Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection

By Mohammad Soleymani, Sadjad Asghari-Esfeden, Yun Raymond Fu, Maja Pantic. IEEE Transactions on Affective Computing, 2016

495 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/2fcb530b7dcd8f6b3b94b9cd4a97b5afcdcd5b2a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Peng Xu
2. Naeem Ramzan
3. Stamos Katsigiannis
4. Weifeng Ge
5. Dezhong Yao
6. Pritam Sarkar
7. Ali Etemad

In Experimental and Cognitive Psychology

1. Yan Ge
2. Peng Xu
3. Naeem Ramzan
4. Yuanchun Shi
5. Minjing Yu
6. Stamos Katsigiannis
7. Guozhen Zhao
8. Jinjing Song

In Human-Computer Interaction

1. Peng Xu
2. Naeem Ramzan
3. Stamos Katsigiannis

Citations in the corpus

• Real-Time Movie-Induced Discrete Emotion Recognition from EEG Signals (2018, methodology): “tion [8], [15], [16], [17], [29], [30], [31], we chose this system to collect EEG signals.”
• DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices (2018, methodology): “posed in [1], [11], [12], [37], leading to a total of 42 features (3 for each of the 14 electrodes).”
• Physiological Signals-based Emotion Recognition via High-order Correlation Learning (2019, background): “The first two of the three modes of input are determined by human will, and there are significant individual differences [35].”
• A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “Model-based [35] Feature-level [397-400] Decision-level [352, 389-391]”
• A survey of sentiment analysis in social media (2018, methodology): “[114] presented an approach on detecting the emotions of video viewers’ emotions from electroencephalogram (EEG) signals and facial expressions, in which long short-term memory recurrent neural networks (LSTM-RNN) and continuous conditional random fields (CCRF) were utilized on detecting emotions au”
• A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition (2017, methodology): “In terms of automatic multimodal emotion recognition, Zeng et al. [29] conducted spontaneous emotion detection from audio–visual modalities using AdaBoost multistream HMM. Soleymani et al. [30] performed continuous emotion recognition from electroencephalogram (EEG) signals and facial expressions.”
• Emotions Recognition Using EEG Signals: A Survey (2019, unknown): “[121] 2016 x x x x x x x x 57.”
• EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations (2019, methodology): “Furthermore, they fused the PSD and facial features to improve the classification performance for continuous emotion recognition [9].”