Emotion recognition plays an important role in human computer interaction systems as it helps
the computer in understanding human behavior and their decision making process. Using Electroencephalographic (EEG) signals in emotion recognition offers a direct assessment on the inner state of human mind.
This study aims to build a subject dependent emotion recognition system that differentiate between high
and low levels of valance and arousal, using multidimensional EEG signals. Our system offers a transfer
learning- minimum distance to Riemannian mean (TL-MDRM) framework. In this work, we perform two
pre-processing stages. In the first stage, we analyze the EEG signals to investigate their non-Gaussianity
and determine the most appropriate signal distribution. Using several statistical and goodness of fit tests,
T-distribution was found to be the most appropriate distribution. Covariance matrix estimations plays a
crucial step in manifold learning technique, based on the most suitable signal distribution the covariance
matrix estimation technique is chosen. In the second stage, we perform transfer learning to deal with
cross-session variability by generating a unique reference point for each participant and performing affine
transformation for the covariance matrices on the symmetric positive definite (SPD) manifold around
this point. The results show that, TL process improved the performance even when assuming Gaussian
distribution, while assuming T-distribution with TL improved the performance further.