Enhancing Electroencephalography Classifications and Decoding Accuracy of Emotion Recognition Using Machine Learning Submitted In Partial Fulfillment of The Requirements For The

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AuthorAlSallout, Mohammed Nabeel
metadata.dc.contributor.departmentUniversity of Bahrain College of Engineering Department of Electrical and Electronics Engineering
metadata.dc.date.accessioned2026-09-20T15:51:54Z
Publication date2023
Degree levelDoctoral
AbstractAbstract: This thesis explores various machine learning and deep learning methods for building real-time emotion recognition systems based on Electroencephalogram (EEG) signals. We aim to enhance the classification and decoding of EEG signals at a large scale and to bridge the gap between basic EEG research and real-world applications. We use some of the most popular EEG datasets for emotions and investigate how the data randomization affects the results. We also propose a novel adaptive channel selection method that uses different filtering techniques and recursive performance ranking with transfer learning to find the top-N ranked channels. We introduce a new feature extraction and selection approach that uses transfer learning to overcome overfitting problems caused by using local data. We divide features into centralized and non-centralized groups, where the centralized ones are more accurate but more resource-intensive. We compare several supervised and unsupervised classification methods, such as Decision Tree, Nearest Neighbor, Support Vector Machine, Naive Bayes, Hierarchical Clustering, and K- mean Clustering. We also evaluate several deep learning methods, such as artificial and deep neural networks. We design our model to have simple convolutions that work well with small samples and to have consistent input and output formats for real-time knowledge transfer and weight forwarding. We leverage the power of Convolutional Neural Networks (CNN) and Recurrent Neural Networks for automatic feature extraction and selection in both centralized and decentralized models. These models can handle different input data formats depending on the available resources and the decision constraints, leading to different levels of accuracy and performance. In summary, we develop and test a robust emotion recognition solution that can utilize modern application architecture, large data flow, and communication capabilities of different network components. This opens up new possibilities for research that is relevant to industry.
metadata.dc.identifier.otherhttps://digitalrepository.uob.edu.bh/id/3445d125-1b18-42a0-9a10-34675b539e36
metadata.dc.identifier.otherDrupal node 2593
metadata.dc.identifier.otherhttps://digitalrepository.uob.edu.bh/en/dar/enhancing-electroencephalography-classifications-and-decoding-accuracy-emotion-recognition
metadata.dc.identifier.urihttp://165.227.56.122/handle/123456789/7220
Languageen
Linkhttps://digitalrepository.uob.edu.bh/en/_flysystem/fedora/2025-01/065-enhancing-electroencephalography-classifications-and-decoding-accuracy-of-emotion-recognition-using-machine-learning.pdf
Subjectelectroencephalogram
SubjectEEG Classification
Subjectbrain-computer interfaces
SubjectBCI applications
Subjectemotion detection
SubjectSignal processing -- Essays
Subjectfiltering
SubjectFeature extraction -- Essays
Subjectclassification, clustering
TitleEnhancing Electroencephalography Classifications and Decoding Accuracy of Emotion Recognition Using Machine Learning Submitted In Partial Fulfillment of The Requirements For The
TypeDoctoral Dissertation
CollegeCollege of Engineering
DepartmentElectrical and Electronics Engineering

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