Predicting brain age from EEG signals using machine learning and neural network

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.

Detalhes bibliográficos
Principais autores:	Pratanu, Abul Mushfique Muslah, Farhad, Fuad Ibne Jashim, Afnan, Aysha, Mim, Nusrat Jahan, Rahman, Farhin
Outros Autores:	Chakrabarty, Amitabha
Formato:	Tese
Idioma:	English
Publicado em:	Brac University 2023
Assuntos:	EEG Brain age K-NN RF Decision tree MLP Naive bayes Raspberry Pi Machine learning Neural networks (Computer science)
Acesso em linha:	http://hdl.handle.net/10361/18092

id	10361-18092
record_format	dspace
spelling	10361-180922023-04-06T21:01:44Z Predicting brain age from EEG signals using machine learning and neural network Pratanu, Abul Mushfique Muslah Farhad, Fuad Ibne Jashim Afnan, Aysha Mim, Nusrat Jahan Rahman, Farhin Chakrabarty, Amitabha Hossain, Muhammad Iqbal Department of Computer Science and Engineering, Brac University EEG Brain age K-NN RF Decision tree MLP Naive bayes Raspberry Pi Machine learning 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 50-53). The objective of this study was to develop a technique for calculating the ages of people’s brains by analyzing EEG data signals and using machine learning algorithms on a Raspberry Pi. We employed many machine learning techniques, including random forest (RF), Decision Tree Classifier, K Nearest Neighbors Classifier (K-NN), Gaussian Naive Bayes, and Multi-layer Perceptron classifier(MLP). K-NN stands for K-nearest Neighbors, whereas RF stands for Random Forest. We initially implemented our machine learning algorithms on a desktop computer with many bells and whistles, where the dataset was also trained. By applying the Random Forest classifier (RF), we were able to attain 90% accuracy, the maximum feasible. The K-Nearest Neighbors classifier placed second with an accuracy of 87%. The accuracy obtained by the Decision Tree Classifier, the Naive Bayes algorithm, and the MLP algorithm, in order, was 83%, 39%, and 40%, respectively. Our major aim was the creation of an Internet of Things-based device, we tested our data on Raspberry Pi. If in the future, we were to construct, based on our model, a device that rapidly turned EEG brain signals into the participant’s brain age, we would be able to significantly improve the quality of our work. In addition, it will aid in the diagnosis of some brain illnesses at an early stage, which has been a struggle up until now. Abul Mushfique Muslah Pratanu Fuad Ibne Jashim Farhad Aysha Afnan Nusrat Jahan Mim Farhin Rahman B. Computer Science 2023-04-06T05:08:52Z 2023-04-06T05:08:52Z 2022 2022-05 Thesis ID 18201183 ID 18301229 ID 18301039 ID 18301003 ID 18301001 http://hdl.handle.net/10361/18092 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. 53 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	EEG Brain age K-NN RF Decision tree MLP Naive bayes Raspberry Pi Machine learning Neural networks (Computer science)
spellingShingle	EEG Brain age K-NN RF Decision tree MLP Naive bayes Raspberry Pi Machine learning Neural networks (Computer science) Pratanu, Abul Mushfique Muslah Farhad, Fuad Ibne Jashim Afnan, Aysha Mim, Nusrat Jahan Rahman, Farhin Predicting brain age from EEG signals using machine learning and neural network
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
author2	Chakrabarty, Amitabha
author_facet	Chakrabarty, Amitabha Pratanu, Abul Mushfique Muslah Farhad, Fuad Ibne Jashim Afnan, Aysha Mim, Nusrat Jahan Rahman, Farhin
format	Thesis
author	Pratanu, Abul Mushfique Muslah Farhad, Fuad Ibne Jashim Afnan, Aysha Mim, Nusrat Jahan Rahman, Farhin
author_sort	Pratanu, Abul Mushfique Muslah
title	Predicting brain age from EEG signals using machine learning and neural network
title_short	Predicting brain age from EEG signals using machine learning and neural network
title_full	Predicting brain age from EEG signals using machine learning and neural network
title_fullStr	Predicting brain age from EEG signals using machine learning and neural network
title_full_unstemmed	Predicting brain age from EEG signals using machine learning and neural network
title_sort	predicting brain age from eeg signals using machine learning and neural network
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/18092
work_keys_str_mv	AT pratanuabulmushfiquemuslah predictingbrainagefromeegsignalsusingmachinelearningandneuralnetwork AT farhadfuadibnejashim predictingbrainagefromeegsignalsusingmachinelearningandneuralnetwork AT afnanaysha predictingbrainagefromeegsignalsusingmachinelearningandneuralnetwork AT mimnusratjahan predictingbrainagefromeegsignalsusingmachinelearningandneuralnetwork AT rahmanfarhin predictingbrainagefromeegsignalsusingmachinelearningandneuralnetwork
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Predicting brain age from EEG signals using machine learning and neural network

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