Feature and channel selection for designing a regression-based continuous-variable emotion recognition system with two EEG channels

Objective: With deepened interactions between human and computer, the need for a reliable and practical system for emotion recognition has become significant. The aim of this study is to propose a practical system for estimation of a continuous measure of valence based on a few number of EEG channels. Methods: A vast spectrum of time, frequency and coherence features were implemented with linear Regression (LR), Support Vector Regression (SVR) and Multi-Layer Perceptron (MLP) models and then ranked for the performance on DEAP database using a regression-based Relief filter. Regression outcomes were also classified to compare the performance of the proposed method with the literature. Finally, a video-based emotion recognition experiment was designed and conducted on 12 subjects using F7, F8, FC2 and T7 electrodes. Results: Magnitude Squared Coherence Estimate(MSCE) on F7-F8 with SVR model provided the highest performance on DEAP dataset. Classification of the output led to an average accuracy of 67.5%. For the gathered data, combination of MSCE and Hilbert-Huang Spectrum provided the best performance with 0.22 root mean square error and 0.67 correlation with self-reported valence in the scale of 1-9. Conclusion: MSCE could provide a good accuracy in estimation of Valence using 2 EEG channels on Deep dataset, and with addition of Hilbert-Huang Spectrum, it also demonstrated good accuracy and correlation with self-reported valence, in a completely different experiment. Significance: Continuous-value estimation of the valence can be achieved with only 2 EEG channels for practical applications out of the lab.

Automated summary

A practical system for continuous valence estimation based on a few EEG channels was proposed in this study. By implementing various features and models and ranking them based on dataset performance, the study demonstrated high accuracy and correlation in emotion recognition experiments.