This paper has proposed a new classification method based on Hilbert probability similarity to detect epileptic seizures from electroencephalogram (EEG) signals. probability-based measure is exploited the between The system consisted of models (HPS) predict state for specific EEG signal. Particle swarm optimization (PSO) been employed feature selection and extraction. Furthermore, used dataset ...

We presents a refined multiscale Shannon entropy for analyzing electroencephalogram (EEG), which reflects the underlying dynamics of EEG over multiple scales. The rationale behind this method is that neurological signals such as EEG possess distinct dynamics over different spectral modes. To deal with the nonlinear and nonstationary nature of EEG, the recently developed empirical mode decomposi...

The electroencephalogram (EEG), a highly complex signal, is one of the most common sources of information used to study brain function and neurological disorders. More than 100 current neural network applications dedicated to EEG processing are presented. Works are categorized according to their objective (sleep analysis, monitoring anesthesia depth, brain-computer interface, EEG artifact detec...

objective: the aim of this study was to assess and compare the effect of two neurofeedback protocols (smr/theta and beta/theta) on adhd symptoms, selective attention and eeg (electroencephalogram) parameters in children with adhd. method: the sample consisted of 16 children (9-15 year old: 13 boys; 3 girls) with adhd-combined type (adhd-c). all of children used methylphenidate (mph) during the ...

Understanding brain reactions to facial expressions can help in explaining emotion-processing and memory mechanisms. The purpose of this research is examine the dynamics electrical activity caused by visual emotional stimuli. focus on detecting changes cognitive mechanisms produced negative, positive, neutral human faces. Three methods were used study reactions: power spectral density, detrendi...

The applications of Electroencephalogram (EEG) have been extended to out laboratory and clinics recently due the advancements in technical capabilities. There are various advantageous EEG, making it a preferable method for wide range applications; is noninvasive method, portable, offers good time resolution sufficient spatial resolution, besides there low cost EEG systems available commercial u...

Electroencephalogram (EEG) signals reveal electrical activity of brain in a person. Brain cells interact by impulses even during sleep. Any disruptions to these induce problems the individual. Hence clinicians analyze EEG readings comprehend impulses. and its sub bands depict pattern human brain. data comprises transient components, spikes, other sorts artifacts due eye blinking, movement indiv...

This paper presents a technique to provide an acoustic representation of electroencephalogram (EEG) data using neural networks. The sample EEG consists of actual random movements of left and right hand recorded with eyes closed of a 21-year old, right handed male with no known medical conditions. In addition, an EEG signal simulator was used to generate random EEG signals aside from the actual ...

EEG is a collaborative tool to diagnosis brain function and disease. Modern interpretation of EEG origin rest with knowledge of basic neuronal electrochemical process. The EEG is composed of electrical rhythms and transient discharges which are distinguished by location, frequency, amplitude, form, periodicity and functional properties. The low-level EEG Signal from the brain as recorded is amp...

OBJECTIVE To evaluate the relationship between specific cognitive aspects and quantitative EEG measures, in patients with mild or moderate Alzheimer's disease (AD). METHOD Thirty-eight AD patients and 31 controls were assessed by CERAD neuropsychological battery (Consortium to Establish a Registry for AD) and the electroencephalogram (EEG). The absolute power and coherences EEG measures were ...