Search results for "signaalinkäsittely"

Methods for space-time parameter estimation in DS-CDMA arrays

2006

Rui Wu tutki väitöstyössään radiokanavan eri ominaisuuksien arviointia. Tietämys radiokanavan ominaisuuksista on monesti välttämätöntä, jotta radiovastaanottimen vastaanottama signaali voidaan purkaa ymmärrettävään muotoon. Wu tutki hajaspektrijärjestelmiä, joissa esimerkiksi puhe- tai datasignaalin kaistanleveys moninkertaistetaan radiolähettimessä. Näin tehdään muun muassa jo toiminnassa olevissa 3G-puhelimissa ja -tukiasemissa. Kaistanleveyden moninkertaistamisella pyritään satunnaistamaan muille järjestelmän käyttäjille aiheutetut häiriösignaalit, koska kyseisissä järjestelmissä signaalit etenevät samalla taajuuskaistalla yhtäaikaisesti. Se, miten kunkin järjestelmän käyttäjän signaali …

Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition

2021

AbstractPrevious researches demonstrate that major depression disorder (MDD) is associated with widespread network dysconnectivity, and the dynamics of functional connectivity networks are important to delineate the neural mechanisms of MDD. Cortical electroencephalography (EEG) oscillations act as coordinators to connect different brain regions, and various assemblies of oscillations can form different networks to support different cognitive tasks. Studies have demonstrated that the dysconnectivity of EEG oscillatory networks is related with MDD. In this study, we investigated the oscillatory hyperconnectivity and hypoconnectivity networks in MDD under a naturalistic and continuous stimuli…

Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization

2021

Ongoing electroencephalography (EEG) signals are recorded as a mixture of stimulus-elicited EEG, spontaneous EEG and noises, which poses a huge challenge to current data analyzing techniques, especially when different groups of participants are expected to have common or highly correlated brain activities and some individual dynamics. In this study, we proposed a data-driven shared and unshared feature extraction framework based on nonnegative and coupled tensor factorization, which aims to conduct group-level analysis for the EEG signals from major depression disorder (MDD) patients and healthy controls (HC) when freely listening to music. Constrained tensor factorization not only preserve…

Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression

2021

To examine the electrophysiological underpinnings of the functional networks involved in music listening, previous approaches based on spatial independent component analysis (ICA) have recently been used to ongoing electroencephalography (EEG) and magnetoencephalography (MEG). However, those studies focused on healthy subjects, and failed to examine the group-level comparisons during music listening. Here, we combined group-level spatial Fourier ICA with acoustic feature extraction, to enable group comparisons in frequency-specific brain networks of musical feature processing. It was then applied to healthy subjects and subjects with major depressive disorder (MDD). The music-induced oscil…

Sokean signaalinkäsittelyn menetelmiä : sovelluksena EEG-aineiston analysointi

2011

Determination of the Time Window of Event-Related Potential Using Multiple-Set Consensus Clustering

2020

Clustering is a promising tool for grouping the sequence of similar time-points aimed to identify the attention blocks in spatiotemporal event-related potentials (ERPs) analysis. It is most likely to elicit the appropriate time window for ERP of interest if a suitable clustering method is applied to spatiotemporal ERP. However, how to reliably estimate a proper time window from entire individual subjects’ data is still challenging. In this study, we developed a novel multiset consensus clustering method in which several clustering results of multiple subjects were combined to retrieve the best fitted clustering for all the subjects within a group. Then, the obtained clustering was processed…

Consistency of Independent Component Analysis for FMRI

2021

Background Independent component analysis (ICA) has been widely used for blind source separation in the field of medical imaging. However, despite of previous substantial efforts, the stability of ICA components remains a critical issue which has not been adequately addressed, despite numerous previous efforts. Most critical is the inconsistency of some of the extracted components when ICA is run with different model orders (MOs). New Method In this study, a novel method of determining the consistency of component analysis (CoCA) is proposed to evaluate the consistency of extracted components with different model orders. In the method, “consistent components” (CCs) are defined as those whic…

Synthesis and application of orthogonal signal bases possessing enhanced timefrequency localization for mobile wireless networks

2012

Unsupervised representation learning of spontaneous MEG data with nonlinear ICA

2023

Funding Information: We wish to thank the reviewers and editors for the useful comments to improve the paper a lot. We thank Dr. Hiroshi Morioka for the useful discussion at the beginning of the project. L.P. was funded in part by the European Research Council (No. 678578 ). A.H. was supported by a Fellowship from CIFAR, and the Academy of Finland. The authors acknowledge the computational resources provided by the Aalto Science-IT project, and also wish to thank the Finnish Grid and Cloud Infrastructure (FGCI) for supporting this project with computational and data storage resources. | openaire: EC/H2020/678578/EU//HRMEG Resting-state magnetoencephalography (MEG) data show complex but stru…

Extracting multi-mode ERP features using fifth-order nonnegative tensor decomposition

2018

Background Preprocessed Event-related potential (ERP) data are usually organized in multi-way tensor, in which tensor decomposition serves as a powerful tool for data processing. Due to the limitation of computation burden for multi-way data and the low algorithm performance of stability and efficiency, multi-way ERP data are conventionally reorganized into low-order tensor or matrix before further analysis. However, the reorganization may hamper mode specification and spoil the interaction information among different modes. New method In this study, we applied a fifth-order tensor decomposition to a set of fifth-order ERP data collected by exerting proprioceptive stimulus on left and right…