Emotion Recognition for Brain Machine Interface: Non-linear Spectral Analysis of EEG Signals Using Empirical Mode Decomposition

Emotions are a fundamental part of the human experience but currently there are no methods that can objectively detect and categorize them. This study utilizes the empirical mode decomposition (EMD) method to categorize emotions from encephalography (EEG) recordings. In the past, EMD has proven to be a very useful signal analysis tool because of its ability to decompose nonstationary signals, like those from an EEG, into component signals with varying frequency content called intrinsic mode functions (IMFs). The method in this paper utilizes three features extracted from the IMFs–the first difference of time, the first difference of phase, and the normalized energy–for data categorization using support vector machine (SVM) classifiers. Two classifiers were trained for each subject, one for valence and another for arousal. The mean accuracies yielded for valence and arousal were 75.86% and 75.31%, respectively. The results of this study verify previous findings by other researchers that these three features are useful in emotion recognition when applied to previously recorded EEG data, though we add the caveat that subject-specific classifiers are needed instead of generalized, global classifiers.