Functional Near-Infrared Spectroscopy

Functional Near-Infrared Spectroscopy (fNIRS): Unveiling the Brain's Secrets

Functional Near-Infrared Spectroscopy (fNIRS) is a powerful non-invasive neuroimaging technique that has gained prominence in recent years for its ability to measure brain activity by monitoring changes in blood oxygenation and hemodynamics. This versatile technology holds immense promise in various fields, including neuroscience, psychology, rehabilitation, and human-computer interaction. This article by Academic Block explores the principles, applications, advantages, and challenges associated with fNIRS, shedding light on its growing significance in understanding the complexities of the human brain.

Principles of Functional Near-Infrared Spectroscopy

Optical Measurement of Hemodynamics

Functional Near-Infrared Spectroscopy relies on the principles of spectroscopy to detect changes in blood oxygenation levels in the brain. The technique measures the absorption of near-infrared light by oxygenated and deoxygenated hemoglobin. Near-infrared light, with wavelengths between 650 and 950 nanometers, penetrates biological tissues, allowing for non-invasive monitoring.

Hemodynamic Response

When a specific brain region becomes active, there is an increase in blood flow to that area, leading to changes in the concentrations of oxygenated and deoxygenated hemoglobin. These changes, known as the hemodynamic response, serve as an indirect indicator of neural activity. fNIRS captures these alterations in hemoglobin concentrations, providing insights into brain function.


fNIRS systems typically consist of a light source, usually a laser or light-emitting diodes (LEDs), and detectors. The emitted light traverses the scalp, skull, and brain tissues before reaching the detectors. By analyzing the attenuation of light at different wavelengths, fNIRS systems can calculate the concentrations of oxygenated and deoxygenated hemoglobin, allowing researchers to map brain activity.

Applications of fNIRS

Cognitive Neuroscience

fNIRS has become an invaluable tool in cognitive neuroscience research. It allows scientists to investigate brain activity during various cognitive tasks, such as memory, attention, language processing, and decision-making. The technique provides a non-invasive means of studying the neural basis of human cognition.

Clinical Research and Diagnosis

In clinical settings, fNIRS is employed for studying neurological disorders, including but not limited to epilepsy, stroke, and neurodegenerative diseases. The technology aids in identifying abnormal brain activity and assessing the efficacy of therapeutic interventions. Additionally, fNIRS has potential applications in diagnosing conditions like traumatic brain injury and monitoring cerebral oxygenation in neonates.

Human-Computer Interaction

fNIRS is finding applications in the field of human-computer interaction (HCI), enabling the development of brain-computer interfaces (BCIs). BCIs use brain activity to control external devices, offering new possibilities for individuals with motor disabilities. fNIRS-based BCIs have been explored for applications ranging from communication devices to neurofeedback systems.


Rehabilitation programs benefit from fNIRS by providing insights into brain plasticity and recovery mechanisms. Monitoring brain activity during motor tasks helps therapists tailor interventions for individuals recovering from stroke or other neurological injuries. fNIRS also aids in understanding the neural processes underlying motor learning and adaptation.

Psychological Research

Psychologists use fNIRS to investigate emotional and social processes in the brain. Studying neural responses to stimuli associated with emotions helps researchers unravel the intricacies of human behavior and subjective experiences. fNIRS complements traditional psychological methods, offering a neurobiological perspective on mental processes.

Advantages of fNIRS


One of the key advantages of fNIRS is its non-invasiveness. Unlike some other neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), fNIRS does not require exposure to ionizing radiation or the use of contrast agents. This makes it a safer option for longitudinal studies and research involving vulnerable populations.


fNIRS systems are generally compact and portable, allowing for flexibility in experimental setups. Researchers can conduct experiments in naturalistic settings, making it easier to study real-world scenarios. The portability of fNIRS systems has also facilitated its use in clinical environments and field studies.

