Spectral Analysis
Spectral Analysis - Related Videos
07:11
ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
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Cited by 1 •
2021
This protocol introduces Franck-Condon Lineshape Analyses (FCLSA) of emission spectra and serves as a tutorial for the use of ARL Spectral Fitting software. The open-source software provides an easy and intuitive way to perform advanced analysis of emission spectra including excited state energy calculations, CIE color coordinate determination, and FCLSA.
11:08
Analysis of Cell Suspensions Isolated from Solid Tissues by Spectral Flow Cytometry
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Cited by 4 •
2017
This article describes spectral cytometry, a new approach in flow cytometry that uses the shapes of emission spectra to distinguish fluorochromes. An algorithm replaces compensations and can treat auto-fluorescence as an independent parameter. This new approach allows for the proper analysis of cells isolated from solid organs.
10:28
High-plex Imaging using Spectral Confocal Microscopy to Minimize Non-specific Tissue Fluorescence
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Cited by 1 •
2025
Spectral Iterative Bleaching Extends Multiplexity (IBEX) builds upon the base IBEX technique by adding heparin blocking to minimize nonspecific binding and leveraging spectral detection with computational unmixing to suppress autofluorescence. This approach accelerates image acquisition while reducing sources of background, enabling robust multi-round, high-parameter spatial proteomic analyses.
04:16
Using Spectral Reflectometry to Determine Myelinated Axon Diameters in a Fixed Mouse Brain Slice
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2025
Source: Kwon, J., et al., Spectral Reflectometric Microscopy on Myelinated Axons In Situ. J. Vis. Exp. (2018) This video demonstrates the use of spectral reflectometry with a hyperspectral confocal microscope to analyze interference patterns and determine myelinated axon diameters in fixed brain tissue slices.
11:15
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Cited by 27 •
2013
Neuroimaging researchers typically consider the brain's response as the mean activity across repeated experimental trials and disregard signal variability over time as "noise". However, it is becoming clear that there is signal in that noise. This article describes the novel method of multiscale entropy for quantifying brain signal variability in the time domain.
FAQs
A power spectrum shows which frequencies contribute most strongly to an EEG or local field potential recording. Peaks or differences in frequency-specific power can indicate changes in rhythmic neural activity that may accompany sleep, attention, movement, disease, or sensory processing. This makes the spectrum useful for relating measurable signal features to changes in brain state or behavior.
The transformation reorganizes a neural recording according to frequency rather than the sequence of signal values over time. Fourier-based methods then make it possible to calculate power at different frequencies and compare oscillatory bands. This perspective helps investigators examine rhythmic structure that may be difficult to characterize from the time-domain waveform alone.
Comparing oscillatory bands allows researchers to determine whether neural activity changes selectively across frequency ranges rather than simply increasing or decreasing overall. Such comparisons can reveal frequency-specific patterns associated with sleep, attention, movement, sensory processing, or disease. The resulting profile provides a more detailed description of how brain dynamics vary across conditions.
A typical workflow begins with a recording in the time domain, such as EEG or a local field potential, followed by a Fourier-based transformation into the frequency domain. Researchers calculate the power spectrum and compare power across relevant frequencies or oscillatory bands. They can then relate those spectral measurements to experimental conditions, behavior, or neurological states.
This approach is useful when the research question concerns rhythmic brain activity or changes in neural dynamics across conditions. Applications described for neuroscience include studying sleep, attention, movement, disease, and sensory processing. It can also help connect signal-level measurements with cognition and behavior, making it relevant to both basic studies and investigations of neurological disorders.
Spectral measurements provide frequency-specific descriptions of neural activity that can be examined alongside relationships between brain signals. In this context, Spectral Analysis supports investigations of functional connectivity and helps researchers link brain dynamics with behavior, cognition, and neurological disorders. Its value lies in connecting measurable oscillatory patterns with broader functional or clinical phenomena.