scholarly journals Fusion of Motif- and Spectrum-Related Features for Improved EEG-Based Emotion Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Abhishek Tiwari ◽  
Tiago H. Falk
Keyword(s):  
Emotion Recognition ◽  
Affective State ◽  
Emotional States ◽  
Score Level Fusion ◽  
State Recognition ◽  
Graph Theoretic ◽  
Recognition Motifs ◽  
Valence And Arousal ◽  
Asymmetry Measures ◽  
Robust To Noise

Emotion recognition is a burgeoning field allowing for more natural human-machine interactions and interfaces. Electroencephalography (EEG) has shown to be a useful modality with which user emotional states can be measured and monitored, particularly primitives such as valence and arousal. In this paper, we propose the use of ordinal pattern analysis, also called motifs, for improved EEG-based emotion recognition. Motifs capture recurring structures in time series and are inherently robust to noise, thus are well suited for the task at hand. Several connectivity, asymmetry, and graph-theoretic features are proposed and extracted from the motifs to be used for affective state recognition. Experiments with a widely used public database are conducted, and results show the proposed features outperforming benchmark spectrum-based features, as well as other more recent nonmotif-based graph-theoretic features and amplitude modulation-based connectivity/asymmetry measures. Feature and score-level fusion suggest complementarity between the proposed and benchmark spectrum-based measures. When combined, the fused models can provide up to 9% improvement relative to benchmark features alone and up to 16% to nonmotif-based graph-theoretic features.

Analyzing Emotional Oscillatory Brain Network for Valence and Arousal-Based Emotion Recognition Using EEG Data

2019 ◽  
Vol 18 (04) ◽  
pp. 1359-1378
Author(s):  
Jianzhuo Yan ◽  
Hongzhi Kuai ◽  
Jianhui Chen ◽  
Ning Zhong
Keyword(s):  
Emotion Recognition ◽  
Affective Computing ◽  
Affective State ◽  
Feature Selection Method ◽  
Emotional Valence ◽  
Brain Network ◽  
Previous Method ◽  
Emotional States ◽  
Eeg Data ◽  
Valence And Arousal

Emotion recognition is a highly noteworthy and challenging work in both cognitive science and affective computing. Currently, neurobiology studies have revealed the partially synchronous oscillating phenomenon within brain, which needs to be analyzed from oscillatory synchronization. This combination of oscillations and synchronism is worthy of further exploration to achieve inspiring learning of the emotion recognition models. In this paper, we propose a novel approach of valence and arousal-based emotion recognition using EEG data. First, we construct the emotional oscillatory brain network (EOBN) inspired by the partially synchronous oscillating phenomenon for emotional valence and arousal. And then, a coefficient of variation and Welch’s [Formula: see text]-test based feature selection method is used to identify the core pattern (cEOBN) within EOBN for different emotional dimensions. Finally, an emotional recognition model (ERM) is built by combining cEOBN-inspired information obtained in the above process and different classifiers. The proposed approach can combine oscillation and synchronization characteristics of multi-channel EEG signals for recognizing different emotional states under the valence and arousal dimensions. The cEOBN-based inspired information can effectively reduce the dimensionality of the data. The experimental results show that the previous method can be used to detect affective state at a reasonable level of accuracy.

scholarly journals Human Emotional State Assessment Based on a Video Portrayal

2020 ◽  
Vol 13 (4) ◽  
pp. 4-24 ◽  
Author(s):  
V.A. Barabanschikov ◽  
E.V. Suvorova
Keyword(s):  
Emotion Recognition ◽  
Recognition Test ◽  
Emotional State ◽  
State Assessment ◽  
Identification Accuracy ◽  
Emotional Expressions ◽  
Emotional States ◽  
Wide Range ◽  
Similarities And Differences ◽  
Valence And Arousal

The article is devoted to the results of approbation of the Geneva Emotion Recognition Test (GERT), a Swiss method for assessing dynamic emotional states, on Russian sample. Identification accuracy and the categorical fields’ structure of emotional expressions of a “living” face are analysed. Similarities and differences in the perception of affective groups of dynamic emotions in the Russian and Swiss samples are considered. A number of patterns of recognition of multi-modal expressions with changes in valence and arousal of emotions are described. Differences in the perception of dynamics and statics of emotional expressions are revealed. GERT method confirmed it’s high potential for solving a wide range of academic and applied problems.

scholarly journals Tracking the affective state of unseen persons

2019 ◽  
Vol 116 (15) ◽  
pp. 7559-7564 ◽  
Author(s):  
Zhimin Chen ◽  
David Whitney
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Social Functioning ◽  
Affective State ◽  
Visual Context ◽  
Unique Contribution ◽  
High Agreement ◽  
Human Ability ◽  
Valence And Arousal ◽  
Over Time

