The investigation of neural correlates of mental imagery and perception has been a pivotal area of research in cognitive neuroscience, offering significant insights into how the brain represents both imagined and perceived experiences. While previous studies have successfully identified electrophysiological markers associated with motor and perceptual imagery, the neural signatures of motivational imagery—encompassing desires, needs, and cravings—remain underexplored. This study employs different machine learning classifiers applied to EEG data to classify and compare neural representations of twelve distinct motivational states under perception and imagery conditions. We conducted experiments using 14-channel and 18-channel EEG configurations to capture and analyze the neural responses of participants exposed to various stimuli. Our primary aims were to evaluate classification performance, measured by accuracy, and assess the impact of electrode density on performance. The results indicate that perception conditions generally yield higher accuracy in distinguishing motivational states than imagery conditions. Specifically, primary needs and somatosensory states exhibited strong and clear neural patterns in perception, with a peak accuracy of 88% in the 18-channel setup, while the accuracy for imagined states was more variable. Comparisons between 14-channel and 18-channel configurations revealed that higher electrode density slightly improved performance but was not significantly superior.

Cardiovascular disease (CVD) is a general term referring to several heart or blood vessels abnormality. Heart failure (HF), directly associated to (CVD), is a significant global health problem as well as the leading cause of morbidity and mortality. The early detection of this condition is crucial for patient health. Traditional diagnostic methods for HF, such as history taking and physical examination, are often insufficient and require the use of advanced techniques such as Electrocardiogram (ECG). This study aims to extract temporal and morphological features from (ECG) signals and compare different Machine Learning (ML) classification models to enable rapid diagnosis and provide interpretable predictions. Specifically, we propose a Light Gradient Boosting (LGBM) model that can discriminate Normal Sinus Rhythm (NSR) and Arrhythmia (ARR) with a high accuracy of 0.99, achieving a Precision of 1.00, Recall of 0.99, and f1-score of 0.99 in the (NSR) class, and Precision of 0.99, Recall of 1.00, and f1-score of 0.99 in the (ARR) class, respectively. In addition, eXplainable Artificial Intelligence (XAI) analysis is performed to explain the model predictions.

Brain-computer interfaces are widely used to control machines using Electroencephalography (EEG) signals. Several low-cost electroencephalographs are available on the market that achieves good-quality EEG signals. One of the most intriguing issues for developing biofeedback systems is classifying users' emotional states using EEG signals and Machine Learning (ML) methods. In our study, we propose a novel ML-based biofeedback tool using a BCI to detect two different users' mental states: Focus, and Relaxation. We compared several ML algorithms achieving an average accuracy on the Test Set of 0.90 by using SVM. Finally, we propose a prototype for music generation according to the classification output that could be adopted in neurorehabilitation scenarios.

Mild Cognitive Impairment (MCI) is a syndrome charac-terized by cognitive impairment that is greater than expected for a subject's age and level of education. Nevertheless, it does not interfere with daily activity. Prevalence in epidemiological and population-based studies ranges from 3% to 19% in adults older than 65 years. A very interesting approach in this area is related to the identification of an Artificial Intelligence (Al)-based model and a subset of relevant features to predict the MCI clinical outcome. In our study, we propose a Pareto-optimality-based approach to identify the best model for predicting MCI. In fact, the best model achieves an Accuracy and Recall on Yes MCI of 71 % and 80% respectively. With this approach, it is possible to select the best model in order to predict Yes MCI (highest risk class). Our study presents a new best model selection approach that can be applied in identifying the best model that can be applied in various disease classification problems.

Brain-Computer Interfaces allow controlling machines through signals coming from Electroencephalography (EEG) analysis. Nowadays, there are several cheap electroencephalographs available on the market that guarantee good quality EEG signals. A very interesting approach in this area is related to detecting the emotional states of a user through the analysis of her EEG signal. In our study, we tried to detect the emotional polarity (Valence), the state of emotional excitement (Arousal), and the level of emotion control (Dominance). Through metric interpolation and Russell’s circumplex model, it is possible to characterize and define the current emotional state of the user who wears the device. Our study presents a prototype of an EEG-based emotion recognizer that provides the user’s emotional state exploitable as bio-feedback.