Temporal Resolution

fNIRS offers a relatively high temporal resolution, capturing changes in hemodynamic response on the order of seconds. This temporal precision is advantageous for studying dynamic cognitive processes and temporal patterns of neural activity. It complements other imaging techniques that might have higher spatial resolution but slower temporal dynamics.

Tolerance to Motion

Compared to fMRI, fNIRS is less sensitive to motion artifacts. This makes it particularly suitable for studying populations that may have difficulty remaining still during data acquisition, such as infants, children, or individuals with movement disorders. The robustness of fNIRS to motion artifacts enhances its applicability in diverse research settings.


In comparison to some neuroimaging technologies, fNIRS systems are relatively cost-effective. The affordability of equipment and the lower associated operational costs make fNIRS an accessible option for researchers and clinicians, especially in resource-limited settings.

Mathematical equations behind the Functional Near-Infrared Spectroscopy

The mathematical equations behind Functional Near-Infrared Spectroscopy (fNIRS) involve principles from spectroscopy, light attenuation, and the Beer-Lambert law. The basic concept is to measure the changes in the absorption of near-infrared light as it passes through biological tissues, specifically the brain, to infer changes in hemoglobin concentration and, consequently, neural activity. Below are the fundamental equations and concepts involved:

1. Beer-Lambert Law:

The Beer-Lambert law describes the relationship between the absorption of light and the concentration of the absorbing substance. In the context of fNIRS, the concentration of interest is the concentration of oxygenated (HbO2) and deoxygenated (Hb) hemoglobin. The law is expressed as follows:

A = ε ⋅ c ⋅ l ;


  • A is the absorbance,
  • ε is the molar absorptivity (extinction coefficient),
  • c is the concentration of the absorbing substance,
  • l is the path length of the light through the medium.

2. Light Transport Equation:

The light transport equation describes how light propagates through a medium, considering absorption and scattering. In the context of fNIRS, the light transport equation is often simplified due to the highly scattering nature of biological tissues. The equation can be written as:

I(x) = I0 ⋅ e−μx ;


  • I(x) is the intensity of light at a depth x,
  • I0 is the initial intensity of the incident light,
  • μ is the absorption coefficient.

3. Change in Optical Density:

The change in optical density (ΔOD) is a key parameter in fNIRS, representing the change in light absorption due to changes in hemoglobin concentration. It is related to the change in intensity (ΔI) by:

ΔOD = −log⁡(I / I0) ;

4. Modified Beer-Lambert Law for fNIRS:

The Modified Beer-Lambert Law for fNIRS relates the change in optical density to changes in hemoglobin concentration:

ΔOD = εHbO2 ⋅ ΔcHbO2 ⋅ l + εHb ⋅ ΔcHb ⋅ l ;


  • ΔOD is the change in optical density,
  • εHbO2 and εHb are the molar absorptivities for oxygenated and deoxygenated hemoglobin,
  • ΔcHbO2 and ΔcHb are the changes in concentrations of oxygenated and deoxygenated hemoglobin, respectively,
  • l is the path length of the light through the medium.

5. Conversion to Changes in Hemoglobin Concentration:

The change in optical density (ΔOD) is often converted to changes in hemoglobin concentration using the differential pathlength factor (DPF):

Δc = ΔOD / (ε ⋅ DPF) ;


  • Δc is the change in hemoglobin concentration,
  • ε is the molar absorptivity,
  • DPF is the differential pathlength factor.

These equations represent the basic principles behind the mathematical modeling of fNIRS signals. The analysis of fNIRS data often involves sophisticated signal processing techniques and the consideration of factors such as scalp and skull contributions, individual variability, and motion artifacts. Advanced mathematical methods, including signal deconvolution and statistical analyses, are employed to extract meaningful information about neural activity from fNIRS measurements.

Challenges and Considerations

Limited Spatial Resolution: One of the main challenges of fNIRS is its limited spatial resolution compared to techniques like fMRI. While fNIRS can provide information about the general location of brain activity, it may struggle to pinpoint activity at the level of individual neurons or specific brain nuclei.

