EEG Signals Classification by S-shaped Radial Implicative Fuzzy Systems

نویسنده

  • David Coufal
چکیده

The paper introduces an EEG signals classifier for classification of vigilance level of a car driver. The classifier is based on the concept of radial implicative fuzzy system. The novel in the presented approach is accommodation of S-shaped fuzzy sets which can handle boundary regions of the relevant input space. Both structure and parameter learning of the system are referred to and corresponding classification abilities are presented.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Real Time Driver’s Drowsiness Detection by Processing the EEG Signals Stimulated with External Flickering Light

The objective of this study is development of driver’s sleepiness using Visually Evoked Potentials (VEP). VEP computed from EEG signals from the visual cortex. We use the Steady State VEPs (SSVEPs) that are one of the most important EEG signals used in human computer interface systems. SSVEP is a response to visual stimuli presented. We present a classification method to discriminate between...

متن کامل

Radial Fuzzy Systems

The class of radial fuzzy systems is introduced. The fuzzy systems in this class use radial functions to implement membership functions of fuzzy sets and exhibit a shape preservation property in antecedents of their rules. The property is called the radial property. It enables the radial fuzzy systems to have their computational model mathematically tractable under both conjunctive and implicat...

متن کامل

Coherence of radial implicative fuzzy systems with nominal consequents

In the paper we are interested in the question of coherence of radial implicative fuzzy systems with nominal consequents (radial I-FSs with NCs). Implicative fuzzy systems are fuzzy systems employing residuated fuzzy implications for representation of IF-THEN structure of their rules. Radial fuzzy systems are fuzzy systems exhibiting the radial property in antecedents of their rules. The proper...

متن کامل

Applying 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 کامل

Epileptic Seizure Detection in EEG signals Using TQWT and SVM-GOA Classifier

Background: Epilepsy is a Brain disorder disease that affects people's quality of life. If it is diagnosed at an early stage, it will not be spread. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. However, this screening system cannot diagnose epileptic seizure states precisely. Nevertheless, with the help of computer-aided diagnosis systems (CADS), neurologists ca...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2011