A comparison between the Hilbert-Huang and Discrete Wavelet Transforms to recognize emotions from...

Recent studies have attempted to recognize emotions by extracting features from electroencephalographic (EEG) signals using either linear and stationary, or linear and non-stationary transformations. However, as EEG signals are non-linear and non-stationary, it seems that a non-linear and non-stationary transformation may be more suitable. Despite the attractiveness of this hypothesis, until now, little studies have used such transformation. The current work presents a comparison between an approach to recognize positive and negative emotions using a non-linear and non-stationary transformation (Hilbert-Huang Transformation) with an approach using linear and non-stationary transformation (Discrete Wavelet Transform). The two approaches were compared using 200 EEG signals recorded from 10 subjects. The comparison indicated that an approach using the Hilbert-Huang Transformation statistically significantly classified emotions more accurately than a Wavelet-based approach (P < 0.02). This result implies that Hilbert-Huang Transformation is a promising tool to increase the prediction of emotional states, thereby helping to designing and developing more robust emotion recognition approaches.Clinical relevance- This remarks the potential of the Hilbert-Huang transform to enhance EEG-based emotion recognition systems, which can potentially help to diagnose and treat mental diseases, such as autism and depression.