Emotion recognition is an essential human ability critical for social functioning. It is widely assumed that identifying facial expression is the key to this, and models of emotion recognition have mainly focused on facial and bodily features in static, unnatural conditions. We developed a method called affective tracking to reveal and quantify the enormous contribution of visual context to affect (valence and arousal) perception. When characters’ faces and bodies were masked in silent videos, viewers inferred the affect of the invisible characters successfully and in high agreement based solely on visual context. We further show that the context is not only sufficient but also necessary to accurately perceive human affect over time, as it provides a substantial and unique contribution beyond the information available from face and body. Our method (which we have made publicly available) reveals that emotion recognition is, at its heart, an issue of context as much as it is about faces.

Deep Learning of Empirical Mean Curve Decomposition-Wavelet Decomposed EEG Signal for Emotion Recognition

2020 ◽  
Vol 28 (01) ◽  
pp. 153-177 ◽  
Author(s):  
Sujata Bhimrao Wankhade ◽  
Dharmpal Dronacharya Doye
Keyword(s):  
Emotion Recognition ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Emotional States ◽  
Eeg Signal ◽  
Significant Information ◽  
State Recognition ◽  
Positive Rate ◽  
Sensitivity Specificity

Recently, the emotional state recognition of humans via Electroencephalogram (EEG) is one of the emerging topics that grasp the attention of researchers too. This EEG based recognition is normally an effective model for many of the real-time applications, especially for disabled people. A number of researchers are in progress to make the recognition model more effective in terms of accurate emotion recognition. However, it is not so satisfactory in the precise accurate progressing. Hence this paper intends to recognize the human emotional states or affects through EEG signals by adopting advanced features and classifier models. In the first stage of recognition procedure, this paper exploits 2501 (EMCD) and Wavelet Transformation to represent the EEG signal in low dimension as well as descriptive. By EMCD, the EEG redundancy can be neglected, and the significant information can be extracted. The classification processes using the extracted features with the aid of a classifier named Deep Belief Network (DBN). The performance of the proposed Wavelet-EMCD (WE) approach is analyzed in terms of measures such as Accuracy, Sensitivity, Specificity, Precision, False positive rate (FPR), False negative rate (FNR), Negative Predictive Value (NPV), False Discovery Rate (FDR), F1Score and Mathews correlation coefficient (MCC) and proven the superiority of proposed work in recognizing the emotions more accurately.

Emotional Reactivity to Visual Content as Revealed by ERP Component Clustering

2015 ◽  
Vol 29 (4) ◽  
pp. 135-146 ◽  
Author(s):  
Miroslaw Wyczesany ◽  
Szczepan J. Grzybowski ◽  
Jan Kaiser
Keyword(s):  
Emotional Reactivity ◽  
Brain Activity ◽  
Affective State ◽  
Occipital Lobe ◽  
Stimulus Onset ◽  
Emotional States ◽  
Neural Basis ◽  
Temporoparietal Junction ◽  
The Right ◽  
The Impact

Abstract. In the study, the neural basis of emotional reactivity was investigated. Reactivity was operationalized as the impact of emotional pictures on the self-reported ongoing affective state. It was used to divide the subjects into high- and low-responders groups. Independent sources of brain activity were identified, localized with the DIPFIT method, and clustered across subjects to analyse the visual evoked potentials to affective pictures. Four of the identified clusters revealed effects of reactivity. The earliest two started about 120 ms from the stimulus onset and were located in the occipital lobe and the right temporoparietal junction. Another two with a latency of 200 ms were found in the orbitofrontal and the right dorsolateral cortices. Additionally, differences in pre-stimulus alpha level over the visual cortex were observed between the groups. The attentional modulation of perceptual processes is proposed as an early source of emotional reactivity, which forms an automatic mechanism of affective control. The role of top-down processes in affective appraisal and, finally, the experience of ongoing emotional states is also discussed.

scholarly journals CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 52
Author(s):  
Tianyi Zhang ◽  
Abdallah El Ali ◽  
Chen Wang ◽  
Alan Hanjalic ◽  
Pablo Cesar
Keyword(s):  
Emotion Recognition ◽  
Electrodermal Activity ◽  
Short Form ◽  
Recognition Performance ◽  
Binary Classification ◽  
Wearable Sensors ◽  
Recognition Algorithm ◽  
Fine Grained ◽  
Physiological Sensors ◽  
Valence And Arousal

Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: 76.37% and 74.03% for V-A on CASE, and 70.29% and 68.15% for V-A on MERCA. Our findings show: (1) instance segment lengths between 1–4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (≤64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance.

