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Correlation size together with Lyapunov exponents estimated from both electroencephalography (EEG) and electromyography (EMG) signals, are the crucial variables in the classification of mental tasks using an artificial neural network (ANN) classifier for patients suffering from neurological disorders/diseases. The above parameters vary according to the status of the patient, for example: depending...
To explore the cross-information in multi-modal data, several multivariate data fusion techniques have been proposed. Partial least square (PLS) has great potential for neuroimaging studies. However, when performing group analysis with PLS, the presence of inter-subject variability makes the conventional technique of simply pooling data from different subjects problematic. To circumvent this issue,...
In this paper, we have studied electroencephalogram (EEG) activity of schizophrenia patients, in resting eyes closed condition, with detrended fluctuation analysis (DFA). The DFA gives information about scaling and long-range correlations in time series. We computed DFA exponents from 30 scalp locations of 18 male neuroleptic-naïve, recent-onset schizophrenia (NRS) subjects and 15 healthy male control...
Thalamus and its interaction with cerebral cortex are considered essential in the generation and propagation of spike and wave discharge (SWD). Our objective is to evaluate the spontaneous functional connectivity between the thalamus and cerebral cortex in absence epilepsy. We use the resting-state fMRI to determine the whole brain functional connectivity with the mediodorsal thalamic nucleus (MDTN)...
In this paper, we consider the problem of quantifying synchrony between multiple simultaneously recorded electroencephalographic signals. These signals exhibit nonlinear dependencies and non-Gaussian statistics. A copula based approach is presented to model the joint statistics. We then consider the application of copula derived synchrony measures for early diagnosis of Alzheimer's disease. Results...
Movement-related changes such as event-related desynchronizationcan (ERD) and event-related synchronization (ERS) can be found in human subthalamic nucleus (STN) with analysis on local field potentials (LFP) recorded from Parkinson's disease (PD) patients. Besides traditional time-frequency (TF) analysis, we introduced nonlinear analysis, bispectral and approximate entropy (ApEn), to measure the signal...
Manual/visual polysomnogram (psg) analysis is a standard and commonly implemented procedure utilized in the diagnosis and treatment of sleep related human pathologies. Current technological trends in psg analysis focus upon translating manual psg analysis into automated/computerized approaches. A necessary first step in establishing efficient automated human sleep analysis systems is the development...
The aim of this study was to analyse the electroencephalogram (EEG) background activity in Alzheimer's disease (AD) with the Detrended Moving Average (DMA) method, a new approach to quantify correlation properties in non-stationary signals with underlying trends. EEGs were recorded from the 19 scalp loci of the international 10-20 system in 11 AD patients and 11 age-matched controls. Our results showed...
It has been shown that acupuncture at acupoint of Shenmen can change the activities of brain and cure some neural diseases in clinic. Magnetic stimulation is a new kind of stimulating technique of non-invasive and painless. In this work, the effects of magnetic stimulation by stimulating the acupoint of Shenmen are studied. Moreover, the connections between the brain functional state and the chaos...
EEG signal is a typical nonlinear time series and its correlation dimension can be calculated to measure the series. In this paper, the correlation dimensions of 30 healthy samples and 30 patient samples are calculated. By the statistic result, an important conclusion is represented that the values of correlation dimension of different groups exist great difference. Based on more clinical experiments,...
Typically data acquired through imaging techniques such as functional magnetic resonance imaging (fMRI), structural MRI (sMRI), and electroencephalography (EEG) are analyzed separately. However, fusing information from such complementary modalities promises to provide additional insight into connectivity across brain networks and changes due to disease. We propose a data fusion scheme at the feature...
Numerous works have been dedicated to the development of signal processing methods aimed at measuring the degree of association between EEG signals. This interdependency parameter is often used to characterize the functional coupling between different brain structures or regions during either normal or pathological processes. In this paper we focus on the time-frequency characterization of interdependencies...
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