Predicting Exact Valence and Arousal Values from EEG

Galvão, Filipe Miguel Dias; Alarcão, Soraia M.; Fonseca, Manuel J.

doi:10.3390/s21103414

Cited by 52 publications

(39 citation statements)

References 59 publications

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“…Another way is to use the existing, well-known database in the field of emotion recognition based on EEG, including DEAP ( Izquierdo-Reyes et al, 2018 ), MAHNOB-HCI ( Izquierdo-Reyes et al, 2018 ), GAMEEMO ( Özerdem and Polat, 2017 ), SEED ( Lu et al, 2020 ), LUMED ( Cimtay and Ekmekcioglu, 2020 ), AMIGOS ( Galvão et al, 2021 ), and DREAMER ( Galvão et al, 2021 ). After obtaining the original EEG signal related to emotion states, the following operation is to preprocess the EEG signal to improve the quality of the EEG data.…”

Section: Acquisition Of Electroencephalography Signals For Emotion Recognitionmentioning

confidence: 99%

“…Unlike researches listed in Table 1 , which only identified a limited set of emotional states (e.g., happiness, sadness, anger, etc. ), Galvão et al (2021) were dedicated to predicting the exact values of valence and arousal in a subject-independent scenario. The systematic analysis revealed that the best prediction model was a KNN regressor ( K = 1) with Manhattan distance, features from the alpha, beta, gamma bands, and the differential asymmetry from the alpha band.…”

Section: Classification Of Emotion-related Electroencephalography Signalsmentioning

confidence: 99%

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Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

Cai

Xiao

Cui

et al. 2021

Front. Syst. Neurosci.

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Emotion recognition has become increasingly prominent in the medical field and human-computer interaction. When people’s emotions change under external stimuli, various physiological signals of the human body will fluctuate. Electroencephalography (EEG) is closely related to brain activity, making it possible to judge the subject’s emotional changes through EEG signals. Meanwhile, machine learning algorithms, which are good at digging out data features from a statistical perspective and making judgments, have developed by leaps and bounds. Therefore, using machine learning to extract feature vectors related to emotional states from EEG signals and constructing a classifier to separate emotions into discrete states to realize emotion recognition has a broad development prospect. This paper introduces the acquisition, preprocessing, feature extraction, and classification of EEG signals in sequence following the progress of EEG-based machine learning algorithms for emotion recognition. And it may help beginners who will use EEG-based machine learning algorithms for emotion recognition to understand the development status of this field. The journals we selected are all retrieved from the Web of Science retrieval platform. And the publication dates of most of the selected articles are concentrated in 2016–2021.

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Section: Acquisition Of Electroencephalography Signals For Emotion Recognitionmentioning

confidence: 99%

Section: Classification Of Emotion-related Electroencephalography Signalsmentioning

confidence: 99%

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

Cai

Xiao

Cui

et al. 2021

Front. Syst. Neurosci.

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“…Poor-quality sleep negatively affects work performance [ 1 ] as well as emotional states [ 2 , 3 ]. A common measure of poor-quality sleep is sleep arousals [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning

Chien

Tsao

2021

Sensors

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Poor-quality sleep substantially diminishes the overall quality of life. It has been shown that sleep arousal serves as a good indicator for scoring sleep quality. However, patients are conventionally asked to perform overnight polysomnography tests to collect their physiological data, which are used for the manual judging of sleep arousals. Even worse, not only is this process time-consuming and cumbersome, the judgment of sleep-arousal events is subjective and differs widely from expert to expert. Therefore, this work focuses on designing an automatic sleep-arousal detector that necessitates only a single-lead electroencephalogram signal. Based on the stacking ensemble learning framework, the automatic sleep-arousal detector adopts a meta-classifier that stacks four sub-models: one-dimensional convolutional neural networks, recurrent neural networks, merged convolutional and recurrent networks, and random forest classifiers. This meta-classifier exploits both advantages from deep learning networks and conventional machine learning algorithms to enhance its performance. The embedded information for discriminating the sleep-arousals is extracted from waveform sequences, spectrum characteristics, and expert-defined statistics in single-lead EEG signals. Its effectiveness is evaluated using an open-accessed database, which comprises polysomnograms of 994 individuals, provided by PhysioNet. The improvement of the stacking ensemble learning over a single sub-model was up to 9.29%, 7.79%, 11.03%, 8.61% and 9.04%, respectively, in terms of specificity, sensitivity, precision, accuracy, and area under the receiver operating characteristic curve.

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“…Different models or theories have been developed and used by psychologists or cognitive neuroscientists to distinguish between emotions based on facial expressions. The categorization of human emotions is mainly based on two perspectives: discrete and dimensional [ 11 ]. In the dimensional perspective, emotions are represented by the valence and arousal dimensions [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…In the dimensional perspective, emotions are represented by the valence and arousal dimensions [ 12 ]. The valence dimension indicates the intrinsic attractiveness or aversion of an event, object, or situation, and it varies from negative to positive [ 11 ]. Arousal, on the other hand, indicates whether the subject is responsive at that given moment and for that given stimulus, as well as how active he/she is.…”

Section: Introductionmentioning

confidence: 99%

Improving Human–Robot Interaction by Enhancing NAO Robot Awareness of Human Facial Expression

Filippini

Perpetuini

Cardone

2021

Sensors

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An intriguing challenge in the human–robot interaction field is the prospect of endowing robots with emotional intelligence to make the interaction more genuine, intuitive, and natural. A crucial aspect in achieving this goal is the robot’s capability to infer and interpret human emotions. Thanks to its design and open programming platform, the NAO humanoid robot is one of the most widely used agents for human interaction. As with person-to-person communication, facial expressions are the privileged channel for recognizing the interlocutor’s emotional expressions. Although NAO is equipped with a facial expression recognition module, specific use cases may require additional features and affective computing capabilities that are not currently available. This study proposes a highly accurate convolutional-neural-network-based facial expression recognition model that is able to further enhance the NAO robot’ awareness of human facial expressions and provide the robot with an interlocutor’s arousal level detection capability. Indeed, the model tested during human–robot interactions was 91% and 90% accurate in recognizing happy and sad facial expressions, respectively; 75% accurate in recognizing surprised and scared expressions; and less accurate in recognizing neutral and angry expressions. Finally, the model was successfully integrated into the NAO SDK, thus allowing for high-performing facial expression classification with an inference time of 0.34 ± 0.04 s.

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Predicting Exact Valence and Arousal Values from EEG

Cited by 52 publications

References 59 publications

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning

Improving Human–Robot Interaction by Enhancing NAO Robot Awareness of Human Facial Expression

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