Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty

By Guillaume Chanel, Cyril Rebetez, Mireille Bétrancourt, Thierry Pun. IEEE transactions on systems, man and cybernetics. Part A. Systems and humans, 2011

423 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/04ad799a08505c2c6e45bd82234f44b2e716e98c

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In Cognitive Neuroscience

In Experimental and Cognitive Psychology

Citations in the corpus

Multimodal Emotion Recognition in Response to Videos (2012, background): “Although EEG classification results are inferior to the eye gaze data, they are comparable to the state-of-the-art classification rates considering the interparticipant classification scheme [25], [52].”
Enabling Effective Programming and Flexible Management of Efficient Body Sensor Network Applications (2013, background): “Other projects process only the GSR signal (however, high accuracy is reached only to detect arousal) or use facial expression to derive emotions [16], [17], although video cameras or EMG electrodes placed on the face are required in this case.”
AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups (2017, background): “For instance, Chanel et al. [2] modiﬁed the difﬁculty of a video game according to user’s emotional state to maintain engagement.”
Mood measurement with Pick-A-Mood: review of current methods and design of a pictorial self-report scale (2016, neutral): “Due to rapid improvements in the accuracy of equipment and data analysis techniques, future devices may also be able to measure valance, and perhaps even some basic emotions (see Chanel et al., 2011), but this does not apply to methods that are currently available.”
Exploring EEG Features in Cross-Subject Emotion Recognition (2018, background): “For example, in human-computer interaction (HCI), recognized user emotion can be utilized as a kind of feedback to provide better content to enhance the user experiences in e-learning, computer games, and information retrieval (Mao and Li, 2009; Chanel et al., 2011; Moshfeghi, 2012).”
EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation (2014, result): “Other machine learning techniques, such as naive Bayes, K-NN, LDA, and ANN, have been applied in other studies [12–15].”
A Review and Meta-Analysis of Multimodal Affect Detection Systems (2015, background): “2013], Bayesian belief integration [Chanel et al. 2011], and Markov decision networks [Krell et al. 2013]. There was considerable variation in model-level fusion methods, but bidirectional long short-term memories [Eyben et al. 2010; Metallinou et al. 2012; Wöllmer et al. 2010, 2013a], various HMM-b”
Emotion Recognition based on EEG using LSTM Recurrent Neural Network (2017, background): “[5] proposed a new approach for adapting game difficulty according to the current emotion of the player.”