Frequency component selection for an EEG-based brain to computer interface

1999 ◽  
Vol 7 (4) ◽  
pp. 413-419 ◽  
Author(s):  
M. Pregenzer ◽  
G. Pfurtscheller
Keyword(s):  
Frequency Component ◽  
Computer Interface ◽  
Component Selection ◽  
Selection For

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

2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Stanisław Karkosz ◽  
Marcin Jukiewicz
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Steady State ◽  
Brain Computer Interface ◽  
Morphological Features ◽  
Computer Interface ◽  
Visually Evoked Potentials ◽  
Computer Interfaces ◽  
Subject Variability ◽  
Selection For

AbstractObjectivesOptimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy.MethodsSystem 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).ResultsThe 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.ConclusionsIt is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.

Approach on Component Selection for Ageing-Trend Analysis Within PSA Models

2009 ◽  
Author(s):  
Shahen Poghosyan ◽  
Armen Amirjanyan ◽  
Albert Malkhasyan
Keyword(s):  
Trend Analysis ◽  
Significant Contribution ◽  
Reactor Core ◽  
Component Selection ◽  
Core Damage ◽  
Ageing Effects ◽  
Level 1 ◽  
Selection For ◽  
Definition Of ◽  
Actual Configuration

The major advantage of PSA is the possibility of in-depth qualitative and quantitative analysis of NPP actual configuration with definition of factors introducing a significant contribution to the general risk of reactor core damage. However main lack of the PSA current models is neglect of equipment ageing effects. Neglecting of ageing effects in PSA could lead to incorrectness of risk profile and influent on risk-informed decision making process. To solve this issue incorporation of ageing aspects into PSA models for Armenian NPP Unit 2 was initiated. Implementation of ageing trend analysis for all PSA components is insuperable effort, so the first step of the analysis is component selection activity. This paper is addressing the approach on component selection for ageing-trend analysis within PSA models. Presented approach is based on ageing effect and risk importance data. The procedure was developed and implemented in the framework of ageing aspects incorporation into PSA level 1 model for Armenian NPP Unit 2.

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