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master's thesis
Izdvajanje značajki i klasifikacija signala EEG-a u sustavima sučelja mozga i računala
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. urn:nbn:hr:168:345269
Franjić, Ivan
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Franjić, I. (2018). Izdvajanje značajki i klasifikacija signala EEG-a u sustavima sučelja mozga i računala (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:345269

Franjić, Ivan. "Izdvajanje značajki i klasifikacija signala EEG-a u sustavima sučelja mozga i računala." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:345269

Franjić, Ivan. "Izdvajanje značajki i klasifikacija signala EEG-a u sustavima sučelja mozga i računala." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:345269

Franjić, I. (2018). 'Izdvajanje značajki i klasifikacija signala EEG-a u sustavima sučelja mozga i računala', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 03 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:345269

Franjić I. Izdvajanje značajki i klasifikacija signala EEG-a u sustavima sučelja mozga i računala [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2018 [cited 2024 November 03] Available at: https://urn.nsk.hr/urn:nbn:hr:168:345269

I. Franjić, "Izdvajanje značajki i klasifikacija signala EEG-a u sustavima sučelja mozga i računala", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:168:345269

Metadata
TitleIzdvajanje značajki i klasifikacija signala EEG-a u sustavima sučelja mozga i računala
Title (english)Feature Extraction and Classification of EEG Signals in Brain Computer Interfaces
AuthorIvan Franjić
MentorMario Cifrek (mentor)
Committee memberMario Cifrek (predsjednik povjerenstva)
Committee memberAna Sušac (član povjerenstva)
Committee memberŽeljka Lučev Vasić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2018-07-06, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Abstract
Sučelje mozga i računala (engl. Brain Computer Interface, BCI) osigurava komunikacijski kanal između čovjeka i računala. Komunikacija se temelji na prikupljanju i analizi signala generiranih u mozgu. BCI sustav zahtijeva od korisnika sposobnost generiranja određenih moždanih obrazaca koje potom može detektirati, dekodirati i pomoću njih upravljati cjelokupnim sustavom. Prema tome, BCI sustav je potpuno neovisan o bilo kakvoj vrsti pokreta korisnika. U ovom su radu analizirani signali EEG-a koji predstavljaju zamišljanje pokreta lijeve odnosno desne ruke te stanje kada ispitanik miruje. Korišteni su javno dostupni signali EEG-a, a to su BCI Competition 2008 Dataset IIIa. Analiza uključuje izdvajanje značajki temeljenih na algebri kvaternionima te klasifikaciju pomoću algoritama temeljenih na stablima odluke. Algoritmi koji su se koristili za klasifikaciju su stablo odluke, algoritam slučajnih šuma, AdaBoost algoritam te ExtraTrees algoritam. Iako primjena kvaterniona pruža elegantan način prikaza signala i efikasan način računanja, točnost koja je postignuta ovim algoritmom, koristeći ovaj skup podataka, je relativno niska te nedovoljna za primjene u stvarnom vremenu.
Abstract (english)
Brain computer interface (BCI) provides communication channel between human and computer. Communication is based on acquisition and analysis of signals generated in brain. BCI system requires from user the ability of generating certain brain patterns which can be detected, decoded and used for managing the entire system. Thus, BCI system is completely independent of any users movement. In this paper EEG signals which represent hand motor imagery and state when subject rests were analyzed. Publicly available signal base was used and it was BCI Competition 2008 Dataset IIIa. Analysis includes feature extraction based on quaternion algebra and classification using algorithms based on decision trees. Classification algorithms that were employed are decision tree, random forest algorithm, AdaBoost algorithm and ExtraTrees algorithm. Despite of fact that use of quaternion algebra provides elegant way of representing signals and computationally is efficient, accuracy that is obtained using this dataset is relatively low and is not sufficient for real-time applications.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:345269
Study programmeTitle: Electrical Engineering and Information Technology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka elektrotehnike i informacijske tehnologije (magistar/magistra inženjer/inženjerka elektrotehnike i informacijske tehnologije)
Type of resourceText
File originBorn digital
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Created on2019-03-13 20:17:11