Advanced bioelectrical signal processing methods: Past, present and future approach-Part II: Brain signals

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10248180" target="_blank" >RIV/61989100:27240/21:10248180 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/1424-8220/21/19/6343" target="_blank" >https://www.mdpi.com/1424-8220/21/19/6343</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s21196343" target="_blank" >10.3390/s21196343</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Advanced bioelectrical signal processing methods: Past, present and future approach-Part II: Brain signals

Popis výsledku v původním jazyce

As it was mentioned in the previous part of this work (Part I)-the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work-various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform. (C) 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Název v anglickém jazyce

Advanced bioelectrical signal processing methods: Past, present and future approach-Part II: Brain signals

Popis výsledku anglicky

As it was mentioned in the previous part of this work (Part I)-the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work-various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform. (C) 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Centrum výzkumu pokročilých mechatronických systémů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Sensors

ISSN

1424-8220

e-ISSN

—

Svazek periodika

21

Číslo periodika v rámci svazku

19

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

35

Strana od-do

—

Kód UT WoS článku

000729178200001

EID výsledku v databázi Scopus

2-s2.0-85115407738