Exploring Federated Learning for Emotion Recognition on Brain-Computer Interfaces

In recent years, data-driven methodologies based on artificial intelligence (AI) technologies are taking the lead in many biomedical fields, including the interpretation of electroencephalogram (EEG) experiments collected by wereable Brain Computer Interfaces (BCI). Yet, the effectiveness of data in medical research hinges significantly on its volume and diversity. Traditional biomedical research faces constraints due to limited access to datasets often confined within individual medical centers, hindering potential breakthroughs. Reluctance to share patient data across institutions is driven by ethical, legal, and privacy concerns, with laws like GDPR rigorously protecting against privacy breaches. Overcoming these obstacles requires a paradigm shift towards decentralized AI training procedures, enabling secure and efficient usage of sensible data. In this work, we examine and evaluate the potential of federated learning for the task of emotion recognition from BCI data, focusing on performance with respect to centralized approaches. Our focus lies on comparing its performance against centralized approaches, delving into key metrics such as accuracy, efficiency, and privacy preservation.