Analysis of amplitude and phase characteristics for delta, theta, and alpha bands at localized time instant from EEG signals is important for the characterizing information processing in the brain. In this paper, complex demodulation method was used to analyze EEG (Electroencephalographic) signal, particularly for auditory evoked potential response signal, with sufficient time resolution and de...

Emotion recognition still poses a challenge lying at the core of rapidly growing area affective computing and is crucial for establishing successful human–computer interaction. Identification understanding emotions are achieved through various measures, such as subjective self-reports, face-tracking, voice analysis, gaze-tracking, well analysis autonomic central neurophysiological measurements....

EEG signal analysis is applied in various fields such as medicine, communication and control. To control based on EEG signals achieved good result, the system must identify effectively EEG signals. In this paper, a novel approach proposes the EEG signal identification based on image with the EEG signal processing via Wavelet transform and the identification via single-layer neural network. The ...

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...

epilepsy is an important disease with a cumulative incidence of 3% all over the life and more than half of them are started from childhood. in this study we surveyed magnetic resonance imaging (mri) findings in epileptic children and its relation with clinical and demographic findings in order to find better diagnostic and treatment modalities for these children in the future. in this cross sec...

background: this paper proposes a new emotional stress assessment system using multi-modal bio-signals. electroencephalogram (eeg) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research. methods: we design an efficient acquisition protocol to acquire the eeg signals in five channels (fp1, fp2, t3, t4 and pz) and peripheral signals such as blood volu...

Since its invention by the Hans Berger of the electroencepha-lography (EEG) in 1929, it was a strong scientific curiosity in analysis of human brain activity. In fact, the electroen-cephalography (EEG) and magnetoencephalography (MEG) have developed into one of the most important and widely used quantitative diagnostic tools in analysis of brain signals and patterns. EEG and MEG potentially con...

Concurrent EEG and fMRI acquisitions in resting state showed a correlation between EEG power in various bands and spontaneous BOLD fluctuations. However, there is a lack of data on how changes in the complexity of brain dynamics derived from EEG reflect variations in the BOLD signal. The purpose of our study was to correlate both spectral patterns, as linear features of EEG rhythms, and nonline...

Analysis of amplitude and phase characteristics for delta, theta, and alpha bands at localized time instant from EEG signals is important for the characterizing information processing in the brain. In this paper, complex demodulation method was used to analyze EEG (Electroencephalographic) signal, particularly for auditory evoked potential response signal, with sufficient time resolution and de...

An important challenge in brain research is to make out the relation between the features of olfactory stimuli and the electroencephalogram (EEG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the EEG signal. This study investigates the relation between the structures of EEG signal and the olfactory stimulus (odorant). We show that the complexity...