Exploring CEEMDAN and LMD Domains Entropy Features for Decoding EEG-Based Emotion Patterns

Abstract

Electroencephalogram (EEG) signal-based emotion classification is vital in the ever-growing human-computer interface (HCI) applications. However, the chaotic, non-stationary, and person-dependent nature of EEG signals often limits such practical applications. These challenges reduce the ability of the state-of-the-art approaches to effectively distinguish between different emotional states from EEG data, resulting in sub-optimal emotion recognition performance. This work presented a time-frequency (T-F) analysis of EEG signals to localize different EEG frequency rhythms responsible for emotion-related information in the EEG signals. In particular, this work investigated two T-F analysis domains for multichannel EEG signals based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and local mean decomposition (LMD) to extract entropy features that capture specific emotion-relevant traits from the specific EEG channels that are highly responsive to emotions. The CEEMDAN and LMD decompose the EEG signals into different EEG frequency rhythms called mode functions, intrinsic mode functions (IMFs), and product functions (PFs), respectively. Further, various types of entropy feature of these two categories of mode functions, such as approximate entropy (ApEn), sample entropy (SaEn), permutation entropy (PeEn), and bubble entropy (BuEn), are computed for extracting emotion-relevant distinguishing features. Entropy features help quantify EEG’s non-linear behavior and eventually help classify EEG-based emotions precisely. Emotion classification has been achieved using a grid search cross-validation (GSCV) optimized XGboost classifier. Thorough experimentations are conducted to validate the efficacy of the proposed approach on publically accessible datasets named Database for Emotion Analysis of Physiological Signals (DEAP), SJTU emotion EEG dataset (SEED), and SEED-IV. The efficacy of the proposed emotion recognition approach is measured in terms of widely used performance metrics such as accuracy, confusion metrics, receiver operating characteristics (RoC), and area under the curve (AuC). The highest average accuracy is attained using the proposed LMD-domain BuEn features, i.e., 97.8%, 98.6%, and 95.7% using SEED, SEED-IV, and DEAP databases, respectively, outperforming the recent state-of-the-art emotion recognition algorithms.

Keywords

• CEEMDAN
• LMD
• XGboost
• bubble entropy
• grid search
• cross-validation and emotion recognition