An evaluation of feature extraction in EEG-based emotion prediction with support vector machines

Electroencephalograph (EEG) data is a recording of brain electrical activities, which is commonly used in emotion prediction. To obtain promising accuracy, it is important to perform a suitable data preprocessing; however, different works employed different procedures and features. In this paper, we aim to investigate various feature extraction techniques for EEG signals. To obtain the best choice, there are four factors investigated in the experiment: (i) the number of channels, (ii) signal transformation methods, (iii) feature representations, and (iv) feature transformation techniques. Support Vector Machine (SVM) is chosen to be our baseline classifier due to its promising performance. The experiments were conducted on the DEAP benchmark dataset. The results showed that the prediction on EEG signals from 10 channels represented by the band power one-minute features gave the best accuracy and F1.