Daafnet: Domain Adaptive Augmented Feature Network for Biosignal based Emotion Recognition

Scalp-Recorded biosignals, like EEG, are very effective tools in capturing neurodynamics due to their high temporal resolution, providing useful insights on complex cognitive processes such as emotion. However, due to the low spatial resolution of EEG along with its low signal power to noise ratio and highly complex nature of the brain, the task of emotion recognition tends to be challenging. Furthermore, inter-subject and inter-trial variability in EEG studies interferes with the deep learning models’ ability to generalize. To overcome these challenges, here, we propose DAAFNet, a Domain Adaptive Augmented Feature Network that utilizes quantitative EEG features as multi-channel images and EEG time series data to perform emotion classification. The benchmark datasets, DEAP and SEED, are utilized to perform extensive experiments to demonstrate the performance of our method in comparison with the state-of-the-art methods. We present an extensive analysis pipeline for personalized emotion classification. For reproducible research, complete implementation code is made available at: https://github.com/Mythili98/DAAFNet.