Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Powiadomienia systemowe

Sesja wygasła!

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: intrinsic mode functions

Sortuj według:

Ogranicz wyniki do:

Detection of SSVEP based on empirical mode decomposition and power spectrum peaks analysis

Antelis Javier M., Rivera Camilo A., Galvis Eduard, Ruiz-Olaya Andres F.

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 3

1010--1021

Steady-state visual evoked potential (SSVEP) based brain–computer interfaces have been widely studied because these systems have potential to restore capabilities of communication and control of disable people. Identifying target frequency using SSVEP signals is still a great challenge due to the poor signal-to-noise ratio of these signals. Commonly, this task is carried out with detection algorithms such as bank of frequency-selective filters and canonical correlation analysis. This work proposes a novel method for the detection of SSVEP that combines the empirical mode decomposition (EMD) and a power spectral peak analysis (PSPA). The proposed EMD+PSPA method was evaluated with two EEG datasets, and was compared with the widely used FB and CCA. The first dataset is freely available and consists of three flickering light sources; the second dataset was constructed and consists of six flickering light sources. The results showed that proposed method was able to detect SSVEP with high accuracy (93.67 ± 9.97 and 78.19 ± 23.20 for the two datasets). Furthermore, the detection accuracy results achieved with the first dataset showed that EMD+PSPA provided the highest detection accuracy (DA) in the largest number of participants (three out of five), and that the average DA across all participant was 93.67 ± 9.97 which is 7% and 4% more than the average DA achieved with FB and CCA, respectively.

Denoising and detrending of measured oscillatory signal in power system

Yang D., Rehtanz C., Li Y., Liu Q., Görner K.

Przegląd Elektrotechniczny

2012

R. 88, nr 3b

135-139

This paper presents a novel method for denoising and detrending of oscillatory signal measured from wide area measurement system (WAMS) using empirical mode decomposition (EMD) and time-frequency analysis. First of all, the measured signal is decomposed into a set of intrinsic mode functions (IMFs) by EMD. Next, the IMFs are divided into three parts based on their time and frequency distributions. Then, the noise and higher frequency components, trend components and meaningful oscillation modes are identified respectively. The proposed method are validated by the actual measured signal from WAProtector and the estimated trend is confirmed by comparing with the sliding linear trend estimated method and other nonlinear trend estimated methods.

W artykule zaprezentowano nową metodę usuwania szumu z sygnału okresowego w systemach WAMS. W pierwszej kolejności przeprowadza się dekompozycję sygnału na funkcje, które następnie dzielone są na trzy części w zależności od rozkładu czasowoczęstotliwościowego.