EEG Based Emotion Recognition: A Tutorial and Review

Li, Xiang; Zhang, Yazhou; Tiwari, Prayag; Song, Dawei; Hu, Bin; Yang, Meihong; Kumar, Neeraj; Marttinen, Pekka

doi:10.1145/3524499

Cited by 164 publications

(68 citation statements)

References 219 publications

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“…The brain is the control center of all human activities and internal functioning. The typical EEG signal obtained is thus a combination of various brain activity and other noise, either related to the environment or body [ 52 ]. Rapid fluctuations in feature value may arise from these noises.…”

Section: Methodsmentioning

confidence: 99%

Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies

Nalwaya

Das

Pachori

2022

Entropy

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Human dependence on computers is increasing day by day; thus, human interaction with computers must be more dynamic and contextual rather than static or generalized. The development of such devices requires knowledge of the emotional state of the user interacting with it; for this purpose, an emotion recognition system is required. Physiological signals, specifically, electrocardiogram (ECG) and electroencephalogram (EEG), were studied here for the purpose of emotion recognition. This paper proposes novel entropy-based features in the Fourier–Bessel domain instead of the Fourier domain, where frequency resolution is twice that of the latter. Further, to represent such non-stationary signals, the Fourier–Bessel series expansion (FBSE) is used, which has non-stationary basis functions, making it more suitable than the Fourier representation. EEG and ECG signals are decomposed into narrow-band modes using FBSE-based empirical wavelet transform (FBSE-EWT). The proposed entropies of each mode are computed to form the feature vector, which are further used to develop machine learning models. The proposed emotion detection algorithm is evaluated using publicly available DREAMER dataset. K-nearest neighbors (KNN) classifier provides accuracies of 97.84%, 97.91%, and 97.86% for arousal, valence, and dominance classes, respectively. Finally, this paper concludes that the obtained entropy features are suitable for emotion recognition from given physiological signals.

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Section: Methodsmentioning

confidence: 99%

Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies

Nalwaya

Das

Pachori

2022

Entropy

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“…Once the variables in objective function (6) which is an Euclidean projection with a simplex constraint [22]. It can be optimized by the Lagrange multiplier method together with the Karush-Kuhn-Tucker (KKT) condition.…”

Section: B Jcsfe Model Formulationmentioning

confidence: 99%

“…Many mental disorders are closely related to emotions [5]; therefore, identifying the emotional state of people with emotional expression disorders is helpful to their treatment and healthcare. In past decades, emotion recognition has been attracting increasing attention from both academia and industry [6]. Compared with the traditional data modalities such as facial expressions, text, and speech, EEG can offer us more reliable emotion recognition results because it is originated from the neural activities of our central nervous system and is not easily camouflaged [7].…”

mentioning

confidence: 99%

Cross-Session Emotion Recognition by Joint Label-Common and Label-Specific EEG Features Exploration

Peng

Liu

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Since Electroencephalogram (EEG) is resistant to camouflage, it has been a reliable data source for objective emotion recognition. EEG is naturally multi-rhythm and multichannel, based on which we can extract multiple features for further processing. In EEG-based emotion recognition, it is important to investigate whether there exist some common features shared by different emotional states, and the specific features associated with each emotional state. However, such fundamental problem is ignored by most of the existing studies. To this end, we propose a Joint label-Common and label-Specific Features Exploration (JCSFE) model for semi-supervised crosssession EEG emotion recognition in this paper. To be specific, JCSFE imposes the ℓ2,1-norm on the projection matrix to explore the label-common EEG features and simultaneously the ℓ1-norm is used to explore the label-specific EEG features. Besides, a graph regularization term is introduced to enforce the data local invariance property, i.e., similar EEG samples are encouraged to have the same emotional state. Results obtained from the SEED-IV and SEED-V emotional data sets experimentally demonstrate that JCSFE not only achieves superior emotion recognition performance in comparison with the state-of-the-art models but also provides us with a quantitative method to identify the labelcommon and label-specific EEG features in emotion recognition.

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“…Electroencephalography (EEG) measures the electrical activity of the brain and is indicated for identifying neurological changes [ 38 ]. Several features of the EEG signal, such as the alpha and beta bands, are useful for identifying positive self-evaluative emotions such as gratitude, inspiration, and pride; the theta and gamma bands are used to characterize pleasure emotions such as amusement, interest, and joy [ 39 ].…”

Section: Emotion Recognitionmentioning

confidence: 99%

Applying Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals

Quispe

Utyiama

Santos

et al. 2022

Sensors

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The use of machine learning (ML) techniques in affective computing applications focuses on improving the user experience in emotion recognition. The collection of input data (e.g., physiological signals), together with expert annotations are part of the established standard supervised learning methodology used to train human emotion recognition models. However, these models generally require large amounts of labeled data, which is expensive and impractical in the healthcare context, in which data annotation requires even more expert knowledge. To address this problem, this paper explores the use of the self-supervised learning (SSL) paradigm in the development of emotion recognition methods. This approach makes it possible to learn representations directly from unlabeled signals and subsequently use them to classify affective states. This paper presents the key concepts of emotions and how SSL methods can be applied to recognize affective states. We experimentally analyze and compare self-supervised and fully supervised training of a convolutional neural network designed to recognize emotions. The experimental results using three emotion datasets demonstrate that self-supervised representations can learn widely useful features that improve data efficiency, are widely transferable, are competitive when compared to their fully supervised counterparts, and do not require the data to be labeled for learning.

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EEG Based Emotion Recognition: A Tutorial and Review

Cited by 164 publications

References 219 publications

Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies

Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies

Cross-Session Emotion Recognition by Joint Label-Common and Label-Specific EEG Features Exploration

Applying Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals

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