Automatic detection of motor imagery movement for neuro-based home appliances system / Mohd Hilmi Firdaus Zakaria
This project of Automatic Detection of Motor Imagery Movements for Neuro Based Home Appliances System aims to design a protocol of recording EEG signals for controlling electronic devices using brain activities and to detect motor imagery movement from EEG signals automatically. For the motor imager...
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2010
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Online Access: | https://ir.uitm.edu.my/id/eprint/80290/1/80290.pdf |
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my-uitm-ir.802902023-09-20T02:36:43Z Automatic detection of motor imagery movement for neuro-based home appliances system / Mohd Hilmi Firdaus Zakaria 2010 Zakaria, Mohd Hilmi Firdaus This project of Automatic Detection of Motor Imagery Movements for Neuro Based Home Appliances System aims to design a protocol of recording EEG signals for controlling electronic devices using brain activities and to detect motor imagery movement from EEG signals automatically. For the motor imagery movement detection, 2 different protocol was design for both real and imagery grasping hand movement. EEG signals will be recorded by placing the electrodes at C3 and C4 using the 10 - 20 international system. The raw EEG data from the Open BCI will be extracted and it will be filtered and transform to Fast Fourier transform for further analysis in time-domain and spectrogram. From the analysis, the threshold was set at both real-time data and spectrogram for automatic detection of motor imagery movement that can be apply on neuro-based home appliances system. 2010 Thesis https://ir.uitm.edu.my/id/eprint/80290/ https://ir.uitm.edu.my/id/eprint/80290/1/80290.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Mansor, Wahidah |
institution |
Universiti Teknologi MARA |
collection |
UiTM Institutional Repository |
language |
English |
advisor |
Mansor, Wahidah |
description |
This project of Automatic Detection of Motor Imagery Movements for Neuro Based Home Appliances System aims to design a protocol of recording EEG signals for controlling electronic devices using brain activities and to detect motor imagery movement from EEG signals automatically. For the motor imagery movement detection, 2 different protocol was design for both real and imagery grasping hand movement. EEG signals will be recorded by placing the electrodes at C3 and C4 using the 10 - 20 international system. The raw EEG data from the Open BCI will be extracted and it will be filtered and transform to Fast Fourier transform for further analysis in time-domain and spectrogram. From the analysis, the threshold was set at both real-time data and spectrogram for automatic detection of motor imagery movement that can be apply on neuro-based home appliances system. |
format |
Thesis |
qualification_level |
Bachelor degree |
author |
Zakaria, Mohd Hilmi Firdaus |
spellingShingle |
Zakaria, Mohd Hilmi Firdaus Automatic detection of motor imagery movement for neuro-based home appliances system / Mohd Hilmi Firdaus Zakaria |
author_facet |
Zakaria, Mohd Hilmi Firdaus |
author_sort |
Zakaria, Mohd Hilmi Firdaus |
title |
Automatic detection of motor imagery movement for neuro-based home appliances system / Mohd Hilmi Firdaus Zakaria |
title_short |
Automatic detection of motor imagery movement for neuro-based home appliances system / Mohd Hilmi Firdaus Zakaria |
title_full |
Automatic detection of motor imagery movement for neuro-based home appliances system / Mohd Hilmi Firdaus Zakaria |
title_fullStr |
Automatic detection of motor imagery movement for neuro-based home appliances system / Mohd Hilmi Firdaus Zakaria |
title_full_unstemmed |
Automatic detection of motor imagery movement for neuro-based home appliances system / Mohd Hilmi Firdaus Zakaria |
title_sort |
automatic detection of motor imagery movement for neuro-based home appliances system / mohd hilmi firdaus zakaria |
granting_institution |
Universiti Teknologi MARA (UiTM) |
granting_department |
Faculty of Electrical Engineering |
publishDate |
2010 |
url |
https://ir.uitm.edu.my/id/eprint/80290/1/80290.pdf |
_version_ |
1783736294806913024 |