Classification Of EEG Imagery Motor Function Using 3D Convolutional Neural Network

A brain-computer interface (BCI) is a computer-based system that acquires brain signals, analyzes them, and translates them into commands. One of the main uses for BCI is motor imagery, which has countless potential ranging from control over prosthetic limbs to cybertronics. This project consists...

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Main Author: Kanesan, Thivagar
Format: Final Year Project
Language: English
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-utpedia.23050 /
Published: Universiti Teknologi PETRONAS 2020
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access: http://utpedia.utp.edu.my/23050/1/FYP%20DISSERTATION%20Thivagar_23508%20-signed.pdf
http://utpedia.utp.edu.my/23050/
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spelling utp-utpedia.230502022-03-11T04:28:04Z http://utpedia.utp.edu.my/23050/ Classification Of EEG Imagery Motor Function Using 3D Convolutional Neural Network Kanesan, Thivagar TK Electrical engineering. Electronics Nuclear engineering A brain-computer interface (BCI) is a computer-based system that acquires brain signals, analyzes them, and translates them into commands. One of the main uses for BCI is motor imagery, which has countless potential ranging from control over prosthetic limbs to cybertronics. This project consists of research done towards braincomputer interfacing mainly using the outputs generated from EEG signals of left/right imagined arm movements. This EEG output signal will then be classified using deep learning technique known as 3D convolutional neural network to create a classification algorithm. 3D ConvNet is well-suited for spatiotemporal feature learning, where convolution and pooling operations are performed spatio-temporally. Compared to 2D ConvNet, 3D ConvNet has the ability to model temporal information better owing to 3D convolution and 3D pooling operations. Universiti Teknologi PETRONAS 2020-09 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/23050/1/FYP%20DISSERTATION%20Thivagar_23508%20-signed.pdf Kanesan, Thivagar (2020) Classification Of EEG Imagery Motor Function Using 3D Convolutional Neural Network. Universiti Teknologi PETRONAS. (Submitted)
institution Universiti Teknologi Petronas
collection UTPedia
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Kanesan, Thivagar
Classification Of EEG Imagery Motor Function Using 3D Convolutional Neural Network
description A brain-computer interface (BCI) is a computer-based system that acquires brain signals, analyzes them, and translates them into commands. One of the main uses for BCI is motor imagery, which has countless potential ranging from control over prosthetic limbs to cybertronics. This project consists of research done towards braincomputer interfacing mainly using the outputs generated from EEG signals of left/right imagined arm movements. This EEG output signal will then be classified using deep learning technique known as 3D convolutional neural network to create a classification algorithm. 3D ConvNet is well-suited for spatiotemporal feature learning, where convolution and pooling operations are performed spatio-temporally. Compared to 2D ConvNet, 3D ConvNet has the ability to model temporal information better owing to 3D convolution and 3D pooling operations.
format Final Year Project
author Kanesan, Thivagar
author_sort Kanesan, Thivagar
title Classification Of EEG Imagery Motor Function Using 3D Convolutional Neural Network
title_short Classification Of EEG Imagery Motor Function Using 3D Convolutional Neural Network
title_full Classification Of EEG Imagery Motor Function Using 3D Convolutional Neural Network
title_fullStr Classification Of EEG Imagery Motor Function Using 3D Convolutional Neural Network
title_full_unstemmed Classification Of EEG Imagery Motor Function Using 3D Convolutional Neural Network
title_sort classification of eeg imagery motor function using 3d convolutional neural network
publisher Universiti Teknologi PETRONAS
publishDate 2020
url http://utpedia.utp.edu.my/23050/1/FYP%20DISSERTATION%20Thivagar_23508%20-signed.pdf
http://utpedia.utp.edu.my/23050/
_version_ 1741195900286402560
score 11.62408

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