Browsing NTNU Open by Author "Moctezuma, Luis Alfredo"
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An Asynchronous Motor Imagery based Brain-Computer Interface for Two-dimensional Drone Control
Brynestad, Kjersti; Vatsvåg, Erlend (Master thesis, 2021)Dette arbeidet undersøker ulike state-of-the-art pre-prosesseringsmetoder, metoder for å ekstrahere egenskaper og klassifiseringsalgoritmer til bruk på Elektroencefalografi (EEG) data av forestilte bevegelser (MI). Datasettet ... -
An Asynchronous Motor Imagery-based Brain-Computer Interface for Two-dimensional Drone Control
Brynestad, Kjersti; Vatsvåg, Erlend (Master thesis, 2021)Dette arbeidet undersøker ulike state-of-the-art pre-prosesseringsmetoder, metoder for å ekstrahere egenskaper og klassifiseringsalgoritmer til bruk på Elektroencefalografi (EEG) data av forestilte bevegelser (MI). Datasettet ... -
An Exploration of Techniques for Electroencephalography-Based Motor Imagery Classification for Real-Time Drone Control
Lønvik, Helene Tørlen; Jonassen, Pauline Mørch (Master thesis, 2023)Denne masteroppgaven undersøker ulike state-of-the-art teknikker for preprosessering, egenskapsekstraksjon og klassifiserings av Electroencephalography (EEG) Motorisk Innbilning (MI) data. Målet for oppgaven var å utvikle ... -
An Exploration of Techniques for Electroencephalography-Based Motor Imagery Classification for Real-Time Drone Control
Jonassen, Pauline Mørch; Lønvik, Helene Tørlen (Master thesis, 2023)Denne masteroppgaven undersøker ulike state-of-the-art teknikker for preprosessering, egenskapsekstraksjon og klassifiserings av Electroencephalography (EEG) Motorisk Innbilning (MI) data. Målet for oppgaven var å utvikle ... -
Assessing the impact of idle state type on the identification of RGB color exposure for BCI
Torres-Garcia, Alejandro Antonio; Moctezuma, Luis Alfredo; Molinas Cabrera, Maria Marta (Chapter, 2020) -
Automated Detection and Removal of EEG Artifacts for an RGB Stimulation-Based Brain-Computer Interface
Dokken, Mari Hestetun; Løkken, Sandra Garder (Master thesis, 2023)Denne oppgaven undersøker metoder for å fjerne okulære artefakter (OA) fra registrerte EEG-signaler og evaluerer deres effektivitet og gjennomførbarhet for bruk i et fremtidig hjerne-datamaskin grensesnitt (BCI). Motivasjonen ... -
Automated Detection and Removal of EEG Artifacts for an RGB Stimulation-Based Brain-Computer Interface
Dokken, Mari Hestetun; Løkken, Sandra Garder (Master thesis, 2023)Denne oppgaven undersøker metoder for å fjerne okulære artefakter (OA) fra registrerte EEG-signaler og evaluerer deres effektivitet og gjennomførbarhet for bruk i et fremtidig hjerne-datamaskin grensesnitt (BCI). Motivasjonen ... -
Automated methodology for optimal selection of minimum electrode subsets for accurate EEG source estimation based on Genetic Algorithm optimization
Soler, Andres; Moctezuma, Luis Alfredo; Giraldo, Eduardo; Molinas, Marta (Peer reviewed; Journal article, 2022)High-density Electroencephalography (HD-EEG) has proven to be the EEG montage that estimates the neural activity inside the brain with highest accuracy. Multiple studies have reported the effect of electrode number on ... -
Classification of low-density EEG-based epileptic seizures by energy and fractal features based on EMD
Moctezuma, Luis Alfredo; Molinas Cabrera, Maria Marta (Journal article, 2019)We are here to present a new method for the classification of epileptic seizures from electroencephalogram (EEG) signals. It consists of applying empirical mode decomposition (EMD) to extract the most relevant intrinsic ... -
Data augmentation using GANs in EEG-based biometric systems
Molvik, Nikolai (Master thesis, 2022)Hjernebølger, målt med electroencephalography(EEG) har mange applikasjoner. Historisk sett har EEG hovedsaklig blitt brukt i medisin, men ved bruk av state of the art teknologi, kan EEG brukes i biometriske systemer, noe ... -
EEG Channel-Selection Method for Epileptic-Seizure Classification Based on Multi-Objective Optimization
