Emotion elicitation and EEG acquisition

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Table of contents

1 Introduction 
1.1 Stimuli choice
1.2 Emotion annotation
1.3 Factors of variability for the EEG response
1.4 Objective and contributions
1.5 Organization of the document
2 Baseline EEG emotion classification 
2.1 Emotion elicitation and EEG acquisition
2.1.1 Specific requirements
2.2 EEG-based affective datasets
2.3 Commonly used features for EEG-based emotion classification
2.3.1 Time domain features versus time-frequency domain features
2.3.2 Exploiting spatial information
2.4 Classifier training and evaluation metrics
2.5 Influence of feature choice and other parameters on classification results .
2.5.1 Extending the observation window of the signal
2.5.2 Impact of feature choice
2.5.3 Choice of classifier
2.5.4 Inter-subject classification
2.5.5 Threshold choice for valence and arousal classes
2.6 Conclusion
3 Group Nonnegative Matrix Factorization for EEG-based emotion recognition
3.1 Nonnegative Matrix Factorization
3.1.1 General principle
3.1.2 Divergence minimization
3.1.3 Specific use to EEG
3.2 Results obtained with NMF and conclusions
3.2.1 Intra-session classification
3.2.2 Inter-session classification
3.3 Group NMF
3.3.1 General method
3.3.2 Specific use to EEG
3.4 Results obtained with GNMF and conclusions
4 EEG-based Inter-Subject Correlation Schemes in a Stimuli-Shared Framework : Interplay with Valence and Arousal 
4.1 The ISC principle
4.1.1 ISC score computation
4.1.2 Averaging Ri j to compute ISC eigenvectors
4.1.3 Shrinkage
4.2 Different ISC computational schemes
4.2.1 Comparing subject signals globally vs pairwise
4.2.2 Choosing the data on which to compute the eigenvectors
4.3 Studying the effects of emotion on ISC
4.3.1 Assessing pairwise agreement
4.3.2 Assigning a subject pairwise annotation for a given stimulus when there is agreement
4.3.3 Effects of valence and arousal on ISC
4.4 Results on HCI MAHNOB
4.4.1 Results with Vall
4.4.2 Results with Vstim/pair
4.4.3 Linking the ISC level to the annotation agreement
4.5 Results on DEAP
4.5.1 Results with Vall
4.5.2 Results with Vstim/pair
4.6 Further discussion
4.6.1 Agreement is arbitrarily defined
4.6.2 ISC score variation from one scheme to another
4.6.3 Differences of ISC score variations along valence between HCI MAHNOB and DEAP
4.6.4 Effects of shrinkage
4.7 Conclusions
5 Towards an ISC-oriented Group Nonnegative Matrix Factorization for EEG-based emotion recognition 
5.1 Multi-task GNMF-based feature learning
5.2 Results obtained with valence/arousal-based GNMF
5.3 Taking ISC into account explicitly
5.4 Conclusion
6 Conclusion 
6.1 Conclusion and discussion
6.2 Outlook
Bibliographie