Enhancing the accuracy of electroencephalogram-based emotion recognition through Long Short-Term Memory recurrent deep neural networks

Mohammad Reza Yousefi; Amin Dehghani; Hamid Taghaavifar

doi:10.3389/fnhum.2023.1174104

Enhancing the accuracy of electroencephalogram-based emotion recognition through Long Short-Term Memory recurrent deep neural networks

Front Hum Neurosci. 2023 Oct 10:17:1174104. doi: 10.3389/fnhum.2023.1174104. eCollection 2023.

Authors

Mohammad Reza Yousefi^{1

2}, Amin Dehghani³, Hamid Taghaavifar²

Affiliations

¹ Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
² Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
³ Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.

Abstract

Introduction: Emotions play a critical role in human communication, exerting a significant influence on brain function and behavior. One effective method of observing and analyzing these emotions is through electroencephalography (EEG) signals. Although numerous studies have been dedicated to emotion recognition (ER) using EEG signals, achieving improved accuracy in recognition remains a challenging task. To address this challenge, this paper presents a deep-learning approach for ER using EEG signals.

Background: ER is a dynamic field of research with diverse practical applications in healthcare, human-computer interaction, and affective computing. In ER studies, EEG signals are frequently employed as they offer a non-invasive and cost-effective means of measuring brain activity. Nevertheless, accurately identifying emotions from EEG signals poses a significant challenge due to the intricate and non-linear nature of these signals.

Methods: The present study proposes a novel approach for ER that encompasses multiple stages, including feature extraction, feature selection (FS) employing clustering, and classification using Dual-LSTM. To conduct the experiments, the DEAP dataset was employed, wherein a clustering technique was applied to Hurst's view and statistical features during the FS phase. Ultimately, Dual-LSTM was employed for accurate ER.

Results: The proposed method achieved a remarkable accuracy of 97.5% in accurately classifying emotions across four categories: arousal, valence, liking/disliking, dominance, and familiarity. This high level of accuracy serves as strong evidence for the effectiveness of the deep-learning approach to emotion recognition (ER) utilizing EEG signals.

Conclusion: The deep-learning approach proposed in this paper has shown promising results in emotion recognition using EEG signals. This method can be useful in various applications, such as developing more effective therapies for individuals with mood disorders or improving human-computer interaction by allowing machines to respond more intelligently to users' emotional states. However, further research is needed to validate the proposed method on larger datasets and to investigate its applicability to real-world scenarios.

Keywords: DEAP dataset; Dual-LSTM; Hurst’s view and statistical features; deep learning; electroencephalography; emotion recognition.