Evaluating the structure of cognitive tasks with transfer learning

Understand

Electroencephalography (EEG) decoding is a challenging task due to the limited availability of labelled data.

• While transfer learning is a promising technique to address this challenge, it assumes that transferable data domains and task are known, which is not the case in this setting.
• This study investigates the transferability of deep learning representations between different EEG decoding tasks.
• We conduct extensive experiments using state-of-the-art decoding models on two recently released EEG datasets, ERP CORE and M$^3$CV, containing over 140 subjects and 11 distinct cognitive tasks.