• biosignals
• emotions

Emotions characterize everyone's life. Emotions are states and signals that allow us to pay more attention to the events that create them, either to get us motivated to create more of a certain experience or less, for example. Anger, fear, and anxiety are emotional states designed to make us uncomfortable. These negative emotional states can create extra stress in your body and your mind, which is uncomfortable but also can lead to health issues if the stress becomes chronic or overwhelming. Physiological signals are known to include emotional information that can be used for emotion assessment. There are indeed unequivocal correlations between physiological changes and specific emotional states. Thanks to the growing computational power, it can exploit machine learning algorithms, intelligent models which learn from experience and have the capability of analysing huge amount of data.
The aim of this study is to validate machine learning algorithms to classify emotions starting from a dataset, inlcuding biosignals and emotions felt in everyday life. A new self-assessment has been developed to allow everyone to express their emotional state in a simple and intuitive way using emojis. Examined signals are EEG, GSR, PPG and EMG, collected using non-invasive wearable sensors. Artificial intelligence algorithms have been applied for the emotions classification using biosignals.