Emotion Feature Analysis and Recognition Based on Reconstructed EEG Sources

Chen, Guijun; Zhang, Xueying; Sun, Ying; Zhang, Jing

doi:10.1109/access.2020.2966144

Cited by 45 publications

(28 citation statements)

References 38 publications

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“…For each trial, ( , , , ) and ( , , , ) are synchronously collected with the given sampling frequency ( 1 = 512 for EEG, 2 = 32 for facial expression) under the coordination of the dedicated server. According to the method and the implementation of building up EEG ground truth data set presented in Section Ⅲ , computer software tool SVSF is performed on ( , , , ) to segment the facial expression images into 60 × 32 = 1920 separated sequence frames ( , , , ), ∈ [1,1920]. And then the computer software tool BEEGLDS is performed on the ( , , , ), ∈ [1,1920] to recognize the special frame frame-s m th and special frame frame-e ℎ of the k th emotion.…”

Section: E Build Up Eeg Ground Truth Data Set Eeg-lmentioning

confidence: 99%

“…According to the method and the implementation of building up EEG ground truth data set presented in Section Ⅲ , computer software tool SVSF is performed on ( , , , ) to segment the facial expression images into 60 × 32 = 1920 separated sequence frames ( , , , ), ∈ [1,1920]. And then the computer software tool BEEGLDS is performed on the ( , , , ), ∈ [1,1920] to recognize the special frame frame-s m th and special frame frame-e ℎ of the k th emotion. The frames ( , , , ), ∈ [ , + 1, … , ] are taken as the facial expression ground truth data, and store them into the facial expression ground truth data set FE-L.…”

Section: E Build Up Eeg Ground Truth Data Set Eeg-lmentioning

confidence: 99%

“…Emotion is the human affective response to the external environment or specific stimulation [1]. Emotional recognition is the process of identifying human emotional state [2], which is currently attractive to more and more researchers because of its extensive and successive applications, such as robotics [3]- [4], rehabilitation [5]- [6], marketing analysis [7], human-machine interface [8]- [9], and adaptive e-learning system [10].…”

Section: Introductionmentioning

confidence: 99%

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Ground Truth Dataset for EEG-Based Emotion Recognition With Visual Indication

et al. 2020

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Along with extensive and successive applications, emotion recognition based on electroencephalogram has attracted more and more researchers. However, how to acquire sufficient and high-quality real emotion electroencephalogram to train emotion recognition model has always been a bottleneck issue in the electroencephalogram-based emotion recognition research field. Because the subject's emotion is easy to be affected by many factors and subjects do not always evoke emotion well, it's very hard to determine whether the real emotion electroencephalogram appears or not in the experiments. On the contrary to electroencephalogram, facial expression obtained without any intentional control is easy to be recognized by computers, and is also one of the primary and reliable cues for understanding emotions. Inspired by such a common sense, we proposed an approach to building up ground truth electroencephalogram dataset with visual indication. Firstly, the relationship between facial expression and electroencephalogram is analyzed in details from the viewpoints of biophysics and correlation. Secondly, based on the analysis result that when the subject's facial expression is evoked, it should be accompanied by the emotional electroencephalogram corresponding to it, the method of building up the ground truth electroencephalogram dataset with visual indication is put forward along with the computer automatic implementation. Thirdly, we have built up a ground truth electroencephalogram dataset which covers 3 kinds of emotions (i.e. joy, sadness, and neutral) of the subjects who are undergraduate and graduate students from Minzu University of China. Lastly, the validation of the established dataset is tested by the comparative experiments between the long short term memory emotion recognition models trained with two different datasets respectively. One dataset contains both truth and false electroencephalograms and another one only contains the truth data.