Moctezuma, Luis Alfredo; Molinas Cabrera, Maria Marta (Peer reviewed; Journal article, 2020)We present a multi-objective optimization method for electroencephalographic (EEG) channel selection based on the non-dominated sorting genetic algorithm (NSGA) for epileptic-seizure classification. We tested the method ... -
EEG-based Subjects Identification based on Biometrics of Imagined Speech using EMD
Moctezuma, Luis Alfredo; Molinas Cabrera, Maria Marta (Chapter, 2018)When brain activity ions, the potential for human capacities augmentation is promising. In this paper, EMD is used to decompose EEG signals during Imagined Speech in order to use it as a biometric marker for creating a ... -
Enhancing Sleep-Wake Detection Using Deep Learning and Optimal Channel Selection from High-Density EEG
Herleiksplass, Karoline Seljevoll (Master thesis, 2023)Denne masteroppgaven utforsker den utfordrende men viktige oppgaven med å klassifisere våkenhet fra søvn ved bruk av elektroencefalografi (EEG) data, som er viktig i både kliniske og hjemmemiljøer. Ettersom søvnforstyrrelser ... -
Event-related potential from EEG for a two-step Identity Authentication System
Moctezuma, Luis Alfredo; Molinas Cabrera, Maria Marta (Peer reviewed; Journal article, 2019)Current problems related to high-level security access are increasing, leaving organizations and persons unsafe. A recent good candidate to create a robust identity authentication system is based on brain signals recorded ... -
Motor Imagery-based Brain-Computer Interfaces: Exploring Optimization and Transfer Learning Techniques for Multiclass Classification
Kenworthy, Victoria Taklo; Nylænder, Karoline Malene (Master thesis, 2023)Dette arbeidet fokuserer på å håndtere noen utfordinger innen hjerne-datamaskin-grensesnitt (BCI) basert på forestilte bevegelser (MI), kalt MI-BCI, inkludert forbedring av deteksjon og klassifisering av MI, identifisering ... -
Motor Imagery-based Brain-Computer Interfaces: Exploring Optimization and Transfer Learning Techniques for Multiclass Classification
Nylænder, Karoline Malene; Kenworthy, Victoria Taklo (Master thesis, 2023)Dette arbeidet fokuserer på å håndtere noen utfordinger innen hjerne-datamaskin-grensesnitt (BCI) basert på forestilte bevegelser (MI), kalt MI-BCI, inkludert forbedring av deteksjon og klassifisering av MI, identifisering ... -
Multi-objective optimization for EEG channel selection and accurate intruder detection in an EEG-based subject identification system
Moctezuma, Luis Alfredo; Molinas Cabrera, Maria Marta (Peer reviewed; Journal article, 2020)We present a four-objective optimization method for optimal electroencephalographic (EEG) channel selection to provide access to subjects with permission in a system by detecting intruders and identifying the subject. Each ... -
Sex differences observed in a study of EEG of linguistic activity and resting-state: Exploring optimal EEG channel configurations
Moctezuma, Luis Alfredo; Molinas Cabrera, Maria Marta (Chapter; Peer reviewed, 2019)This study reports the differences observed in theEEG signals of linguistic activity and resting-state between maleand female subjects in a population of 16 individuals (8 femalesand 8 males). These differences ... -
Subject Identification from Low-Density EEG-Recordings of Resting-States: A Study of Feature Extraction and Classification
Moctezuma, Luis Alfredo; Molinas Cabrera, Maria Marta (Journal article; Peer reviewed, 2019)A new concept of low-density electroencephalograms-based (EEG) Subject identification is proposed in this paper. To that aim, EEG recordings of resting-states were analyzed with 3 different classifiers (SVM, k-NN, and naive ... -
Subject Identification using EEG Signals and Supervised Learning
Premkumar, Shobiha (Master thesis, 2020)Denne masteroppgaven undersøker bruken av elektriske hjernesignaler fanget i elektroencefalografi (EEG) som parameter for et biometrisk system. De fangede hjernesignalene brukes til å lage et person-identifikasjonssystem ...