Emotion detection using EEG signals
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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| Kieli: | English |
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Brac University
2021
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| Linkit: | http://hdl.handle.net/10361/14988 |
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10361-14988 |
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10361-149882022-01-26T10:20:04Z Emotion detection using EEG signals Hossain, Mohammad Adnan Akhondm, Mostafijur Rahman Department of Computer Science and Engineering, Brac University Emotion detection EEG signal Wavelet energy Wavelet entropy Deep Neural Network Support Vector Machine K-Nearest Neighbour Emotion This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 30-31). Emotions play a vital role in how people feel, think and act which makes it worthwhile for analyzing human behavior. As the patterns of emotions and their reflections differ from person to person, their study needs to be based on methods that are effective regardless of the diverse domain of the population. Hence, the analysis of physiological signals in detecting and extracting human emotions is gaining significance. To support this, resources and standards are being developed simultaneously. In this paper, we propose a pre-processing method along with some feature extractions and a model for emotion detection using EEG Signals based on DEAP dataset, a current benchmark for Emotion Classification research. For the pre-processing of data, prominent channels which contribute most to the classification are selected based on the role of the prefrontal cortex in emotion regulation and conscious experience and as for feature extractions, wavelet energy, wavelet entropy, and standard deviation are used. DNN (Deep Neural Network), SVM (Support Vector Machine), and KNN (K-Nearest Neighbour) are considered as the proposed model to detect emotions on a quadrant, HAHV (High Arousal and High Valence) or HALV (High Arousal and Low Valence) or LAHV (Low Arousal and High Valence) or LALV (Low Arousal and Low Valence). The approach we used yielded a maximum accuracy of 64%, 64%, and 70% for valence, arousal, and dominance respectively. Mohammad Adnan Hossain B. Computer Science 2021-09-08T10:26:48Z 2021-09-08T10:26:48Z 2021 2021-06 Thesis ID 18101262 http://hdl.handle.net/10361/14988 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. 31 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Emotion detection EEG signal Wavelet energy Wavelet entropy Deep Neural Network Support Vector Machine K-Nearest Neighbour Emotion |
| spellingShingle |
Emotion detection EEG signal Wavelet energy Wavelet entropy Deep Neural Network Support Vector Machine K-Nearest Neighbour Emotion Hossain, Mohammad Adnan Emotion detection using EEG signals |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Akhondm, Mostafijur Rahman |
| author_facet |
Akhondm, Mostafijur Rahman Hossain, Mohammad Adnan |
| format |
Thesis |
| author |
Hossain, Mohammad Adnan |
| author_sort |
Hossain, Mohammad Adnan |
| title |
Emotion detection using EEG signals |
| title_short |
Emotion detection using EEG signals |
| title_full |
Emotion detection using EEG signals |
| title_fullStr |
Emotion detection using EEG signals |
| title_full_unstemmed |
Emotion detection using EEG signals |
| title_sort |
emotion detection using eeg signals |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/14988 |
| work_keys_str_mv |
AT hossainmohammadadnan emotiondetectionusingeegsignals |
| _version_ |
1814309202111234048 |