Emotions Recognition Using EEG Signals: A Survey

Alarcão, Soraia M.; Fonseca, Manuel J.

doi:10.1109/taffc.2017.2714671

Cited by 776 publications

(471 citation statements)

References 108 publications

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“…Since these physiological changes involve the whole organism, there are many parameters that can be measured and connected with specific emotions. One of the useful physiological parameters is the electric activity of the cerebral cortex measured with electroencephalography (EEG; for a review, see Alarcao and Fonseca). It is debatable whether specific emotions can be robustly identified with this measure, but analysis of EEG spectral power can provide information on valence and arousal as well as on the strength of expressed emotions .…”

Section: Different Aspects Of Emotional Expressionmentioning

confidence: 99%

Emotional expression in psychiatric conditions: New technology for clinicians

Grabowski

Rynkiewicz

Lassalle

et al. 2018

Psychiatry Clin Neurosci

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Aim Emotional expressions are one of the most widely studied topics in neuroscience, from both clinical and non‐clinical perspectives. Atypical emotional expressions are seen in various psychiatric conditions, including schizophrenia, depression, and autism spectrum conditions. Understanding the basics of emotional expressions and recognition can be crucial for diagnostic and therapeutic procedures. Emotions can be expressed in the face, gesture, posture, voice, and behavior and affect physiological parameters, such as the heart rate or body temperature. With modern technology, clinicians can use a variety of tools ranging from sophisticated laboratory equipment to smartphones and web cameras. The aim of this paper is to review the currently used tools using modern technology and discuss their usefulness as well as possible future directions in emotional expression research and treatment strategies. Methods The authors conducted a literature review in the PubMed, EBSCO, and SCOPUS databases, using the following key words: ‘emotions,’ ‘emotional expression,’ ‘affective computing,’ and ‘autism.’ The most relevant and up‐to‐date publications were identified and discussed. Search results were supplemented by the authors’ own research in the field of emotional expression. Results We present a critical review of the currently available technical diagnostic and therapeutic methods. The most important studies are summarized in a table. Conclusion Most of the currently available methods have not been adequately validated in clinical settings. They may be a great help in everyday practice; however, they need further testing. Future directions in this field include more virtual‐reality‐based and interactive interventions, as well as development and improvement of humanoid robots.

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Section: Different Aspects Of Emotional Expressionmentioning

confidence: 99%

Emotional expression in psychiatric conditions: New technology for clinicians

Grabowski

Rynkiewicz

Lassalle

et al. 2018

Psychiatry Clin Neurosci

View full text Add to dashboard Cite

show abstract

“…It is thus often a supervised learning task since labels are already assigned to the data by humans, although clustering methods have also been employed . Detailed information about the current research in this area can be found in the work of Alarcao and Fonseca …”

Section: Related Workmentioning

confidence: 99%

“…7 Detailed information about the current research in this area can be found in the work of Alarcao and Fonseca. 8 An features include magnitude-squared coherence estimate and differential and rational asymmetries. Frequency domain features are prevalent and appear in the majority of the studies surveyed in the paper, in particular spectral power, but it was also found that its performance scores is lower compared to other features.…”

Section: Related Workmentioning

confidence: 99%

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Emotion detection from EEG recordings based on supervised and unsupervised dimension reduction

Liu

Meng

et al. 2018

Concurrency and Computation

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Summary In recent years, researchers have been trying to detect human emotions from recorded brain signals such as electroencephalogram (EEG) signals. However, due to the high levels of noise from the EEG recordings, a single feature alone cannot achieve good performance. A combination of distinct features is the key for automatic emotion detection. In this paper, we present a hybrid dimension feature reduction scheme using a total of 14 different features extracted from EEG recordings. The scheme combines these distinct features in the feature space using both supervised and unsupervised feature selection processes. Maximum Relevance Minimum Redundancy (mRMR) is applied to re‐order the combined features into max‐relevance with the labels and min‐redundancy of each feature. The generated features are further reduced with principal component analysis (PCA) for extracting the principal components. Experimental results show that the proposed work outperforms the state‐of‐art methods using the same settings in the publicly available DEAP data set.

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“…The central nervous system consists of spinal card and the brain. To analyze the signals and measure the emotions, there are various methods viz., Galvanic skin response (GSR), Electromyography (EMG), Heart rate(HR), Respiration Rate (RR) and Electro Encephalography(EEG) etc [4]. However, EEG is proved to be the direct mode of studying the brain data [5] [6].…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition Based on General-ized Gamma Distribution

.T¹,

Srinivas²,

Vadaparthi³

2018

IJET

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It is highly difficult to identify the emotions of a person. Literature accessible to recognize the emotions in case of immobilized personnel is limited to the outcome obtainable from the machines only. In this process, brain computer communication is utilized using neuro-scan machines like Encephalography (EEG), to recognize the feeling of immobilized persons. It uses the physiological signals accessible from EEG data extracted from the brain signals of immobilized personnel and tries to find out the emotions, but these results vary from machine to machine, and there exists no consistency by which one can identify the thoughts of the brain diseased personnel precisely. In this manuscript a novel technique is projected by using Generalized Gamma Mixture models (GGMM). The advantage of considering GGMM is its ability of extracting the emotions closely even in a noisy environment. The outcomes of the proposed method exceed the accuracy rates of conventional systems.

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Emotions Recognition Using EEG Signals: A Survey

Cited by 776 publications

References 108 publications

Emotional expression in psychiatric conditions: New technology for clinicians

Emotional expression in psychiatric conditions: New technology for clinicians

Emotion detection from EEG recordings based on supervised and unsupervised dimension reduction

Emotion Recognition Based on General-ized Gamma Distribution

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