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In the present paper, an electroencephalography (EEG)-based real-time dynamic neuroimaging system, which was recently developed by the authors, is introduced and its potential applications are presented. The real-time system could monitor spatiotemporal changes of cortical rhythmic activity on a subject's cortical surface, not on the subject's scalp surface, with a high temporal resolution. The developed...
Loss of alertness can have dire consequences for people controlling motorized equipment or for people in professions such as defense. Electroencephalogram (EEG) is known to be related to alertness of the person, but due to high level of noise and low signal strength, the use of EEG for such applications has been considered to be unreliable. This study reports the fractal analysis of EEG and identifies...
The stationary wavelet packet analysis is exploited for the first time in the design of a self-paced BCI based on mental tasks. The BCI system is custom designed to achieve a zero false positive rate, as false activations highly restricts the applications of BCIs in real life. The EEG signals of four subjects performing five different mental tasks are used as the dataset. The stationary wavelet packets...
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...
This paper addresses the topic of evaluating the significance of frequency domain measures of causal coupling in multivariate time series through generation of surrogate data. The considered approaches are the traditional Fourier Transform (FT) algorithm and a new causal FT (CFT) algorithm for surrogate data generation. Both algorithms preserve the FT modulus of the original series; differences are...
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...
l0 norm is an effective constraint used to solve EEG inverse problem for a sparse solution. However, due to the discontinuous and un-differentiable properties, it is an open issue to solve the l0 norm constrained problem, which is usually instead solved by using some alternative functions like l1 norm to approximate l0 norm. In this paper, a continuous and differentiable function having the same form...
In this work, it is proposed a technique for the feature extraction of electroencephalographic (EEG) signals for classification of mental tasks which is an important part in the development of Brain Computer Interfaces (BCI). The Empirical Mode Decomposition (EMD) is a method capable to process nonstationary and nonlinear signals as the EEG. This technique was applied in EEG signals of 7 subjects...
In this paper, we propose a solution to the EEG source localization problem considering its dynamic behavior. We assume a dipolar approach which makes the problem nonlinear. From the dynamic probabilistic model of the problem, we formulate the extended Kalman filter and particle filter solutions. In order to test the algorithms, we designed an experimental protocol based on error-related potentials...
We investigate the potential of using EEG recordings of observers performing a rapid visual categorization task for person identification. We examine a 0.5 s epoch of EEG data using machine learning techniques that, unlike most previous studies, analyze the data in a holistic manner and extracts discriminative spatio-temporal filters. The analysis of the filters suggest sparse feature representation...
It has frequently been reported in the medical literature that the EEG of Alzheimer disease (AD) patients is less synchronous than in healthy subjects. In this paper, it is explored whether loss in EEG synchrony can be used to diagnose AD at an early stage. Multiple synchrony measures are applied to two different EEG data sets: (1) EEG of pre-dementia patients and control subjects; (2) EEG of mild...
An electroencephalograph (EEG)-based brain computer interface (BCI) requires rapid and reliable extraction of features in EEG signal. Recently, the rhythmic component extraction (RCE) method has been proposed to extract features of multi-channel EEG. RCE can extract a signal component with a certain frequency from multi-sensor signals. In this paper, we applied RCE to extract a feature corresponding...
Individuals with certain sleep disorders (e.g. narcolepsy) are subject to uncontrollable sleep episodes accompanied by cataplexy and thus these patients are more vulnerable to household and occupational accidents. Currently, narcolepsy has no cure, and this research pursues developing a portable medical device to assist in narcolepsy treatment through providing diagnosis, real-time detection and logging...
Sleep spindles are one of the most important short-lasting rhythmic events occurring in the EEG during Non-Rapid Eye Movement sleep. Their accurate identification in a polysomnographic signal is essential for sleep professionals to help them mark Stage 2 sleep. Visual spindle scoring however is a tedious workload, as there are often a thousand spindles in an all-night recording. In this paper a novel...
Rhythmic electroencephalographic (EEG) activities associated with movement imaginations are widely used in developing noninvasive Brain-Computer Interface (BCI) towards replacing or restoring the lost motor function in the paralytics. And it is of great importance to develop imaging techniques to enhance the spatial resolution and specificity of the EEG modality. In our work, we developed an innovative...
A major limitation of current Brain-Computer Interfaces (BCI) based on Motor Imagery (MI) is that they are subject-specific BCI, which require data recording and system training for each new user. This process is time consuming and inconvenient, especially for casual users or portable BCI with limited computational resources. In this paper, we explore the design of a Subject-Independent (SI) MI-based...
In this paper, we intend to investigate further the effects of single pulse TMS (sTMS) on auditory attention through an experimental design that combines a modified version of maximum entropy stimulation paradigm. Single pulses of TMS with 4.4s inter-stimulus interval (ISI) were applied to the left temporal lobe of subjects while three randomized auditory stimuli with constant ISI of 1.1s were delivered...
In this work we present an integrated method for electroencephalography (EEG) source localization in newborn infants, based on a realistic head model. To build a realistic head model we propose an interactive hybrid segmentation method for T1 magnetic resonance images (MRI), consisting of active contours, fuzzy c-means (FCM) clustering and mathematical morphology. Subsequently, we solve the localization...
We propose using a state-space model to estimate cortical connectivity from scalp-based EEG recordings. A state equation describes the dynamics of the cortical signals and an observation equation describes the manner in which the cortical signals contribute to the scalp measurements. The state equation is based on a multivariate autoregressive (MVAR) process model for the cortical signals. The observation...
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....
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