scholarly journals Acting Surprised: Comparing Perceptions of Different Dynamic Deliberate Expressions

2020 ◽  
Author(s):  
Mircea Zloteanu ◽  
Eva G. Krumhuber ◽  
Daniel C. Richardson
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Affective State ◽  
External Condition ◽  
Internal Condition ◽  
Minimal Effort ◽  
Better Than

AbstractPeople are accurate at classifying emotions from facial expressions but much poorer at determining if such expressions are spontaneously felt or deliberately posed. We explored if the method used by senders to produce an expression influences the decoder’s ability to discriminate authenticity, drawing inspiration from two well-known acting techniques: the Stanislavski (internal) and Mimic method (external). We compared spontaneous surprise expressions in response to a jack-in-the-box (genuine condition), to posed displays of senders who either focused on their past affective state (internal condition) or the outward expression (external condition). Although decoders performed better than chance at discriminating the authenticity of all expressions, their accuracy was lower in classifying external surprise compared to internal surprise. Decoders also found it harder to discriminate external surprise from spontaneous surprise and were less confident in their decisions, perceiving these to be similarly intense but less genuine-looking. The findings suggest that senders are capable of voluntarily producing genuine-looking expressions of emotions with minimal effort, especially by mimicking a genuine expression. Implications for research on emotion recognition are discussed.

scholarly journals A EEG-based emotion recognition model with rhythm and time characteristics

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Jianzhuo Yan ◽  
Shangbin Chen ◽  
Sinuo Deng
Keyword(s):  
Emotion Recognition ◽  
Time Scales ◽  
Time Scale ◽  
Short Term Memory ◽  
Recognition Model ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Valence And Arousal ◽  
Memory Characteristics

Abstract As an advanced function of the human brain, emotion has a significant influence on human studies, works, and other aspects of life. Artificial Intelligence has played an important role in recognizing human emotion correctly. EEG-based emotion recognition (ER), one application of Brain Computer Interface (BCI), is becoming more popular in recent years. However, due to the ambiguity of human emotions and the complexity of EEG signals, the EEG-ER system which can recognize emotions with high accuracy is not easy to achieve. Based on the time scale, this paper chooses the recurrent neural network as the breakthrough point of the screening model. According to the rhythmic characteristics and temporal memory characteristics of EEG, this research proposes a Rhythmic Time EEG Emotion Recognition Model (RT-ERM) based on the valence and arousal of Long–Short-Term Memory Network (LSTM). By applying this model, the classification results of different rhythms and time scales are different. The optimal rhythm and time scale of the RT-ERM model are obtained through the results of the classification accuracy of different rhythms and different time scales. Then, the classification of emotional EEG is carried out by the best time scales corresponding to different rhythms. Finally, by comparing with other existing emotional EEG classification methods, it is found that the rhythm and time scale of the model can contribute to the accuracy of RT-ERM.

scholarly journals Multimodal affective state recognition in serious games applications

2016 ◽  
Author(s):  
Athanasios Psaltis ◽  
Kyriaki Kaza ◽  
Kiriakos Stefanidis ◽  
Spyridon Thermos ◽  
Konstantinos C. Apostolakis ◽  
...  
Keyword(s):  
Serious Games ◽  
Affective State ◽  
State Recognition

Fractal dimension pattern-based multiresolution analysis for rough estimator of speaker-dependent audio emotion recognition

2017 ◽  
Vol 15 (05) ◽  
pp. 1750042 ◽  
Author(s):  
Miao Cheng ◽  
Ah Chung Tsoi
Keyword(s):  
Fractal Dimension ◽  
Emotion Recognition ◽  
Multiresolution Analysis ◽  
Real Life ◽  
Emotional States ◽  
Acoustic Feature ◽  
Comparative Performance ◽  
Audio Analysis ◽  
The Arts ◽  
The Given

As a general means of expression, audio analysis and recognition have attracted much attention for its wide applications in real-life world. Audio emotion recognition (AER) attempts to understand the emotional states of human with the given utterance signals, and has been studied abroad for its further development on friendly human–machine interfaces. Though there have been several the-state-of-the-arts auditory methods devised to audio recognition, most of them focus on discriminative usage of acoustic features, while feedback efficiency of recognition demands is ignored. This makes possible application of AER, and rapid learning of emotion patterns is desired. In order to make predication of audio emotion possible, the speaker-dependent patterns of audio emotions are learned with multiresolution analysis, and fractal dimension (FD) features are calculated for acoustic feature extraction. Furthermore, it is able to efficiently learn the intrinsic characteristics of auditory emotions, while the utterance features are learned from FDs of each sub-band. Experimental results show the proposed method is able to provide comparative performance for AER.

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