Stress detection for visually impaired people using EEG signals based on extracted features from time-frequency domain
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2022.
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| Médium: | Diplomová práce |
| Jazyk: | English |
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Brac University
2023
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| On-line přístup: | http://hdl.handle.net/10361/17699 |
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10361-17699 |
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10361-176992023-01-09T21:01:45Z Stress detection for visually impaired people using EEG signals based on extracted features from time-frequency domain Sultana, Samia Parvez, Mohammad Zavid Department of Computer Science and Engineering, Brac University EMD IMF Stress STFT Beta band Brain-computer interfaces Computational intelligence Time-domain analysis This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 28-32). Stress refers to body's physical, emotional and psychological reaction to any environmental change needing adjustment with major impact on human psychology. Stress is specially di cult to manage for visually impaired people (VIP) as they can become easily stressed in unknown situations. Electroencephalogram (EEG) signals can be used to detect stress as it basically represents the ongoing electrical signal changes in human brain. Literature shows that the stress detection techniques are mostly based on either time or frequency domain analysis. However, using either time or frequency domain analysis may not be su cient to provide appropriate outcome for stress detection. Hence, in this paper a method is proposed using empirical mode decomposition (EMD) and short-term Fourier transform (STFT) are used to extract features considering spatio-temporal information from EEG signals. In the EMD, the signal is rst decomposed into intrinsic mode functions (IMFs) representing a nite number of signals while maintaining the time domain and STFT is used to convert time domain to time-frequency domain. Support vector machine (SVM) is applied to classify the stress of VIP in unfamiliar indoor environments. The performance of the proposed method is compared with a state-of-the-art technique for stress detection. The experimental results demonstrate the superiority of the proposed technique over the existing technique. Samia Sultana M. Computer Science and Engineering 2023-01-09T08:50:25Z 2023-01-09T08:50:25Z 2023 2022-06 Thesis ID 18366003 http://hdl.handle.net/10361/17699 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. 32 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
EMD IMF Stress STFT Beta band Brain-computer interfaces Computational intelligence Time-domain analysis |
| spellingShingle |
EMD IMF Stress STFT Beta band Brain-computer interfaces Computational intelligence Time-domain analysis Sultana, Samia Stress detection for visually impaired people using EEG signals based on extracted features from time-frequency domain |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2022. |
| author2 |
Parvez, Mohammad Zavid |
| author_facet |
Parvez, Mohammad Zavid Sultana, Samia |
| format |
Thesis |
| author |
Sultana, Samia |
| author_sort |
Sultana, Samia |
| title |
Stress detection for visually impaired people using EEG signals based on extracted features from time-frequency domain |
| title_short |
Stress detection for visually impaired people using EEG signals based on extracted features from time-frequency domain |
| title_full |
Stress detection for visually impaired people using EEG signals based on extracted features from time-frequency domain |
| title_fullStr |
Stress detection for visually impaired people using EEG signals based on extracted features from time-frequency domain |
| title_full_unstemmed |
Stress detection for visually impaired people using EEG signals based on extracted features from time-frequency domain |
| title_sort |
stress detection for visually impaired people using eeg signals based on extracted features from time-frequency domain |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/17699 |
| work_keys_str_mv |
AT sultanasamia stressdetectionforvisuallyimpairedpeopleusingeegsignalsbasedonextractedfeaturesfromtimefrequencydomain |
| _version_ |
1814308477423583232 |