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Cross-Domain EEG-Based Emotion Recognition

Shirkarami, Mohsen | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56427 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Mohammadzadeh, Hoda
  7. Abstract:
  8. The non-stationary nature of brain activity signals and their many inter-subject differences have created many challenges in the practical applications of emotion recognition based on electroencephalogram (EEG) signals, such as brain-computer interfaces. In such a way, the use of traditional classifiers in classifying these signals leads to a significant decrease in accuracy when applying the classifier to a new subject. Domain Adaptation methods seem to be an effective way to solve this problem by minimizing the difference between the EEG signals of different subjects. But in the basic techniques for domain adaptation, looking at all subjects' data in the same look causes the loss of a part of the potential power of these methods. In the present study, after examining the different structures and approaches of Domain Adaptation, such as cross-session and cross-subject, we present and implement an extended version of the Subspace Alignment method for Domain Adaptation utilizing clustering of source subjects. This method can make an effective choice between the available data from the source domain by clustering the source subjects based on the behavioral similarity of their EEG signals. This effective selection among the subjects, which is done by defining and implementing several different criteria for choosing the optimal cluster, as well as the idea of purposeful data weighting, have caused the final model to represent the space of the target subject more effectively. To confirm the feasibility of the proposed method, we have conducted experiments on the SEED dataset, which is used in the emotion recognition EEG-based task, and it achieved an accuracy of 84.3%. The results show that the presented model has better classification accuracy than several state-of-the-art models
  9. Keywords:
  10. Emotion Recognition ; Deep Neural Networks ; Machine Learning ; Domain Adaptation ; Electroencephalogram Signals Classification ; Subspace Alignment ; Brain-Computer Interface (BCI)

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