Electroencephalogram Emotion Recognition Based on Individual Frontal Asymmetry Hypothesis

The use of Electroencephalogram(EEG) for emotion recognition has tremendous potential across psychology and biomedicine. However, how the brain generates emotions remains unclear. Inspired by neuroscience and psychology, this paper puts forward the individual frontal asymmetry hypothesis and three methods of Electroencephalogram(EEG) emotion recognition based on this potential hypothesis are introduced, which recognizes and classifies the individual’s emotion effectively with signals from only four channels out of the total 32 channels. First, all EEG signals are filtered according to the EEG frequency band. Then, taking the filtered left and right frontal lobe signal differences as the input, three different models are used for classification with leave-one-out cross-validation. For each subject, one film is used for testing and the remaining films are used for training. We verify our idea on the public database DEAP, and recognition accuracy reaches 75.39% in the valence dimension and 68.13% in the arousal dimension, respectively. Since only four EEG channels were used, it greatly improves the operation efficiency and saves the running time. This work might be a demonstration that emotion recognition using individual frontal asymmetry hypothesis is effective, and it provides a potential direction for emotion recognition using portable EEG acquisition devices.