Type: Bachelor Master UROP
Status: Available

This thesis addresses the impact of lateralization on Electrodermal Activity (EDA) sensors in wearable devices. Lateralization, influenced by brain hemisphere activation, affects the accuracy of EDA readings based on the device’s placement on a specific body side. Despite recent studies highlighting this issue, there is limited exploration of the potential benefits of using EDA devices on both sides simultaneously. The research aims to fill this gap by investigating how leveraging data from both sides concurrently can enhance classifier accuracy through machine learning. The focus is on datasets in the lab, with implications for medical-grade applications affected by lateralization. Success in demonstrating improved accuracy may revolutionize the field, particularly in sensitive medical tasks, offering more reliable predictions for tasks impacted by lateralization.

For more information contact: Leonardo Alchieri, Silvia Santini