Emotion recognition using brain activity
By Robert Horlings, Dragos D. Datcu, Léon J. M. Rothkrantz. International Conference on Computer Systems and Technologies, 2008
304 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/947c458209891297a3e3653b1c0ea888b5541a23
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
- Zhaoxiang Zhang
- Olga Sourina
- Leontios J. Hadjileontiadis
- Angelika Peer
- Martin Buss
- Jinpeng Li
- Robert Jenke
- Panagiotis C. Petrantonakis
In Experimental and Cognitive Psychology
- Zhaoxiang Zhang
- Olga Sourina
- Leontios J. Hadjileontiadis
- Angelika Peer
- Martin Buss
- Jinpeng Li
- Robert Jenke
- Panagiotis C. Petrantonakis
Citations in the corpus
- Feature Extraction and Selection for Emotion Recognition from EEG (2014, methodology): “MSCE features are equivalent to crosscorrelation coefficients used in earlier studies [3], [7], [20].”
- Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition (2017, background): “there was still a lot of diversity among different people and circumstances [40].”
- Emotion Recognition From EEG Using Higher Order Crossings (2010, background): “Despite the classification potential of EEG for such kind of recognition reflected in the works reported in the literature [17], [18], [22]–[24], further research is needed in order to improve recognition rates and discover unknown aspects of emotion mechanisms performed in the human brain.”
- Real-Time EEG-Based Human Emotion Recognition and Visualization (2010, methodology): “SVM was applied in work [3] for emotion classification with the accuracy for valence and arousal identification as 32% and 37% respectively.”