Short-term emotion assessment in a recall paradigm

By Guillaume Chanel, Joep J. M. Kierkels, Mohammad Soleymani, Thierry Pun. Int. J. Hum. Comput. Stud., 2009

387 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/cb106189af42394da270c2917c4b4d38af3a2e12

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In Cognitive Neuroscience

In Experimental and Cognitive Psychology

Citations in the corpus

EEG-Based Emotion Recognition in Music Listening (2010, methodology): “[20] reported an average accuracy of 63% by using EEG time-frequency information”
Emotions Recognition Using EEG Signals: A Survey (2019, background): “[29] Band pass frequency filter with a bandwidth of 4-45 Hz; laplacian reference [40] Band pass frequency filter with a bandwidth of 4-45 Hz; laplacian reference [34] Remove segments with EOG [42] Band pass frequency filter with a bandwidth of 1-100 Hz [43] Band pass frequency filter with a bandwidt”
A Multimodal Database for Affect Recognition and Implicit Tagging (2012, methodology): “In total, 102 features were extracted from peripheral physiological responses based on the proposed features in the literature [34], [33], [30].”
Categorical and dimensional affect analysis in continuous input: Current trends and future directions (2013, background): “, [32]), while on the other hand obtaining a reliable”
Human Emotion Recognition: Review of Sensors and Methods (2020, neutral): “com), plethysmograph to measure blood pressure [91]”
Multimodal Emotion Recognition in Response to Videos (2012, background): “[3] M. Soleymani, G. Chanel, J. J. M. Kierkels, and T. Pun,“ Affective Characterization of Movie Scenes Based on Content Analysis and Physiological Changes,”International Journal of Semantic Computing, vol. 3, no. 2, pp. 235-254, June 2009.”
A Review of Emotion Recognition Using Physiological Signals (2018, methodology): “Moreover, a recall paradigm and autobiographical memories were used in [19,20] respectively.”
Emotion representation, analysis and synthesis in continuous space: A survey (2011, background): “[42] reported large differences in accuracy between the EEG and peripheral features which may be due to the fact that the 8 s length of trials may be too short for a complete activation of peripheral signals while it may be sufficient for EEG signals.”
Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty (2011, background): “In [16], three self-induced emotional states were recognized with an accuracy of 68%.”
A Novel Bi-Hemispheric Discrepancy Model for EEG Emotion Recognition (2019, background): “Thus, the asymmetry on the frontal brain areas may mainly reflect the differential information that is helpful for emotion recognition [43]–[46].”