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Section: E Build Up Eeg Ground Truth Data Set Eeg-lmentioning

confidence: 99%

Section: E Build Up Eeg Ground Truth Data Set Eeg-lmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ground Truth Dataset for EEG-Based Emotion Recognition With Visual Indication

et al. 2020

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“…EEG estimates voltage variations because of ionic current within the neurons of the cerebrum. The state-of-the-art emotion recognition methods [ 6 , 7 ] can be broadly divided into three types namely, knowledge-based methods, statistical approaches, and hybrid techniques [ 8 ]. In knowledge-based, emotion detection is carried out by utilizing domain understanding and the semantic and syntactic features of language [ 9 ] using WordNet, SenticNet [ 10 ], ConceptNet, and EmotiNet [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

An EEG Database and Its Initial Benchmark Emotion Classification Performance

Seal

Reddy

Chaithanya

et al. 2020

Computational and Mathematical Methods in Medicine

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Human emotion recognition has been a major field of research in the last decades owing to its noteworthy academic and industrial applications. However, most of the state-of-the-art methods identified emotions after analyzing facial images. Emotion recognition using electroencephalogram (EEG) signals has got less attention. However, the advantage of using EEG signals is that it can capture real emotion. However, very few EEG signals databases are publicly available for affective computing. In this work, we present a database consisting of EEG signals of 44 volunteers. Twenty-three out of forty-four are females. A 32 channels CLARITY EEG traveler sensor is used to record four emotional states namely, happy, fear, sad, and neutral of subjects by showing 12 videos. So, 3 video files are devoted to each emotion. Participants are mapped with the emotion that they had felt after watching each video. The recorded EEG signals are considered further to classify four types of emotions based on discrete wavelet transform and extreme learning machine (ELM) for reporting the initial benchmark classification performance. The ELM algorithm is used for channel selection followed by subband selection. The proposed method performs the best when features are captured from the gamma subband of the FP1-F7 channel with 94.72% accuracy. The presented database would be available to the researchers for affective recognition applications.

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“…Russell’s valence–arousal model is a widely-recognized model of emotion, and in this model, emotions are represented in the space of two axes: valence (ranging from pleasant to unpleasant state) and arousal (ranging from excited to calm state), as illustrated in Figure 1 a [ 5 ]. Among many emotion classification studies based on EEG, some studies estimated source activities in the brain and performed classification using only emotion-related source signals [ 6 , 7 , 8 , 9 ]. Padilla-Buritica and colleagues reconstructed source-level signals from scalp EEG and classified emotions using signals extracted from the selected brain regions [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Independent Components of EEG Activity Correlating with Emotional State

et al. 2020

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Among brain-computer interface studies, electroencephalography (EEG)-based emotion recognition is receiving attention and some studies have performed regression analyses to recognize small-scale emotional changes; however, effective brain regions in emotion regression analyses have not been identified yet. Accordingly, this study sought to identify neural activities correlating with emotional states in the source space. We employed independent component analysis, followed by a source localization method, to obtain distinct neural activities from EEG signals. After the identification of seven independent component (IC) clusters in a k-means clustering analysis, group-level regression analyses using frequency band power of the ICs were performed based on Russell’s valence–arousal model. As a result, in the regression of the valence level, an IC cluster located in the cuneus predicted both high- and low-valence states and two other IC clusters located in the left precentral gyrus and the precuneus predicted the low-valence state. In the regression of the arousal level, the IC cluster located in the cuneus predicted both high- and low-arousal states and two posterior IC clusters located in the cingulate gyrus and the precuneus predicted the high-arousal state. In this proof-of-concept study, we revealed neural activities correlating with specific emotional states across participants, despite individual differences in emotional processing.

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Emotion Feature Analysis and Recognition Based on Reconstructed EEG Sources

Cited by 45 publications

References 38 publications

Ground Truth Dataset for EEG-Based Emotion Recognition With Visual Indication

Ground Truth Dataset for EEG-Based Emotion Recognition With Visual Indication

An EEG Database and Its Initial Benchmark Emotion Classification Performance

Independent Components of EEG Activity Correlating with Emotional State

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