Classi fication of motor imagery tasks based on BCI paradigm

Classi fication of motor imagery tasks based on BCI paradigm

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.

Bibliographic Details
Main Authors: Hossain, Nahid, Hasan, Bhuiyan Itmam, Mohona, Mahfuza Humayra, Noshin, Kantat Rehnuma
Other Authors: Parvez, Mohammad Zavid
Format: Thesis
Language:English
Published: Brac University 2019
Subjects:
EEG
EMD
IMF
CNN
BCI
Brain-computer interfaces
Human-computer interaction
Computational intelligence
Online Access:http://hdl.handle.net/10361/12783
id 10361-12783
record_format dspace
spelling 10361-127832022-01-26T10:20:05Z Classi fication of motor imagery tasks based on BCI paradigm Hossain, Nahid Hasan, Bhuiyan Itmam Mohona, Mahfuza Humayra Noshin, Kantat Rehnuma Parvez, Mohammad Zavid Department of Computer Science and Engineering, Brac University EEG EMD IMF CNN BCI Brain-computer interfaces Human-computer interaction Computational intelligence This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 27-31). Motor imagery tasks are mental processes by which individual practices a set of actions in their mind without actually performing the physical movements. Research in the motor imagery tasks allow us to acquire critical information on how the human brain works, which further enables us to integrate the knowledge with brain-computer interface (BCI) technologies to improve neurological rehabilitation along with, commercial uses such as communication, entertainment, etc. Electroencephalogram (EEG) is a commonly used process to observe and classify brain activities. However, EEG signal is non-stationary in nature, therefore, feature extraction based on EEG signals is quite hard. In our thesis, empirical mode decomposition (EMD) was used to break down the original signal into intrinsic mode functions (IMFs) in order of higher frequency to lower frequency. Convolution neural network (CNN) is then used on IMFs' feature vector and classify di erent motor imagery tasks. Our proposed model achieves around 78% accuracy, where the dataset was captured from nine participants. Bhuiyan Itmam Hasan Nahid Hossain Mahfuza Humayra Mohona Kantat Rehnuma Noshin B. Computer Science 2019-10-14T04:52:17Z 2019-10-14T04:52:17Z 2019 2019-09 Thesis ID 14201027 ID 14201035 ID 14301028 ID 15301066 http://hdl.handle.net/10361/12783 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 EEG
EMD
IMF
CNN
BCI
Brain-computer interfaces
Human-computer interaction
Computational intelligence
spellingShingle EEG
EMD
IMF
CNN
BCI
Brain-computer interfaces
Human-computer interaction
Computational intelligence
Hossain, Nahid
Hasan, Bhuiyan Itmam
Mohona, Mahfuza Humayra
Noshin, Kantat Rehnuma
Classi fication of motor imagery tasks based on BCI paradigm
description This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
author2 Parvez, Mohammad Zavid
author_facet Parvez, Mohammad Zavid
Hossain, Nahid
Hasan, Bhuiyan Itmam
Mohona, Mahfuza Humayra
Noshin, Kantat Rehnuma
format Thesis
author Hossain, Nahid
Hasan, Bhuiyan Itmam
Mohona, Mahfuza Humayra
Noshin, Kantat Rehnuma
author_sort Hossain, Nahid
title Classi fication of motor imagery tasks based on BCI paradigm
title_short Classi fication of motor imagery tasks based on BCI paradigm
title_full Classi fication of motor imagery tasks based on BCI paradigm
title_fullStr Classi fication of motor imagery tasks based on BCI paradigm
title_full_unstemmed Classi fication of motor imagery tasks based on BCI paradigm
title_sort classi fication of motor imagery tasks based on bci paradigm
publisher Brac University
publishDate 2019
url http://hdl.handle.net/10361/12783
work_keys_str_mv AT hossainnahid classificationofmotorimagerytasksbasedonbciparadigm
AT hasanbhuiyanitmam classificationofmotorimagerytasksbasedonbciparadigm
AT mohonamahfuzahumayra classificationofmotorimagerytasksbasedonbciparadigm
AT noshinkantatrehnuma classificationofmotorimagerytasksbasedonbciparadigm
_version_ 1814309194773299200

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