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Evoked potentials are usually embedded in the ongoing electroencephalogram with a very low signal-to-noise ratio. The neural network filtering technique which has the advantage of complex mapping is one of the applicable methods for evoked potentials estimation. The backpropagation algorithm based on second order statistics is commonly used to adapt neural network filters. However it is easily influenced...
Electroencephalographic (EEG) signals are normally acquired in the presence of background noise which causes inaccurate or false entropy measurement throughout the signal. In this paper, spectral subtraction is used to pre-process EEG signals to improve the accuracy of computing the subband wavelet entropy (SWE). The silent period in the EEG signal is identified via cepstral distance which allows...
The aim of this study is to investigate whether EEG coherence during different functional states facilitates the detection of AD-related EEG changes; and which brain regions these changes were. The EEGs in both rest and performing the cognitive task states of 3 groups was recorded for coherence measure. The 3 groups are the mild cognitive impairment (MCI) group, Alzheimer's disease (AD) and the healthy...
In recent years, scientists, doctors in the field of biomedical engineering and researchers of the correlated fields have been concentrating on study of activities of bioelectricity of different cortex fields of human brain on the condition of different evocable and cognitive stimulations, and try to test human psychology and physiology, and control exterior environment. Independent component analysis...
Frequent arousals during sleep degrade the quality of sleep and result in sleep fragmentation. Visual inspection of physiological signals to detect the arousal events is inconvenient and time-consuming work. The purpose of this study was to develop an automatic algorithm to detect the arousal events. We proposed the automatic method to detect arousals based on time-frequency analysis and the support...
In the nearest future a lot of post-operative treatment and health monitoring is going to be performed in people's homes instead of in hospital. The increasing number of elderly people in the developed countries and the need for more advanced medical treatment and equipment in conjunction with economical demands will force the development of more cost-effective solutions. The equipment used must be...
This study aims to determine whether or not the mismatch negativity (MMN) is involved in the processing on time-frequency distribution of acoustic information. Invariable, step down and gradually decreasing time-frequency distribution complex tones compose the three kinds of deviant stimuli, which appear randomly in the repeating standard tones sequence. MMNs were evoked by the deviant stimuli. The...
Dynamic synchronization between different brain regions has long been considered as the underlying neural mechanism of sensory, motor and cognitive functions. Practical methods of accurately quantifying this kind of dynamics by using scalp EEG are plagued by volume conduction effects and background noise. We propose a new method of measuring transient phase locking between independent components underlying...
How to effectively remove the magnetic resonance imaging (MRI) artifacts in the electroencephalography (EEG) recordings, induced when EEG and functional magnetic resonance imaging (FMRI) are simultaneously recorded, is a challenge for integration of EEG and FMRI. According to the temporal-spatial difference between MRI artifacts and EEG, a new method based on sparse component decomposition in the...
Invasive intracranial electroencephalography (EEG) studies help identify the epileptogenic focus and assess if the identified zone overlies eloquent cortex by means of cortical stimulation. Proper interpretation and use of the intracranial recording/stimulation studies requires an effective display of multimodal information. We developed a software system which can combine the patient's segmented...
This paper describes a three-stage system for the detection of neonatal seizures. The first stage detects 5-s seizure segments using signal processing and pattern recognition techniques. In the second stage, the seizure segments overlapping with artifactual segments are marked for post-processing using rules. Rules add intelligence to the spatio-temporal clustering in the third stage, by incorporating...
In this paper a new method for muscle artifact removal in EEG is presented, based on canonical correlation analysis (CCA) as a blind source separation technique (BSS). This method is demonstrated on a synthetic data set. The method outperformed a low pass filter with different cutoff frequencies and an independent component analysis (ICA) based technique for muscle artifact removal. The first preliminary...
Symbolic dynamics is a useful tool in several fields of complexity analysis in nonlinear science. In order to investigate complexities of the human brain electrical activities under different brain functional states, a novel method in terms of symbolic entropy is defined and proposed in this paper. The novel algorithm based on symbolic dynamics is developed for quantitatively measuring the complexity...
A novel approach is proposed to deal with the problem of detecting the single trial ERP using a modified RBF neural network, rational Gaussian network. The Gaussian RBF is normalized to obtain optimal behavior of noise suppression even at low SNR. The performance of the proposed scheme is also evaluated with both MSE and the tracking ability. Several experimental results with real ERP signals provide...
Wavelet entropy (WE), a new method of complexity measure for non-stationary signals, was used to investigate the dynamic features of rat EEGs under three vigilance states. The EEGs of the freely moving rats were recorded with implanted electrodes and were decomposed into four components of delta, theta, alpha and beta by using multi-resolution wavelet transform. Then, the wavelet entropy curves were...
A novel parametric method, based on the non-Gaussian AR model, is proposed for the partition of non-stationary EEG data into a finite set of third-order stationary segments. With the assumption of piecewise third-order stationarity of the signal, a series of parametric bispectral estimations of the non-stationary EEG data can be performed so as to describe the time-varying non-Gaussian nonlinear characteristics...
The FEM geometry modeling of realistic head is a key issue for the research on FEM-based EEG/MEG. In this paper, a methodology is developed to construct this kind of model. By using this method, a five -layer realistic head FEM model is obtained, and with its application in FEM-based EEG, a satisfying result shows the reliability of the model
Sleep apnea syndrome (SAS) is a very common sleep disorder disease. Reliable detection of apnea is very crucial for subsequent treatment. In this article, a novel method based on artificial neural network is proposed for such purpose. With its time-invariant property the time delay neural network (TDNN) is adopted in this system to employ the temporal trend of apnea event. As airflow and SaO take...
Most hospitals in the world do not perform electroencephalograms in the emergency departments due to space, cost, training, and complexity of the equipment and the test. New miniature, low-cost, simple, digital, wireless EEG machines have been developed that solve all four of these inhibiting factors to allow EEG, to be used in emergency departments to evaluate patients presenting with altered mental...
Electroencephalograms (EEGs) reflect the electrical activity of the brain. The problem of analyzing and interpreting the meaning of these signals has received a great deal of attention. Since EEG signals may be considered chaotic, chaos theory may supply effective quantitative descriptors of EEG dynamics and of underlying chaos in the brain. The complexity of the chaotic system can be characterized...
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