Key result
NIG-based emotion recognition outperforms state-of-the-art methods to achieve up to ~99% accuracy.
Why the study?
The non-stationarity and chaotic nature of EEG signals restrict state-of-the-art techniques from precisely identifying distinct emotional states.
A novel EEG signal processing approach using LMD and NIG modeling with an Adaboost classifier achieves high accuracy in automatic emotion recognition.
Advances EEG emotion recognition accuracy; extends computational methods but leaves open clinical translation.
Electroencephalography (EEG)-based emotion recognition is crucial in the domain of Human-Computer Interaction (HCI), which gained significant attention in recent years. However, the non-stationarity and chaotic nature of the EEG signals pose challenges and restrict the state-of-the-art techniques from precisely identifying distinct emotional states from the EEG data and hence offer limited emotion recognition performance. To capture the underlying non-linear characteristics of EEG, this study employed a novel local mean decomposition (LMD) algorithm which decomposes EEG signals into product functions (PFs). Further PFs are modeled by Normal Inverse Gaussian (NIG) probability density function (PDF) parameters. Thus these PDF features are fed to an optimized Adaboost classifier developed with the help of a cross-validation approach. The novelty of the work lies in the NIG modeling of LMD domain PFs to identify specific emotions from the EEG signals. The significance of the NIG parameters is illustrated by qualitative, pictorial, and statistical analyses. To assess the efficiency of the proposed approach, intensive experiments are conducted on open-source datasets, SJTU Emotion EEG Dataset (SEED), SEED-IV, and Database for Emotion Analysis of Physiological Signals (DEAP). The emotion recognition performance is evaluated in terms of heat maps, receiver operating characteristics (ROC), and accuracy. The proposed emotion recognition system outperformed the state-of-art methods and achieved a maximum accuracy of 97.3%, 98%, and 98.6% with the cross-validation approach and 93.23%, 94.87%, and 95.58% for the cross-subject validation approach using DEAP and SEED, SEED-IV datasets, respectively.
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Pusarla et al. (2022) studied Emotion recognition. Normal Inverse Gaussian (NIG) modeling of local mean decomposition (LMD) domain product functions vs. State-of-the-art methods was evaluated on Emotion recognition accuracy. The proposed NIG-based emotion recognition system outperformed state-of-the-art methods, achieving maximum cross-validation accuracies of 97.3%, 98.0%, and 98.6% on DEAP, SEED, and SEED-IV datasets.
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