نتایج جستجو برای: eeg signals
تعداد نتایج: 217307 فیلتر نتایج به سال:
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...
objective: electroencephalogram is a reliable reflection of many physiological factors modulating the brain. the bispectrum is very useful for analyzing non-gaussian signals such as eeg, and detecting the quadratic phase coupling between distinct frequency components in eeg signals.the main aim of this study was to test the existence of nonlinear phase coupling within the eeg signals in a certa...
Introduction: In this paper, a novel complexity measure is proposed to detect dynamical changes in nonlinear systems using ordinal pattern analysis of time series data taken from the system. Epilepsy is considered as a dynamical change in nonlinear and complex brain system. The ability of the proposed measure for characterizing the normal and epileptic EEG signals when the signal is short or is...
Detection and Classification of Emotions Using Physiological Signals and Pattern Recognition Methods
Introduction: Emotions play an important role in health, communication, and interaction between humans. The ability to recognize the emotional status of people is an important indicator of health and natural relationships. In DEAP database, electroencephalogram (EEG) signals as well as environmental physiological signals related to 32 volunteers are registered. The participants in each video we...
Many efforts have been done to predict epileptic seizures so far. It seems that some kind of abnormal synchronization among brain areas is responsible for the seizure generation. This is because the synchronization-based algorithms have been the most important methods so far. However, the huge number of EEG channels, which is the main requirement of these methods, make them very difficult to us...
The record of human brain neural activities, namely electroencephalogram (EEG), is generally known as a non-stationary and nonlinear signal. In many applications, it is useful to divide the EEGs into segments within which the signals can be considered stationary. Combination of empirical mode decomposition (EMD) and Hilbert transform, called Hilbert-Huang transform (HHT), is a new and powerful ...
Background and purpose: The nonlinear quality of electroencephalography (EEG), like other irregular signals, can be quantified. Some of these values, such as Lyapunovchr('39')s representative, study the signal path divergence and some quantifiers need to reconstruct the signal path but some do not. However, all of these quantifiers require a long signal to quantify the signal complexity. Mate...
introduction: evoked potentials arisen by stimulating the brain can be utilized as a communication tool between humans and machines. most brain-computer interface (bci) systems use the p300 component, which is an evoked potential. in this paper, we evaluate the use of the hidden markov model (hmm) for detection of p300. materials and methods: the wavelet transforms, wavelet-enhanced indepen...
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Introduction: Evoked potentials arisen by stimulating the brain can be utilized as a communication tool between humans and machines. Most brain-computer interface (BCI) systems use the P300 component, which is an evoked potential. In this paper, we evaluate the use of the hidden Markov model (HMM) for detection of P300. Materials and Methods: The wavelet transforms, wavelet-enhanced indepen...
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