Optimal Classification of Emotions from Electroencephalography (EEG) Signals

It is incredibly challenging to build an intelligent algorithm for emotion recognition that can deliver high accuracy because electroencephalography (EEG) signals are not stationary, nonlinear, and noisy. First, decomposing the preprocessed EEG of SEED dataset into five frequency bands: delta, theta, alpha, beta, gamma, then calculated their energy entropy from extracted features. Then Principal Component Analysis (PCA) method feature reduction was performed. important note different types wavelets transform (db6, db5, etc.) were tested, hyperparameter tuning classification models done obtain optimal accuracy. The next step classifying emotions three states: -1(negative), 0(neutral), +1(positive), tested on two models, namely Random Forest Support vector machine (SVM). SVM gives better performance compared with 80.74%.