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Detecting connectivity in EEG: A comparative study of data-driven effective connectivity measures.
In this paper, we perform the first comparison of a large variety of effective connectivity measures in detecting causal effects among observed interacting systems based on their statistical significance. Well-known measures estimating direction and strength of interdependence between time series are compared: information theoretic measures, model-based multivariate measures in the time and frequency domains, and phase-based measures. The performance of measures is tested on simulated data from three systems: three coupled Hénon maps; a multivariate autoregressive (MVAR) model with and without EEG as an exogenous input; and simulated EEG. No measure was consistently superior. Measures that model the data as MVAR perform well when the data are drawn from that model. Frequency domain measures perform well when the data have a clearly defined band of interest. When neither of these is true, information theoretic measures perform well. Overall, the measure with the best performance in a variety of situations and with a low computational cost is conditional Granger causality. Partial Granger causality and multivariate Granger causality are also good measures, but their computational cost rises rapidly with the number of channels. Copula Granger causality can also be used reliably, but its computational cost rises rapidly with the number of data.
Detecting synchrony in EEG: A comparative study of functional connectivity measures.
In neuroscience, there is considerable current interest in investigating the connections between different parts of the brain. EEG is one modality for examining brain function, with advantages such as high temporal resolution and low cost. Many measures of connectivity have been proposed, but which is the best measure to use? In this paper, we address part of this question: which measure is best able to detect connections that do exist, in the challenging situation of non-stationary and noisy data from nonlinear systems, like EEG. This requires knowledge of the true relationship between signals, hence we compare 26 measures of functional connectivity on simulated data (unidirectionally coupled Hénon maps, and simulated EEG). To determine whether synchrony is detected, surrogate data were generated and analysed, and a threshold determined from the surrogate ensemble. No measure performed best in all tested situations. The correlation and coherence measures performed best on stationary data with many samples. S-estimator, correntropy, mean-phase coherence (Hilbert), mutual information (kernel), nonlinear interdependence (S) and nonlinear interdependence (N) performed most reliably on non-stationary data with small to medium window sizes. Of these, correlation and S-estimator have execution times that scale slower with the number of channels and the number of samples.
Towards a brain-controlled wheelchair prototype
Copyright 2010 ACM. In this project, a design for a non-invasive, EEG-based brain-controlled wheelchair has been developed for use by completely paralyzed patients. The proposed design includes a novel approach for selecting optimal electrode positions, a series of signal processing algorithms and an interface to a powered wheelchair. In addition, a 3D virtual environment has been implemented for training, evaluating and testing the system prior to establishing the wheelchair interface. Simulation of a virtual scenario replicating the real world gives subjects an opportunity to become familiar with operating the device prior to engaging the wheelchair.
APOE-ε4 Genotype and Dementia Before and After Transient Ischemic Attack and Stroke: Population-Based Cohort Study.
Background and Purpose- APOE-ε4 genotype is a risk factor for sporadic Alzheimer disease and reduced recovery from brain injury. Since data on APOE genotype and dementia associated with transient ischemic attack/stroke are sparse, we determined the associations in a longitudinal population-based cohort. Methods- All patients with transient ischemic attack or stroke (2002-2012) in a defined population of 92 728 OxVASC (Oxford Vascular Study) had follow-up to 5-years. Pre-event and incident postevent dementia were ascertained through direct patient assessment and follow-up, supplemented by review of hospital/primary care records. Associations between pre- and post-event dementia and APOE genotype (ε4/ε4-homozygous and ε4/ε3-heterozygous versus ε3/ε3) were examined using logistic regression and Cox regression models, respectively, adjusted for age, sex, education, cerebrovascular burden (stroke severity, prior stroke, white matter disease), diabetes mellitus, and dysphasia. Results- Among 1767 genotyped patients (mean/SD age, 73.0/13.0 years, 901 [51%] male, 602 [34%] transient ischemic attack), 1058 (59.9%) were APOE-ε3/ε3, 403 (22.8%) were ε4/ε3 and 30 (1.7%) were ε4-homozygous. Homozygosity was associated with both pre-event (adjusted odds ratio, 5.81 [95% CI, 1.93-17.48]; P=0.002) and postevent dementia (adjusted hazard ratio, 3.64 [95% CI, 1.90-7.00]; P<0.0001). Association with postevent dementia was maintained after further adjustment for baseline cognitive impairment (hazard ratio, 2.41 [95% CI, 1.19-4.89]; P=0.01). There were no associations overall between ε4/ε3 and pre-event dementia (adjusted odds ratio, 1.47 [95% CI, 0.88-2.45]; P=0.14) or postevent dementia (hazard ratio, 1.11 [95% CI, 0.84-1.48]; P=0.47). Conclusions- In patients with transient ischemic attack and stroke, APOE-ε4 homozygosity was associated with both pre- and post-event dementia. Associations were independent of cerebrovascular burden and may be mediated through increased neurodegenerative pathology or vulnerability to injury.