diplomsko delo
Sašo Pavlič (Author), Sašo Karakatič (Mentor), Boris Bizjak (Co-mentor)

Abstract

Diplomska naloga zajema spoznavanje in predstavitev z osnovami EEG-možganskih valov s pomočjo naprave Emotiv Insight. Zajeti EEG-podatki predstavljajo vhodne podatke v modelu strojnega učenja, s pomočjo katerega se je ugotavljalo, kdaj in kje se pojavljajo iskani vzorci. Eksperiment razvite metode zajema podatkov in uporabe modela se je izvedel tako, da se je testni subjekt izpostavil izmenjujočim izbranim slikam, ob tem pa so se z napravo Emotiv Insight zajeli EEG-možganski valovi. Zajeti EEG-podatki so služili kot zbirka podatkov, iz katere se je učil klasifikacijski model umetne nevronske mreže, ki uspešno razpoznava, kdaj je testni subjekt podvržen eni vrsti slik in kdaj drugi.

Keywords

EEG;možganski valovi;strojno učenje;BCI-naprava;snemanje podatkov;diplomske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UM FERI - Faculty of Electrical Engineering and Computer Science
Publisher: [S. Pavlič]
UDC: 004.383.3:612.82(043.2)
COBISS: 22842134 Link will open in a new window
Views: 666
Downloads: 85
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Other data

Secondary language: English
Secondary title: Reading and processing eeg signals - the machine learning approach
Secondary abstract: The diploma thesis covers cognition and presentation, with the basics of EEG brain waves using the Emotiv Insight device. The captured EEG data represents the input data into a machine learning model, which was used to determine when and where the required patterns appear. The experiment of the developed method of capturing data and model usage was carried out by exposing the test subject to the alternating selected images and capturing the EEG brain waves with the Emotiv Insight device. The captured EEG data served as a database from which the artificial neural network classification model learnt to successfully recognize when a test subject was exposed to one type of image and when to another.
Secondary keywords: EEG;brainwaves;machine learning;BCI device;recording data;
Type (COBISS): Bachelor thesis/paper
Thesis comment: Univ. v Mariboru, Fak. za elektrotehniko, računalništvo in informatiko, Informatika in tehnologije komuniciranja
Pages: IV, 52 f.
ID: 11208457