Tensor Decompositions of EEG Signals for Transfer Learning Applications
In this report, tensor decomposition methods of EEG signals have been evaluated for the purpose of transfer learning. The aim has been to address the person-to-person Brain-Computer Interface (BCI) calibration problem by transferring training data between sessions, which can shorten calibration times, extend the amount of training data, and enable using data from simulated environments in real wor