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Wyszukujesz frazę "Rak, R. J." wg kryterium: Autor


Wyświetlanie 1-3 z 3
Tytuł:
Brain-computer interface as measurement and control system The review paper
Autorzy:
Rak, R. J.
Kołodziej, M.
Majkowski, A.
Powiązania:
https://bibliotekanauki.pl/articles/221747.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
EEG
brain-computer interface
feature extraction
feature selection
measurement and control
Opis:
In the last decade of the XX-th century, several academic centers have launched intensive research programs on the brain-computer interface (BCI). The current state of research allows to use certain properties of electromagnetic waves (brain activity) produced by brain neurons, measured using electroencephalographic techniques (EEG recording involves reading from electrodes attached to the scalp - the non-invasive method - or with electrodes implanted directly into the cerebral cortex - the invasive method). A BCI system reads the user's "intentions" by decoding certain features of the EEG signal. Those features are then classified and "translated" (on-line) into commands used to control a computer, prosthesis, wheelchair or other device. In this article, the authors try to show that the BCI is a typical example of a measurement and control unit.
Źródło:
Metrology and Measurement Systems; 2012, 19, 3; 427-444
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Joint time-frequency and wavelet analysis - an introduction
Autorzy:
Majkowski, A.
Kołodziej, M.
Rak, R. J.
Powiązania:
https://bibliotekanauki.pl/articles/220841.pdf
Data publikacji:
2014
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
frequency analysis
time-frequency analysis
short-time Fourier transform
Gabor transform
Wigner-Ville transform
Cone-Shaped Transform
wavelet analysis
time-scale analysis
wavelet decomposition
filter banks
wavelet packets
Opis:
A traditional frequency analysis is not appropriate for observation of properties of non-stationary signals. This stems from the fact that the time resolution is not defined in the Fourier spectrum. Thus, there is a need for methods implementing joint time-frequency analysis (t/f) algorithms. Practical aspects of some representative methods of time-frequency analysis, including Short Time Fourier Transform, Gabor Transform, Wigner-Ville Transform and Cone-Shaped Transform are described in this paper. Unfortunately, there is no correlation between the width of the time-frequency window and its frequency content in the t/f analysis. This property is not valid in the case of a wavelet transform. A wavelet is a wave-like oscillation, which forms its own “wavelet window”. Compression of the wavelet narrows the window, and vice versa. Individual wavelet functions are well localized in time and simultaneously in scale (the equivalent of frequency). The wavelet analysis owes its effectiveness to the pyramid algorithm described by Mallat, which enables fast decomposition of a signal into wavelet components.
Źródło:
Metrology and Measurement Systems; 2014, 21, 4; 741-758
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Decision Support System for Epileptogenic Zone Location During Brain Resection
Autorzy:
Kołodziej, M.
Majkowski, A.
Rak, R. J.
Rysz, A.
Marchel, A.
Powiązania:
https://bibliotekanauki.pl/articles/221787.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
ECoG
iEEG
electrocorticography
epileptogenic zone
signal analysis
expert system
neural network
Opis:
This paper presents a system for locating the epileptogenic zone (EZ) using an automated analysis of electrocorticography (ECoG) signal recorded with 20 electrodes placed on the brain surface. The developed system enables automatic determination of places where anomalies connected with epilepsy are observed. The developed algorithm was tested on signals recorded for 33 patients who, after a prior neurological analysis, underwent the brain resection surgery. The results obtained with the algorithm were compared with those of medical analyses performed by the neurologist. The proposed system has a satisfactory accuracy – 87.8% – and can be used as a decision-supporting tool by the neurosurgeon during brain resection.
Źródło:
Metrology and Measurement Systems; 2018, 25, 1; 15-32
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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