 1: **(a)** Descriptive statistics for game-score dataset for NDs and no-NDs per feature category. **(b)** Descriptive statistics for heart-rate dataset for NDs and no-NDs per feature category. **(c)** Descriptive statistics for eye tracking dataset for NDs and no-NDs per feature category. The core element of a DNN is the artificial neuron [81]. Specifically, a neuron applies a nonlinear function to the weighted sum of those inputs and outputs. In the case of a fully  
Figure 3: DNN architecture. Figure 2: RBF Neural Network flowchart. connected network, each neuron of each layer is connected with every neuron of the next layer, and the weights of those connections are learned during the training. The learning procedure, or, differently, the training phase of a DNN, usually involves the adjustment of the weights of all neuron connections to minimize an error between the network's predictions and the actual output values. Usually, this is conducted using a method called backpropagation [82], in which the gradient of the error with respect to each weight in the network is calculated to reduce the error. This minimization can also be conducted by using more sophisticated optimization techniques but with a cost. One of the challenges in training DNNs is to avoid overfitting. Overfitting means that the network becomes so specialized to the training data that it cannot perform well on new unseen data. Many techniques have been proposed to mitigate this problem, such as dropout and weight decay [83], among others. A genetic programming technique called grammatical evolution uses a grammar-based strategy to evolve computer programs [84]. It is an evolutionary process that has been used in various cases such as music composition [85], economics [86], symbolic regression [87], and caching algorithms [88]. In the genetic algorithm, the chromosomes serve as a vector of integer values to represent the production rules of a Backus-Naur Form (BNF) grammar [89]. The algorithm proposed by [PERSON], named GenClass [70], is a classification algorithm based on grammatical evolution.