Emotion clustering from stimulated electroencephalographic signals using a Duffing oscillator

The paper proposes an alternative approach to emotion recognition from stimulated EEG signals using Duffing oscillator. Reported works on emotion clustering generally employ the principles of supervised learning. Unfortunately, because of noisy and limited feature set, the classification problem often suffers from high inaccuracy. This has been overcome in this paper by submitting the EEG signals directly to a Duffing oscillator and the phase portraits constructed from its time-response demonstrate structural similarity to similar emotion excitatory stimuli. The accuracy in clustering was experimentally validated even with injection of Gaussian noise over the EEG signal up to a signal-to-noise ratio of 25 dB. The results of clustering in presence of low signal-to-noise ratio confirm the robustness of the proposed scheme.