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Brain state dynamics vary at different spatiotemporal scales with behavior, stimulation, and disease, and may be unobserved (latent). Using a state-space model framework and subspace identification, we estimated spatiotemporally localized, latent state changes associated with the application of transcranial magnetic stimulation (TMS), to assess the effect of stimulation on brain state dynamics. State...
Dyslexia constitutes a specific reading disability, a condition characterized by severe difficulty in the mastery of reading despite normal intelligence or adequate education. Electroencephalogram (EEG) signal may be able to play an important role in the diagnosis of dyslexia. The Approximate Entropy (ApEn) is a recently formulated statistical parameter used to quantify the regularity of a time series...
Over 50 million people worldwide suffer from epilepsy. Traditional diagnosis of epilepsy relies on tedious visual screening by highly trained clinicians from lengthy EEG recording that contains the presence of seizure (ictal) activities. Nowadays, there are many automatic systems that can recognize seizure-related EEG signals to help the diagnosis. However, it is very costly and inconvenient to obtain...
Early recognition and aggressive management of seizure activity is important in the treatment of patients with nerve agent exposure. However, these patients can experience non-convulsive seizures that are difficult to identify without EEG monitoring. In this paper, we discuss the development and testing of a low-cost, field-deployable device that records and displays patient EEG trends over time....
The anti-social behaviors of the people who are characteristic of abnormal action have seriously affected our society. Recent years, with the development of brain science, the features of human's abnormal action have been identified by means of the low frontal lobe activities. However, in many countries, the corresponding systems for identification and treatment are in an insufficient situation. Thus,...
Vagus nerve stimulation (VNS) is an approved therapy for the treatment of adult patients and adolescents aged 12 years and older who have partial onset seizures refractory to antiepileptic medications. More than 50,000 patients worldwide have been implanted with the VNS system. Work continues to understand the mechanism of action of VNS with the goal of improving the treatment, particularly to identify...
Epilepsy is a neurological disorder that affects around 50 million people worldwide. The seizure detection is an important component in the diagnosis of epilepsy. In this study, the Empirical Mode Decomposition (EMD) method was proposed on the development of an automatic epileptic seizure detection algorithm. The algorithm first computes the Intrinsic Mode Functions (IMFs) of EEG records, then calculates...
Perinatal hypoxia remains a significant cause of brain damage. Currently there are no biomarkers to detect the at risk brain. Recent research, however, suggests that the appearance of epileptiform transients in the first 6-8 hours after hypoxia (the latent phase of injury) are predictive of neural outcome. To quantify this further a key need is to automate EEG signal analysis to aid clinical staff...
This paper examines whether an appropriate algorithm, developed for use with neonatal data, could also be used, without alteration, for the detection of seizures in adults with epilepsy. The performance of a feature extraction and SVM classifier system is evaluated on databases of 17 neonatal patients and 15 adult patients. Mean ROC curve areas of 0.96 and 0.94 for neonatal and adult databases respectively...
Epilepsy is a devastating disease of the central nervous system, affecting ~1% of the world's population. Drug therapy is effective in many patients, but 25% of the patients do not respond to anticonvulsants. Surgical resection can be an effective treatment but is associated with serious complications that can remove it as an option. Electrical stimulation has been successful to control abnormal activity...
In this study, the scanning EMG technique was implemented to investigate electrophysiological cross-sections of the motor unit (MU) territories in healthy volunteers and in subjects with juvenile myoclonic epilepsy and spinal muscular atrophy. Measurements were taken intramuscularly by means of two concentric needle electrodes from biceps brachialis muscles. 3-D maps of the MU territories were plotted...
This paper presents a novel theoretical paradigm for epileptic seizure prediction based on a coupled oscillator model of brain dynamics. This model is used to investigate prediction methods capable of tracking the synchronization changes that may lead to a seizure. Previous results indicate that state-space reconstruction of a coupled oscillator model from an EEG-like signal is ill-posed, therefore,...
We investigate thalamocortical interactions in the tetanus toxin and the cortical stimulation rat models of epilepsy. Using local field potential recordings from the cortex and the thalamus of the rat, the nonlinear regression index is calculated to create the direction index in order to study neurodynamics during seizures. Coarse time-scale analysis reveals that the cortex drives the thalamus for...
Electrical current is widely used to interact with or stimulate neural systems. Current transduction from device to tissue is mediated at the electrode-tissue interface by capacitive charge and electrochemistry. This charge-passing-capacity is frequency dependent. While safety parameters have been established for high-frequencies, safety has not been fully determined for novel materials and pulse...
A patient-specific model-based seizure detection method using statistically optimal null filters (SONF) has been recently proposed to aid the review of long-term EEG [1, 2]. The method relies on the model of a priori known seizure (template pattern) for subsequent detection of similar seizures. Artifacts, non-epileptic EEG rhythms, and at times modeling errors lead to increased false or missed detections...
In this study, a novel application of Principal Component Analysis (PCA) is proposed to detect language activation map patterns. These activation patterns were obtained by processing functional Magnetic Resonance Imaging (fMRI) studies on both control and localization related epilepsy (LRE) patients as they performed an auditory word definition task. Most group statistical analyses of fMRI datasets...
In this paper, we present a fuzzy rule-based system for the automatic detection of seizures in the intracranial EEG (IEEG) recordings. A total of 302.7 hours of the IEEG with 78 seizures, recorded from 21 patients aged between 10 and 47 years were used for the evaluation of the system. After preprocessing, temporal, spectral, and complexity features were extracted from the segmented IEEGs. The results...
In the present study, we utilize methods from graph theory to analyze epileptogenic network properties during periods of ictal activity. Using these methods, we analyzed the DTF-based causal information flow in nine seizures recorded from two patients undergoing presurgical monitoring for the treatment of medically intractable epilepsy. From the results, we observed a high degree of correlation between...
A simple algorithm to automatically detect segments with epileptic seizures in long EEG records has been developed. The main advantages of the proposed method are: the simple algorithm used and the lower computational cost. The algorithm measures the energy of each EEG channel by a sliding window and calculates some features of each patient signal to detect the epileptic seizure. It is also able to...
Most brain-computer interface classification experiments from electrical potential recordings have been focused on the identification of classes of stimuli or behavior where the timing of experimental parameters is known or pre-designated. Real world experience, however, is spontaneous, and to this end we describe an experiment predicting the occurrence, timing, and types of visual stimuli perceived...
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