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Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Random and Systematic Errors01:20

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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Sources of variability in MEG.

Wanmei Ou1, Polina Golland, Matti Hämäläinen

  • 1Department of Computer Science and Artificial Intelligence Laboratory, MIT, USA.

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|November 30, 2007
PubMed
Summary

Inter-subject variability is the primary source of differences in magnetoencephalography (MEG) signals. Early somatosensory response timing is consistent, but magnitudes vary across sites, requiring modeling for data pooling.

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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

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Area of Science:

  • Neuroscience
  • Biophysics
  • Biomedical Engineering

Background:

  • Magnetoencephalography (MEG) is a crucial tool for studying brain activity.
  • Multi-site, multi-subject MEG studies face challenges due to signal variability.
  • Understanding variability sources is key for robust data analysis and interpretation.

Purpose of the Study:

  • To investigate and characterize sources of variability in MEG signals.
  • To identify strategies for comparing and pooling data across different experimental conditions, subjects, and sites.
  • To inform best practices for multi-site MEG research.

Main Methods:

  • Analysis of somatosensory MEG data from three distinct research sites.
  • Application of variance component analysis to quantify variability sources.
  • Utilized nonparametric KL divergence analysis for signal characterization.

Main Results:

  • Inter-subject differences were identified as the largest contributor to signal variability.
  • Early somatosensory response timing demonstrated high consistency across subjects and sites.
  • Deflection magnitudes exhibited greater variability across sites compared to timing.

Conclusions:

  • The consistency in early somatosensory response timing supports direct comparison of peak times across diverse datasets.
  • Modeling of deflection magnitude variability is essential for effective data pooling in multi-site MEG studies.
  • This research provides a foundation for more reliable cross-site and cross-subject MEG data integration.