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

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

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.

روابط وملفات

التعريف والنوع

رقم الوثيقة

edaf9d59-6ac5-473e-bf40-0fdf8a991554

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل دكتوراة

نوع الملف

pdf text

أسماء الملفات

edaf9d59-6ac5-473e-bf40-0fdf8a991554_1.pdf

بيانات النشر

ترجمة العنوان

تحسين دقة التعرف على المشاعر وفك تشفيرها عبر اشارات الدماغ الكهربائية باستخدام تعلم الالة

ألقاب المؤلفين

[{"name_ar":" Ebrahim Mattar","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":" Mohammed Nabeel AlSallout","title_ar":"اعداد","title_en":"Preparation"}]

اللغة

English

المصدر والدورية

اسم المصدر

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

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

electroencephalogram EEG Classification brain-computer interfaces BCI applications emotion detection Signal processing -- Essays filtering Feature extraction -- Essays classification, clustering

إشراف وإعداد

الإشراف

Ebrahim Mattar

الإعداد

Mohammed Nabeel AlSallout

الاقتباسات الببليوغرافية