Fig. 1

Pipeline of the Artificial Neural Network (AANs) development. Somatosensory evoked potentials (SSEPs) were recorded by stimulating the median nerve at the wrist in both healthy volunteers (HV) and interictal episodic migraine patients (MO). The recordings were analyzed offline to extract low-frequency responses (LF-SSEPs) and high-frequency oscillations (HFO) from the cortical components of the somatosensory evoked potentials. Eleven features were extracted from the analysis, and two techniques were independently applied to reduce the dimensionality and select the relevant features: Principal Component Analysis (PCA) and Forward Feature Selection (FFS). PCA selected four relevant linear combinations of the features, while FFS selected three relevant features. Two different neural network models were trained with these features transferred to the input layer. The hidden layer comprised 50 neurons, while the output layer comprised 2 neurons. Both models were trained one hundred times by randomly dividing our dataset into training, validation, and test sets for each run. Finally, the performance of both models in classifying HV from MO was evaluated by calculating the median accuracy, the sensitivity (recall), the specificity, and the F1 score. The outcomes were derived by averaging the outputs from 100 neural networks trained on the same dataset. Created in BioRender. Sebastianelli, G. (2025) https://BioRender.com/l67q659