dc.contributor.advisor | Uddin, Jia | |
dc.contributor.author | Hossain, Prommy Sultana Ferdawoos | |
dc.contributor.author | Shaikat, Istiaque Mannafee | |
dc.contributor.author | George, Fabian Parsia | |
dc.date.accessioned | 2018-05-22T03:44:50Z | |
dc.date.available | 2018-05-22T03:44:50Z | |
dc.date.copyright | 2018 | |
dc.date.issued | 2018-04 | |
dc.identifier.other | ID 16241002 | |
dc.identifier.other | ID 14101072 | |
dc.identifier.other | ID 14301059 | |
dc.identifier.uri | http://hdl.handle.net/10361/10188 | |
dc.description | This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 40-49). | |
dc.description.abstract | Over the years many groundbreaking research involving Brain Computer Interface (BCI), has been conducted in order to study emotions of human beings, to build better-quality human-machine interaction systems. On the other hand, it is also quite possible to log the activities of brain in real-time and then use it to distinguish patterns related to emotional status. BCI creates a mutual understanding between the users and its environment for measuring emotions through brain activities. Electroencephalogram (EEG) is a well-accepted method to measure the brain activities. Once the system records the EEG signals, we analyze and process these activities to distinguish different emotions. Previous researchers used standard and pre-defined methods of signal processing area with fewer channels and participations to record their EEG signals. In this thesis, a novel method was proposed that extracted features from EEG signals based on time-frequencies analysis and supervised learning algorithm was used to classify different emotional states. Our proposed method provides 92.36% accuracy by using a benchmark dataset, where 32 participants were used to carry out this experiment. | en_US |
dc.description.statementofresponsibility | Prommy Sultana Ferdawoos Hossain | |
dc.description.statementofresponsibility | Fabian Parsia George | |
dc.description.statementofresponsibility | Istiaque Mannafee Shaikat | |
dc.format.extent | 49 pages | |
dc.language.iso | en | en_US |
dc.publisher | BRAC University | en_US |
dc.rights | 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. | |
dc.subject | Brain Computer Interface (BCI) | en_US |
dc.subject | DEAP | en_US |
dc.subject | IAPS | en_US |
dc.subject | Emotion recognition | en_US |
dc.subject | Brian signals | en_US |
dc.subject | Learning algorithm | en_US |
dc.title | Emotion recognition using brian signals based on time-frequency analysis and supervised learning algorithm | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, BRAC University | |
dc.description.degree | B. Computer Science and Engineering
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