13,432 research outputs found
Review of analytical instruments for EEG analysis
Since it was first used in 1926, EEG has been one of the most useful
instruments of neuroscience. In order to start using EEG data we need not only
EEG apparatus, but also some analytical tools and skills to understand what our
data mean. This article describes several classical analytical tools and also
new one which appeared only several years ago. We hope it will be useful for
those researchers who have only started working in the field of cognitive EEG
On-Off Intermittency in Time Series of Spontaneous Paroxysmal Activity in Rats with Genetic Absence Epilepsy
Dynamic behavior of complex neuronal ensembles is a topic comprising a
streamline of current researches worldwide. In this article we study the
behavior manifested by epileptic brain, in the case of spontaneous
non-convulsive paroxysmal activity. For this purpose we analyzed archived
long-term recording of paroxysmal activity in animals genetically susceptible
to absence epilepsy, namely WAG/Rij rats. We first report that the brain
activity alternated between normal states and epilepsy paroxysms is the on-off
intermittency phenomenon which has been observed and studied earlier in the
different nonlinear systems.Comment: 11 pages, 6 figure
Mapping the epileptic brain with EEG dynamical connectivity: established methods and novel approaches
Several algorithms rooted in statistical physics, mathematics and machine learning are used to analyze neuroimaging data from patients suffering from epilepsy, with the main goals of localizing the brain region where the seizure originates from and of detecting upcoming seizure activity in order to trigger therapeutic neurostimulation devices. Some of these methods explore the dynamical connections between brain regions, exploiting the high temporal resolution of the electroencephalographic signals recorded at the scalp or directly from the cortical surface or in deeper brain areas. In this paper we describe this specific class of algorithms and their clinical application, by reviewing the state of the art and reporting their application on EEG data from an epileptic patient
Wavelet analysis of epileptic spikes
Interictal spikes and sharp waves in human EEG are characteristic signatures
of epilepsy. These potentials originate as a result of synchronous,
pathological discharge of many neurons. The reliable detection of such
potentials has been the long standing problem in EEG analysis, especially after
long-term monitoring became common in investigation of epileptic patients. The
traditional definition of a spike is based on its amplitude, duration,
sharpness, and emergence from its background. However, spike detection systems
built solely around this definition are not reliable due to the presence of
numerous transients and artifacts. We use wavelet transform to analyze the
properties of EEG manifestations of epilepsy. We demonstrate that the behavior
of wavelet transform of epileptic spikes across scales can constitute the
foundation of a relatively simple yet effective detection algorithm.Comment: 4 pages, 3 figure
EEG analytics for early detection of autism spectrum disorder: a data-driven approach
Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. Finding scalable biomarkers for early detection is challenging because of the variability in presentation of the disorder and the need for simple measurements that could be implemented routinely during well-baby checkups. EEG is a relatively easy-to-use, low cost brain measurement tool that is being increasingly explored as a potential clinical tool for monitoring atypical brain development. EEG measurements were collected from 99 infants with an older sibling diagnosed with ASD, and 89 low risk controls, beginning at 3 months of age and continuing until 36 months of age. Nonlinear features were computed from EEG signals and used as input to statistical learning methods. Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. Specificity, sensitivity and PPV were high, exceeding 95% at some ages. Prediction of ADOS calibrated severity scores for all infants in the study using only EEG data taken as early as 3 months of age was strongly correlated with the actual measured scores. This suggests that useful digital biomarkers might be extracted from EEG measurements.This research was supported by National Institute of Mental Health (NIMH) grant R21 MH 093753 (to WJB), National Institute on Deafness and Other Communication Disorders (NIDCD) grant R21 DC08647 (to HTF), NIDCD grant R01 DC 10290 (to HTF and CAN) and a grant from the Simons Foundation (to CAN, HTF, and WJB). We are especially grateful to the staff and students who worked on the study and to the families who participated. (R21 MH 093753 - National Institute of Mental Health (NIMH); R21 DC08647 - National Institute on Deafness and Other Communication Disorders (NIDCD); R01 DC 10290 - NIDCD; Simons Foundation)Published versio
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