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undergraduate thesis
Regresijska analiza EEG signala
Split: University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, 2022. urn:nbn:hr:179:789438
Jovanović, Luka
University of Split
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture
Department of Electronics and Computing

Institutional repository: Repository of the Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture

Cite this document

Jovanović, L. (2022). Regresijska analiza EEG signala (Undergraduate thesis). Split: University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:179:789438

Jovanović, Luka. "Regresijska analiza EEG signala." Undergraduate thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, 2022. https://urn.nsk.hr/urn:nbn:hr:179:789438

Jovanović, Luka. "Regresijska analiza EEG signala." Undergraduate thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, 2022. https://urn.nsk.hr/urn:nbn:hr:179:789438

Jovanović, L. (2022). 'Regresijska analiza EEG signala', Undergraduate thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, accessed 07 August 2024, https://urn.nsk.hr/urn:nbn:hr:179:789438

Jovanović L. Regresijska analiza EEG signala [Undergraduate thesis]. Split: University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture; 2022 [cited 2024 August 07] Available at: https://urn.nsk.hr/urn:nbn:hr:179:789438

L. Jovanović, "Regresijska analiza EEG signala", Undergraduate thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:179:789438

Metadata
TitleRegresijska analiza EEG signala
AuthorLuka Jovanović
MentorDinko Begušić (mentor)
GranterUniversity of Split
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture
(Department of Electronics and Computing)
Split
Defense date and country2022-09-14, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovaj završni rad opisuje EEG postupak kao medicinsku metodu za dijagnosticiranje neuroloških bolesti. Također, prikazana je primjena regresijske analize na EEG postupak u prediktivne svrhe. U nekoliko poglavlja obrađene su i opisane teme poput elektroencefalografije, matematičkog modela regresijske analize podataka, navedeni su i opisani tipovi regresijskog modela za obradu EEG signala te su istaknuti prikladni alati za analizu i obradu signala. Opisani su alati poput Pythona, MATLABA i R programskih jezika. Opisani su paketi koji se koriste u tim alatima koji olakšavaju obradu i analizu signala. U poglavlju implementacije opisani su formati kojim se prenose podaci dobiveni postupkom EEG. Također je naveden primjer korištenja regresijskog modela podataka. Kao rezultat implementacije regresijske metode nad EEG signalom izvučen je zaključak popraćen matricom zabune. Objašnjen je razlog primjene regresijskog modela na ovaj primjer i način na koji se tumače rezultati dobiveni u primjeru. Prikazuje se regresijska metoda kao moćno statističko sredstvo na području strojnog učenja.
Abstract (english)
This thesis describes the EEG procedure as a medical method for diagnosing neurological diseases. Also, the application of regression analysis to the EEG procedure for predictive purposes is presented. In several chapters, topics such as electroencephalography, the mathematical model of regression analysis of data are discussed and described, the types of regression models for EEG signal processing are listed and described, suitable tools for signal analysis and processing are highlighted. Tools such as Python, MATLAB and R programming languages are described. Packages used in these tools that facilitate signal processing and analysis are described. The implementation chapter describes the formats used to transmit data obtained by the EEG procedure. An example of using regression model data is also provided. As a result of the implementation of the regression method over the EEG signal, a conclusion accompanied by a confusion matrix was drawn. The reason for applying the regression model to this example and how to interpret the results obtained in the example are explained. The regression method is presented as a powerful statistical tool in the field of Machine Learning.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:179:789438
Study programmeTitle: Computing
Study programme type: professional
Study level: undergraduate
Academic / professional title: stručni/a prvostupnik/ prvostupnica inženjer/inženjerka računarstva (stručni/a prvostupnik/ prvostupnica inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to authenticated users
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Created on2023-05-18 10:03:27