Emotion Personalization with Machine Learning using EEG Signals and Dry Electrodes

Personalization is essential in enhancing the performance of machine learning models in brain-computer interfaces (BCIs) for emotion recognition, specifically in valence and arousal classification. In this work, we address the challenge of personalizing BCI models utilizing a wireless consumer non-invasive electroencephalogram (EEG) device with dry electrodes. Our research investigates the effectiveness of three machine learning algorithms in classifying valence and arousal: k-Nearest Neighbors (k-NNs), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs). To achieve personalization, we adopt an incremental learning approach by progressively incorporating high-quality subject data during model training that are taken from the ground-truth. We compare the performance of the models before and after personalization. The results demonstrate significant improvements in valence and arousal classification accuracy through personalization, with the personalized models outperforming the non-personalized models by up to 27.82% and 28.80%, respectively.