Implementation of genetic algorithms to feature selection for the use of brain-computer interface
نویسندگان
چکیده
The main goal of the article is to apply genetic algorithms to feature selection for the use of brain-computer interface (BCI). FFT coefficients of EEG signal were used as features. The best features for a BCI system depends on the person who uses the system as well as on the mental state of the person. Therefore, it is very important to apply efficient methods of feature selection. The genetic algorithm proposed by authors enables to choose the most representative features and electrodes. Streszczenie. W artykule przedstawiono zastosowanie algorytmów genetycznych do selekcji cech na użytek interfejsów mózg-komputer (BCI). Najlepszy zestaw cech dla tego typu interfejsów jest zależny od osoby, która używa interfejsu, jak również od jej stanu psychicznego. Z tego powodu konieczne jest zastosowanie bardzo efektywnych metod selekcji cech. Jako cechy wykorzystane zostały współczynniki FFT sygnału EEG. Zaproponowany przez autorów algorytm genetyczny umożliwia wyznaczenie najbardziej reprezentatywnego zbioru cech, jak również elektrod, z których pobierany jest sygnał EEG. (Zastosowanie algorytmów genetycznych do selekcji cech na użytek interfejsów mózg-komputer)
منابع مشابه
Improvement of effort estimation accuracy in software projects using a feature selection approach
In recent years, utilization of feature selection techniques has become an essential requirement for processing and model construction in different scientific areas. In the field of software project effort estimation, the need to apply dimensionality reduction and feature selection methods has become an inevitable demand. The high volumes of data, costs, and time necessary for gathering data , ...
متن کاملA review on EEG based brain computer interface systems feature extraction methods
The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each pha...
متن کاملA review on EEG based brain computer interface systems feature extraction methods
The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each pha...
متن کاملApplying Genetic Algorithm to EEG Signals for Feature Reduction in Mental Task Classification
Brain-Computer interface systems are a new mode of communication which provides a new path between brain and its surrounding by processing EEG signals measured in different mental states. Therefore, choosing suitable features is demanded for a good BCI communication. In this regard, one of the points to be considered is feature vector dimensionality. We present a method of feature reduction us...
متن کاملSelecting and Extracting Effective Features of SSVEP-based Brain-Computer Interface
User interfaces are always one of the most important applied and study fields of information technology. The development and expansion of cognitive science studies and functionalization of its tools such as BCI1, as well as popularization of methods such as SSVEP2 to stimulate brain waves, have led to using these techniques every day, especially in appropriate solutions for physically and menta...
متن کاملFeature Selection Using Multi Objective Genetic Algorithm with Support Vector Machine
Different approaches have been proposed for feature selection to obtain suitable features subset among all features. These methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions are divided into two main groups: filter and wrapper methods. In filter methods, features subsets are selected due to some measu...
متن کامل