## 2 Material and Methods
In all three experiments, the input consists of elements with 260 spectral values obtained by applying the ST-FFT on the initial signal segments. The ST-FFT was applied on the signal segment using a sliding window of 128 samples and a step of 32 samples, yielding 4 \(\times\) 65 = 260 spectral values per segment. This approach results in an input size roughly equivalent to that of the experiments with the 256 samples of the MEG signal. In this case, the results are significantly better, as shown by the detailed findings presented in the following section. In the third group (Group III), we use two FFNNs of different sizes. In Exp. 6, we train a three-hidden-layer network with 64, 32, and 16 neurons, respectively. In Exp. 7, we train a four-hidden-layer network with 64, 32, 32, and 16 neurons, respectively. In these two experiments, the input consists of a combination of the 256 sample values of each item and the corresponding 260 spectral values obtained through ST-FFT applied to the initial signal segment, resulting in a total of 516 values for each item. The activation function used in all FFNN hidden layers was Hyperbolic Tangent Sigmoid, and for the output layers, Linear Activation was used. A typical Nguyen-Widrow initialization method was used for weight initialization for hidden layers, and random small values were used to initialize input and output layers. Levenberg-Marquardt backpropagation algorithm was used, which is a second-order optimization method that adjusts learning dynamically. The loss function was mean squared error (MSE), and validation and stopping criteria were based on the early-stopping technique using a validation set; the learning rate was adjusted dynamically. The default number of epochs was set arbitrarily extremely high (to 1000), but the algorithm always terminates based on the early-stopping criteria: training stops if the validation error increases for a certain number of epochs (six in our case), if the performance gradient (\(1\times 10^{-7}\)) becomes too small, if the network reachesa specified error threshold, or if adaptive parameter mu exceeds \(1\times 10^{10}\). The batch size is automatically determined based on the training function [31]. In the fourth group (Group IV), we conduct five experiments using 1D convolutional neural networks (1D-CNNs) with varying configurations. In Exp. 8, we train a network with four convolutional layers consisting of 64, 128, 256, and 256 neurons, respectively, and a filter size of 5. In Exp. 9 and 10, we train a four-layer network similar to the previous one, but with a pooling layer inserted between the second and third layers. For Exp. 11 and Exp. 12, we add two additional ReLU layers after the first and third convolutional layers. The one-dimensional convolutional networks are designed for processing one-dimensional signals. Thus, their input, as in the first group, consists of the raw values of the 256 samples for each component of our signal. All CNNs use Glorot (Xavier) initialization, and the optimizer is SGDM (Stochastic Gradient Descent with Momentum). Learning rate is 0.01 and learning rate scheduling is \"piecewise\" decay. Number of epochs is set to 30. Batch size is set to 64 based on our system's memory and the magnitude of our trainset. Training stops early if validation loss does not improve for 5 consecutive epochs ('Patience'). Loss function used is cross-entropy (for classification), and validation loss is used as validation criteria [32]. In all experiments, the networks are trained using the training set (80% of the data: 4348 items/segments), followed by testing and validation with the corresponding sets (each one, 10% of the data).