Wyniki wyszukiwania - Biblioteka Nauki

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: information transfer rate

Sortuj według:

Ogranicz wyniki do:

On diversity within operators' EEG responses to LED-produced alternate stimulus in SSVEP BCI

100%

Byczuk M. , Poryzała P. , Materka A.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2012

tom Vol. 60, nr 3

447-453

This work is an attempt to identify causes of the widely observed fact that performance of Brain-Computer Interface systems based on Steady-State Visual Evoked Potentials varies between different users. The efficient LED-produced alternate stimulus systems are taken into account. The effect of stimulus color and flickering frequency on measured SSVEP response at first and second harmonics is investigated for 10 women and 11 men. The experimental setup is described, measurement procedure, signal processing and analysis algorithms are outlined. The results are presented and discussed. One of the early conclusions drawn from this extensive research is that the promising strategy of SSVEP-based BCI system optimization for best performance can be through stimulus adjustment to each individual user.

Feature selection for classification in Steady state visually evoked potentials (SSVEP)-based brain-computer interfaces with genetic algorithm

86%

Karkosz S. , Jukiewicz M.

Bio-Algorithms and Med-Systems

2020

tom Vol. 16, no. 2

art. no. 20200013

Objectives: Optimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy. Methods: System of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI). Results: The designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects. Conclusions: It is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.