Shallow Penetration Depth: The penetration depth of near-infrared light is restricted, limiting fNIRS to the outer layers of the cortex. Deep brain structures are challenging to access, which may hinder the examination of certain brain functions and restrict the scope of fNIRS applications.

Signal Contamination: fNIRS signals can be contaminated by various sources, including scalp and skull hemodynamics. Advanced signal processing techniques are employed to minimize these contaminations, but they add complexity to data analysis. Researchers must carefully interpret fNIRS data, considering potential artifacts and confounding factors.

Individual Variability: Inter-subject variability in skull thickness, scalp composition, and other anatomical factors can affect the accuracy of fNIRS measurements. Researchers need to account for these individual differences to ensure the reliability and validity of their findings.

Integration with Other Modalities: While fNIRS provides valuable information about hemodynamic changes, integrating it with other imaging modalities, such as EEG or fMRI, can offer a more comprehensive understanding of brain activity. However, the synchronization of different data streams poses technical challenges that researchers continue to address.

Future Directions and Innovations

Multimodal Imaging Integration

The integration of fNIRS with other neuroimaging modalities is a promising avenue for future research. Combining fNIRS with techniques like EEG, fMRI, or even magnetoencephalography (MEG) can provide complementary information, enhancing the spatial and temporal resolution of the overall neuroimaging approach.

Advancements in Wearable Technology

Ongoing efforts focus on developing wearable fNIRS devices that allow for continuous monitoring of brain activity in real-world settings. These portable systems have the potential to revolutionize applications in neuroergonomics, sports science, and clinical monitoring.

Machine Learning Applications

Machine learning algorithms are increasingly being applied to fNIRS data analysis. These algorithms can aid in feature extraction, pattern recognition, and the interpretation of complex neural dynamics. The synergy between fNIRS and machine learning holds promise for enhancing the accuracy and efficiency of brain activity classification.

Clinical Translation

As research continues to validate the utility of fNIRS in clinical settings, its integration into routine diagnostics and therapeutic monitoring is on the horizon. The development of standardized protocols and the establishment of normative databases will contribute to the clinical translation of fNIRS technology.

Final Words

Functional Near-Infrared Spectroscopy has emerged as a valuable tool in the realm of neuroimaging, offering a non-invasive and portable means of studying brain function. From unraveling the mysteries of cognition to aiding in clinical diagnosis and rehabilitation, fNIRS has demonstrated its versatility across diverse applications. In this article by Academic Block we have seen that, while challenges such as limited spatial resolution and potential signal contamination persist, ongoing research and technological innovations continue to enhance the capabilities of this promising neuroimaging technique. As we journey deeper into the complexities of the human brain, fNIRS stands as a beacon, illuminating the pathways to a more profound understanding of neural processes and behavior. Please provide your comments below, it will help us in improving this article. Thanks for reading!

Key Contributors for Functional Near-Infrared Spectroscopy

  1. David Boas: David Boas is a prominent figure in the field of biomedical optics and neuroimaging. He has made substantial contributions to the development and advancement of fNIRS technology. Boas has played a key role in promoting the use of fNIRS for studying brain function, and his work has had a significant impact on the field.

  2. Enrico Gratton: Enrico Gratton is another researcher who has made significant contributions to the field of fNIRS. His work has focused on the development of novel optical imaging techniques, including the application of near-infrared spectroscopy to study brain activity. Gratton’s contributions have helped establish fNIRS as a valuable tool in neuroscience research.

Hardware and software required for Functional Near-Infrared Spectroscopy

Hardware Components:

1. fNIRS Instrumentation:

  • fNIRS Probes: Optodes (light emitters and detectors) arranged on flexible cables or caps to be placed on the scalp.
  • Light Sources: Light-emitting diodes (LEDs) or lasers emitting near-infrared light.
  • Detectors: Photodiodes or photomultiplier tubes to measure the transmitted or reflected light.
  • Connectors and Cables: Ensure proper connection between the light sources, detectors, and data acquisition system.

