Prediction and detection in change of cognitive load for VIP’s by a machine learning approach
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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| Język: | English |
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Brac University
2021
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| Dostęp online: | http://hdl.handle.net/10361/14989 |
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10361-149892022-01-26T10:18:18Z Prediction and detection in change of cognitive load for VIP’s by a machine learning approach Rahman, Fahim Ahmed, Md.Istiyak Saad, Saif Shahnewaz Ashrafuzzaman, Md Mogno, Sharita Shehnaz Parvez, Mohammad Zavid Rahman, Rafeed Department of Computer Science and Engineering, Brac University Cognitive load Machine Learning Supervised Learning EEG Performance parameters Alpha Beta ratio Gradient Boost Algorithm ERDS Machine learning 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 52-58). The significance and urgency of detecting cognitive load of Visually Impaired Person is essential when perception comes while designing an automated navigation aid for them in unfamiliar indoor environments.Our paper presents a novel, robust and multidimensional framework based on iterative feature pooling technique which recursively selects paramount features that maintains relation with the change in cognitive load of the brain. We have chosen to use Electroencephalogram as it is one of the fastest imaging techniques available having a high sampling rate and analytical neuro-psychologic benchmarks of perceptive process indicated by rhythmic activities of the brain. We took the well established ERDS method for indexing the cognitive load and further developed the work by operating with the band power of not only the Alpha wave but the Alpha Beta ratio band power and Alpha Theta ratio band power.The intricacy of the tasks in terms of cognitive load were quantified considering multiple aspects to support the redemption of usability of a way finding aid by features extraction from specific attributes, some of which were new to this field, to support the vindication of accessibility of a way finding aid.As the machine learning classifier the Gradient Boost outperformed all other classifiers(94% accuracy). We considered other performance parameters like the f-1 score,recall, time delay, sensitivity and false positive rate to evaluate the performance of all available supervised ML classifiers.This chapter marks out the estimation of based on existing literature, background, leeway, characteristics, and machine learning approaches, cognitive load was calculated using EEG data. Fahim Rahman Md. Istiyak Ahmed Saif Shahnewaz Saad Md Ashrafuzzaman Sharita Shehnaz Mogno B. Computer Science 2021-09-08T11:38:43Z 2021-09-08T11:38:43Z 2921 2021-06 Thesis ID 17101500 ID 16201021 ID 16101181 ID 16101110 ID 21141040 http://hdl.handle.net/10361/14989 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. 58 pages application/pdf Brac University |
| institution |
Brac University |
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Institutional Repository |
| language |
English |
| topic |
Cognitive load Machine Learning Supervised Learning EEG Performance parameters Alpha Beta ratio Gradient Boost Algorithm ERDS Machine learning |
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Cognitive load Machine Learning Supervised Learning EEG Performance parameters Alpha Beta ratio Gradient Boost Algorithm ERDS Machine learning Rahman, Fahim Ahmed, Md.Istiyak Saad, Saif Shahnewaz Ashrafuzzaman, Md Mogno, Sharita Shehnaz Prediction and detection in change of cognitive load for VIP’s by a machine learning approach |
| 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 |
Parvez, Mohammad Zavid |
| author_facet |
Parvez, Mohammad Zavid Rahman, Fahim Ahmed, Md.Istiyak Saad, Saif Shahnewaz Ashrafuzzaman, Md Mogno, Sharita Shehnaz |
| format |
Thesis |
| author |
Rahman, Fahim Ahmed, Md.Istiyak Saad, Saif Shahnewaz Ashrafuzzaman, Md Mogno, Sharita Shehnaz |
| author_sort |
Rahman, Fahim |
| title |
Prediction and detection in change of cognitive load for VIP’s by a machine learning approach |
| title_short |
Prediction and detection in change of cognitive load for VIP’s by a machine learning approach |
| title_full |
Prediction and detection in change of cognitive load for VIP’s by a machine learning approach |
| title_fullStr |
Prediction and detection in change of cognitive load for VIP’s by a machine learning approach |
| title_full_unstemmed |
Prediction and detection in change of cognitive load for VIP’s by a machine learning approach |
| title_sort |
prediction and detection in change of cognitive load for vip’s by a machine learning approach |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/14989 |
| work_keys_str_mv |
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