EEG Based Participant Independent Emotion Classification using Gradient Boosting Machines

Analysis of EEG (Electroencephalography) signals provides an alternative ingenious approach towards Emotion recognition. Nowadays, Gradient Boosting Machines (GBMs) have emerged as state-of-the-art supervised classification techniques used for robust modeling of various standard machine learning problems. In this paper, two GBM’s (XGBoost and LightGBM) were used for emotion classification on DEAP Dataset. Furthermore, a participant independent model was fabricated by excluding participant number from features. The proposed approach performed well with high accuracies and faster training speed.