Sökning: "EEG signal processing"

Visar resultat 1 - 5 av 21 uppsatser innehållade orden EEG signal processing.

  1. 1. Deep Learning-Driven EEG Classification in Human-Robot Collaboration

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Yuan Wo; [2023]
    Nyckelord :Human-robot collaboration; Electroencephalogram signal; Signal Processing Feature Extraction; Deep Learning method; Dilated Convolutional Neural Network; Människa-robot-samarbete; Elektroencefalogram-signal; Signalförädlingsfunktionsutvinning; Djupinlärningsmetod; Dilaterat konvolutionellt neuronnätverk.;

    Sammanfattning : Human-robot collaboration (HRC) occurs when people and robots work together in a shared environment. Current robots often use rigid programs unsuitable for HRC. Multimodal robot programming offers an easier way to control robots using inputs like voice and gestures. LÄS MER

  3. 2. Neurotactile Integration

    Kandidat-uppsats, Lunds universitet/Avdelningen för Biomedicinsk teknik

    Författare :Kalle Svensson; Julius Cewers; [2023]
    Nyckelord :Technology and Engineering;

    Sammanfattning : The field of neural representation of sensory integration is an advanced topic with complex processes. The mechanisms of the brain are far from fully understood and are in need of further development to be implemented in clinical usages such as neuroprosthetics. LÄS MER

  4. 3. Motor Imagery Signal Classification using Adversarial Learning - A Systematic Literature Review

    Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

    Författare :Osama Mahmudi; Shubhra Mishra; [2023]
    Nyckelord :Adversarial Learning; Motor Imagery; BCI; EEG; Machine Learning;

    Sammanfattning : Context: Motor Imagery (MI) signal classification is a crucial task for developing Brain-Computer Interfaces (BCIs) that allow people to control devices using their thoughts. However, traditional machine learning approaches often suffer from limited performance due to inter-subject variability and limited data availability. LÄS MER

  5. 4. Evaluation of non-stationary signal processing methods for binary EEG classification

    Master-uppsats, Lunds universitet/Matematisk statistik

    Författare :Amanda Ledell; [2022]
    Nyckelord :Mathematics and Statistics;

    Sammanfattning : Electroencephalogram (EEG) measurements are notoriously noisy and non-stationary and there are several specialized techniques for their analysis and interpretation. In this thesis, we implement a collection of stationary and non-stationary methods including coherence, Phase Locking Value (PLV), Phase Lag Index (PLI), and their imaginary counterparts. LÄS MER

  7. 5. Statistics and Machine Learning for Classification of Emotional and Semantic Content of EEG

    Master-uppsats, Lunds universitet/Matematisk statistik

    Författare :Oskar Keding; David Ohlin; [2021]
    Nyckelord :Machine Learning; machine; learning; neural; network; statistics; EEG; emotion; semantic; emotional; signal; processing; reassignment; time-frequency; spectrogram; brain processing; bci; CNN; convolutional; Mathematics and Statistics;

    Sammanfattning : Interpreting EEG measurements is of great relevance, both for developing underlying neuroscientific theory and improving existing applications. In this study, two networks with different approaches to time-frequency analysis and feature selection are compared on simulated and real data for semantic and emotional perception. LÄS MER