Essays about: "eeg feature extraction"

Showing result 1 - 5 of 16 essays containing the words eeg feature extraction.

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

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Yuan Wo; [2023]
   Keywords : 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.;

   Abstract : 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. READ MORE

3. 2. EEG-Based Speech Decoding Using a Machine Learning Pipeline

   University essay from KTH/Skolan för kemi, bioteknologi och hälsa (CBH)

   Author : Julia Önerud; [2023]
   Keywords : EEG; EEG-Decoding; Speech-like; Neuro; Neurotech; Machine-learning; SVM; Feature Extraction; Feature Selection; Mentalab; Inner Speech; OVR; Baseline-Correction; State-of-the-art; State-of-the-art Accuracy; Directions; Emotions;

   Abstract : his project aims to find a method that will help fill the information gaps in electroencephalography (EEG) brain-computer interfaces (BCI) research, by creating a pipeline method that allows for quicker research iterations than current state-of-the-art methods. The pipeline method is a multi-step processstarting from the recording EEG data from a subject performing a thought paradigm action, continuing with processing and decoding of the data, and ending with visualization and analysis the decoded results. READ MORE

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

   University essay from Stockholms universitet/Institutionen för data- och systemvetenskap

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

   Abstract : 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. READ MORE

5. 4. Tensor Decompositions of EEG Signals for Transfer Learning Applications

   University essay from Lunds universitet/Institutionen för reglerteknik

   Author : Emma Fallenius; Linda Karlsson; [2022]
   Keywords : Technology and Engineering;

   Abstract : In this report, tensor decomposition methods of EEG signals have been evaluated for the purpose of transfer learning. The aim has been to address the person-to-person Brain-Computer Interface (BCI) calibration problem by transferring training data between sessions, which can shorten calibration times, extend the amount of training data, and enable using data from simulated environments in real world applications. READ MORE

6. 5. Analysis of Brain Signals from Patients with Parkinson’s Disease using Self-Supervised Learning

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Emma Lind; [2022]
   Keywords : Machine Learning; Self-supervised learning; Feature extraction; Parkinson’s Disease; Magnetoencephalography; Electroencephalogram; Maskininlärning; Självlärande inlärning; Särdragsextraktion; Parkinsons sjukdom; Magnetoencefalografi; Elektroencefalografi;

   Abstract : Parkinson’s disease (PD) is one of the most common neurodegenerative brain disorders, commonly diagnosed and monitored via clinical examinations, which can be imprecise and lead to a delayed or inaccurate diagnosis. Therefore, recent research has focused on finding biomarkers by analyzing brain networks’ neural activity to find abnormalities associated with PD pathology. READ MORE