Electroencephalogram based Emotion Recognition with Graph Convolutional Network Model
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
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| स्वरूप: | थीसिस |
| भाषा: | English |
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Brac University
2023
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| ऑनलाइन पहुंच: | http://hdl.handle.net/10361/18031 |
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10361-18031 |
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10361-180312023-03-28T21:01:50Z Electroencephalogram based Emotion Recognition with Graph Convolutional Network Model Islam, Md. Tahmidul Kabir, Abrar Chowdhury, Imtiaz Ahmed Afrin, Sadiya Nahin, Rakibul Alam Rabiul Alam, Dr. Md. Golam Rahman, Rafeed Department of Computer Science and Engineering, Brac University Emotional Intelligence Machinery activity EEG Emotion Brain signal Deep learning Hybrid model GCN. Machine Learning Electroencephalography Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 46-51). Recently, researchers have focused on understanding human sentiment using mechanical devices or reactions to any machinery activity. Computerization is becoming more prevalent in today’s environment. People are unaware of the proper way of expressing their emotions to others. People are unsure how to respond in some situations. Emotional intelligence is a collection of abilities that includes emotional awareness and self-control. In 1995, Daniel Goleman’s book Emotional Intelligence popularized the term. Emotional intelligence has five components: self-awareness, motivation, self-regulation, and social abilities. Emotion indicates a broad phrase that alludes to a human being’s cognitive or intelligible and psychological comeback to the perceived circumstances of another person. Emotional response or sensitivity towards others boosts one’s chances of assisting others and displaying sentiment. Some people have been traumatized, handicapped, or have a disability that makes it difficult for them to express themselves. Our goal is to evaluate human sentiment and the factors working behind emotions using EEG signals to identify a person’s feelings. We propose a deep learning-based approach with a hybrid model for detecting emotions such as happiness, sadness, etc. The electroencephalogram, abbreviation of EEG, is a medical evaluation that computes the electrical activity of the cerebrum using electrodes or wires placed on the scalp. Using EEG-based emotion recognition, the computer can see inside the user’s head to study their mental state. To achieve this goal, our mission is to discover the cognitive stimulation that plays a crucial role in generating happiness and sadness in the human brain via brain signals using Deep learning(DL) approach and hybrid Graph Convolutional Network(GCN) model. Md. Tahmidul Islam Abrar Kabir Imtiaz Ahmed Chowdhury Sadiya Afrin Rakibul Alam Nahin B. Computer Science 2023-03-28T07:47:20Z 2023-03-28T07:47:20Z 2022 2022-09 Thesis ID: 19101251 ID: 19101337 ID: 19101228 ID: 19101162 ID: 19101215 http://hdl.handle.net/10361/18031 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 51 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Emotional Intelligence Machinery activity EEG Emotion Brain signal Deep learning Hybrid model GCN. Machine Learning Electroencephalography Neural networks (Computer science) |
| spellingShingle |
Emotional Intelligence Machinery activity EEG Emotion Brain signal Deep learning Hybrid model GCN. Machine Learning Electroencephalography Neural networks (Computer science) Islam, Md. Tahmidul Kabir, Abrar Chowdhury, Imtiaz Ahmed Afrin, Sadiya Nahin, Rakibul Alam Electroencephalogram based Emotion Recognition with Graph Convolutional Network Model |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Rabiul Alam, Dr. Md. Golam |
| author_facet |
Rabiul Alam, Dr. Md. Golam Islam, Md. Tahmidul Kabir, Abrar Chowdhury, Imtiaz Ahmed Afrin, Sadiya Nahin, Rakibul Alam |
| format |
Thesis |
| author |
Islam, Md. Tahmidul Kabir, Abrar Chowdhury, Imtiaz Ahmed Afrin, Sadiya Nahin, Rakibul Alam |
| author_sort |
Islam, Md. Tahmidul |
| title |
Electroencephalogram based Emotion Recognition with Graph Convolutional Network Model |
| title_short |
Electroencephalogram based Emotion Recognition with Graph Convolutional Network Model |
| title_full |
Electroencephalogram based Emotion Recognition with Graph Convolutional Network Model |
| title_fullStr |
Electroencephalogram based Emotion Recognition with Graph Convolutional Network Model |
| title_full_unstemmed |
Electroencephalogram based Emotion Recognition with Graph Convolutional Network Model |
| title_sort |
electroencephalogram based emotion recognition with graph convolutional network model |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/18031 |
| work_keys_str_mv |
AT islammdtahmidul electroencephalogrambasedemotionrecognitionwithgraphconvolutionalnetworkmodel AT kabirabrar electroencephalogrambasedemotionrecognitionwithgraphconvolutionalnetworkmodel AT chowdhuryimtiazahmed electroencephalogrambasedemotionrecognitionwithgraphconvolutionalnetworkmodel AT afrinsadiya electroencephalogrambasedemotionrecognitionwithgraphconvolutionalnetworkmodel AT nahinrakibulalam electroencephalogrambasedemotionrecognitionwithgraphconvolutionalnetworkmodel |
| _version_ |
1814308626493341696 |