Multiscale Fractal Analysis on EEG Signals for Music-Induced Emotion Recognition
This work addresses emotion recognition for medical and rehabilitative applications, but it is incremental as it builds on existing methods with new feature extraction techniques.
The paper tackled emotion recognition from noisy EEG signals by using multifractal analysis to extract features based on fluctuations and fragmentation across frequency bands, achieving results that surpassed baseline features on the DEAP dataset.
Emotion Recognition from EEG signals has long been researched as it can assist numerous medical and rehabilitative applications. However, their complex and noisy structure has proven to be a serious barrier for traditional modeling methods. In this paper, we employ multifractal analysis to examine the behavior of EEG signals in terms of presence of fluctuations and the degree of fragmentation along their major frequency bands, for the task of emotion recognition. In order to extract emotion-related features we utilize two novel algorithms for EEG analysis, based on Multiscale Fractal Dimension and Multifractal Detrended Fluctuation Analysis. The proposed feature extraction methods perform efficiently, surpassing some widely used baseline features on the competitive DEAP dataset, indicating that multifractal analysis could serve as basis for the development of robust models for affective state recognition.