Biosignal processing - 2

Biosignals are any signal from any living being that can be recorded and analyzed. There are many different forms of biosignals, namely electrical or mechanical. Examples of such signals include skin conductivity and muscle contraction signals. These signals have also been used in many different app...

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Main Author: Ling Adam Muhd Hasyim Ling
Other Authors: Deepu Rajan
Format: Final Year Project
Language:English
Published: 2017
Subjects:
Online Access:http://hdl.handle.net/10356/70248
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-702482023-03-03T20:59:20Z Biosignal processing - 2 Ling Adam Muhd Hasyim Ling Deepu Rajan School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering::Computer applications Biosignals are any signal from any living being that can be recorded and analyzed. There are many different forms of biosignals, namely electrical or mechanical. Examples of such signals include skin conductivity and muscle contraction signals. These signals have also been used in many different applications, such as in the medical setting where biosignals from the handicapped have been recorded and analyzed in order to provide suitable healthcare to them. Biosignals have also been recorded with the intention of foreseeing the emotions elicited by a subject. Methods such as statistical classification have been implemented in previous studies in which the prediction of emotions have been successfully achieved by a machine. This project aims to manipulate recorded biosignals from a previously obtained dataset, analyze and process these signals via feature extraction. After which, the extracted features will be supplied to a k-Nearest Neighbor classifier as a training set to establish a model which is able to predict the emotion that is associated with the different biosignals elicited by the subject in the data set. Bachelor of Engineering (Computer Engineering) 2017-04-18T01:10:45Z 2017-04-18T01:10:45Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/70248 en Nanyang Technological University 34 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering::Computer applications
spellingShingle DRNTU::Engineering::Computer science and engineering::Computer applications
Ling Adam Muhd Hasyim Ling
Biosignal processing - 2
description Biosignals are any signal from any living being that can be recorded and analyzed. There are many different forms of biosignals, namely electrical or mechanical. Examples of such signals include skin conductivity and muscle contraction signals. These signals have also been used in many different applications, such as in the medical setting where biosignals from the handicapped have been recorded and analyzed in order to provide suitable healthcare to them. Biosignals have also been recorded with the intention of foreseeing the emotions elicited by a subject. Methods such as statistical classification have been implemented in previous studies in which the prediction of emotions have been successfully achieved by a machine. This project aims to manipulate recorded biosignals from a previously obtained dataset, analyze and process these signals via feature extraction. After which, the extracted features will be supplied to a k-Nearest Neighbor classifier as a training set to establish a model which is able to predict the emotion that is associated with the different biosignals elicited by the subject in the data set.
author2 Deepu Rajan
author_facet Deepu Rajan
Ling Adam Muhd Hasyim Ling
format Final Year Project
author Ling Adam Muhd Hasyim Ling
author_sort Ling Adam Muhd Hasyim Ling
title Biosignal processing - 2
title_short Biosignal processing - 2
title_full Biosignal processing - 2
title_fullStr Biosignal processing - 2
title_full_unstemmed Biosignal processing - 2
title_sort biosignal processing - 2
publishDate 2017
url http://hdl.handle.net/10356/70248
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