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Related Concept Videos

Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

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When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...
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Applications of IR Spectroscopy: Overview01:11

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The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...
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IR Spectroscopy: Molecular Vibration Overview01:24

IR Spectroscopy: Molecular Vibration Overview

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When Infrared (IR) radiation passes through a covalently bonded molecule, the bonds transition from lower to higher vibrational levels. The fundamental vibrational motions that result in infrared absorption can be classified as stretching or bending vibrations.
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IR Spectrometers01:25

IR Spectrometers

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There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...
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IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration01:16

IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration

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A covalently bonded heteronuclear diatomic molecule can be modeled as two vibrating masses connected by a spring. The vibrational frequency of the bond can be expressed using an equation derived from Hooke's law, which describes how the force applied to stretch or compress a spring is proportional to the displacement of the spring. In this case, the atoms behave like masses, and the bond acts like a spring.
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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Related Experiment Video

Updated: Sep 4, 2025

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
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A General and Scalable Vision Framework for Functional Near-Infrared Spectroscopy Classification.

Zenghui Wang, Jun Zhang, Yi Xia

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |July 13, 2022
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    Summary

    This study introduces a novel framework to improve brain activity classification using functional near-infrared spectroscopy (fNIRS) by converting signals into images. This approach overcomes limitations in datasets and evaluation, enabling competitive performance with advanced computer vision models.

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    Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy
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    Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Computer Science

    Background:

    • Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical technique for monitoring brain activity, crucial for disease diagnosis and brain-computer interfaces (BCIs).
    • Deep learning for fNIRS classification faces challenges including limited datasets, ambiguous evaluation metrics, and domain barriers.
    • Existing methods often struggle with generalizability and efficient utilization of available data.

    Purpose of the Study:

    • To address the limitations in fNIRS classification, specifically limited datasets, evaluation criteria, and domain barriers.
    • To propose a general and scalable framework for fNIRS signal analysis by leveraging computer vision techniques.
    • To enhance the classification performance of fNIRS data by adapting state-of-the-art visual models.

    Main Methods:

    • Applied appropriate evaluation methods to three open-access fNIRS datasets to resolve issues with criteria and dataset size.
    • Developed a vision fNIRS framework converting multi-channel fNIRS signals into multi-channel virtual images using the Gramian angular difference field (GADF).
    • Trained state-of-the-art computer vision (CV) models using the proposed framework for rapid classification of fNIRS data.

    Main Results:

    • Visual models trained via the framework achieved competitive classification performance, comparable to the latest fNIRS-specific models.
    • Achieved high average classification accuracies of 78.68% for mental arithmetic and 73.92% for word generation tasks in cross-validation.
    • Demonstrated insignificant differences in performance for unilateral finger- and foot-tapping tasks compared to fNIRS models, as indicated by F1-score and kappa coefficient in subject-independent experiments.

    Conclusions:

    • The proposed vision fNIRS framework effectively addresses domain barriers in deep learning for fNIRS classification.
    • Leveraging computer vision models and sequence-to-image methods offers a promising direction for advancing fNIRS data analysis.
    • This approach facilitates the integration of advancements from the CV domain to improve fNIRS classification research and applications.