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 cl...

in recent decades, seizure prediction caused a lot of research in both signal processing and neuroscience field. the researches tried to enhance the conventional seizure prediction algorithms such that rate of the false alarms be appropriately small so that seizures can be predicted according to clinical standards. up to now none of the proposed algorithms have been sufficiently adequate. in th...

In this paper, unique approach is presented for the electroencephalography (EEG) signals analysis. This is based on Eigen values distribution of a matrix which is called as scaled Hankel matrix. This gives us a way to find out the number of Eigen values essential for noise reduction and extraction of signal in singular spectrum analysis. This paper gives us an approach to classify the EEG signa...

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...

Introduction and Aims: Alzheimer’s disease is the most prevalent neurodegenerative disorder and a type of dementia. 80% of dementia in older adults is because of Alzheimer’s disease. According to multiple research articles, Alzheimer's has several changes in EEG signals such as slowing of rhythms, reduction in complexity and reduction in functional associations, and disordered functional commun...

Introduction: Brain visual evoked potential (VEP) signals are commonly known to be accompanied by high levels of background noise typically from the spontaneous background brain activity of electroencephalography (EEG) signals. Material and Methods: A model based on dyadic filter bank, discrete wavelet transform (DWT), and singular value decomposition (SVD) was developed to analyze the raw data...

electroencephalogram (eeg) is one of the useful biological signals to distinguish different brain diseases and mental states. in recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from eeg signals. in this research, we introduce an emot...

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 ...

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...