2. fNIRS Systems:

  • fNIRS Devices: Commercially available systems such as those from NIRx, Hitachi, or Artinis.
  • Amplifiers: Amplify the weak optical signals obtained from the detectors.

3. Headgear:

  • Optode Holders: Securely hold the optodes in place on the participant’s scalp.
  • Head Caps: Flexible caps or headbands with integrated optode holders for consistent optode placement.

4. Positioning Systems:

  • 3D Digitizers: Helps in precisely map the optode positions onto the participant’s head anatomy. Also, facilitates co-registration with other neuroimaging modalities.

5. Computer Hardware:

  • Computers: High-performance computers for real-time data acquisition and processing.

6. Triggering and Synchronization Devices:

  • Trigger Boxes: Synchronize fNIRS data acquisition with external events (e.g., stimulus presentation).
  • External Input/Output Interfaces: Connect fNIRS systems to other devices or software for synchronization.

7. Recording Devices:

  • Data Storage: Hard drives or solid-state drives for storing raw and processed fNIRS data.
  • Backup Systems: Ensure data integrity and prevent data loss.

Software Components:

1. Data Acquisition Software:

  • Control Software: Interface to control fNIRS system settings, such as acquisition parameters. It monitor real-time data during experiments.

2. Signal Processing Software:

  • Pre-processing Tools: Remove noise, motion artifacts, and baseline drift.
  • Filtering Algorithms: Butterworth or wavelet filters to enhance signal quality.
  • Segmentation Tools: Divide data into epochs for analysis.

3. Analysis Software:

  • Statistical Analysis Packages: MATLAB, Python (with libraries like SciPy and StatsModels), or specialized fNIRS analysis software. Mainly to perform statistical analyses to identfy regions of interest and assess experimental effects.

4. Visualization Tools:

  • Brain Mapping Software: Neuroimaging software like BrainVision Analyzer, NIRS-SPM, or NIRS Brain AnalyzIR for spatial visualization. It generate activation maps based on fNIRS data.

5. Integration Software:

  • Interfaces for Multimodal Integration: Software that facilitates integration with other neuroimaging modalities like EEG or fMRI. It allows for a more comprehensive understanding of brain activity.

6. Reporting Tools:

  • Data Reporting Software: Tools for generating reports and visualizations of study results.

7. Programming Environments:

  • Scripting Languages: MATLAB and Python for custom analysis scripts and algorithm development.

8. User Interface Software:

  • Experiment Design Software: Tools for designing and presenting experimental stimuli.

Facts on Functional Near-Infrared Spectroscopy

Principle of Measurement: fNIRS measures changes in the concentration of oxygenated and deoxygenated hemoglobin in the brain. It is based on the principle that near-infrared light can penetrate biological tissues, allowing the detection of changes in hemodynamics associated with neural activity.

Near-Infrared Light: fNIRS uses near-infrared light with wavelengths typically between 650 and 950 nanometers. This range allows for sufficient penetration through the scalp and skull to reach the cerebral cortex.

Hemodynamic Response: When a brain region becomes active, there is an increase in blood flow to that area, leading to changes in the concentrations of oxygenated and deoxygenated hemoglobin. fNIRS detects these changes, known as the hemodynamic response, as an indirect measure of neural activity.

Non-Invasiveness: One of the major advantages of fNIRS is its non-invasive nature. Unlike some other neuroimaging techniques, fNIRS does not involve ionizing radiation, making it safe for repeated measurements and applicable in various populations, including infants and children.

Portability: fNIRS systems are generally compact and portable. This portability allows researchers to conduct experiments in naturalistic settings, making it a valuable tool for studies outside traditional laboratory environments.

Temporal Resolution: fNIRS provides a relatively high temporal resolution, capturing changes in hemodynamic response on the order of seconds. While its spatial resolution is more limited compared to some other techniques, its temporal precision is suitable for studying dynamic cognitive processes.

Tolerance to Motion: Compared to techniques like functional magnetic resonance imaging (fMRI), fNIRS is less sensitive to motion artifacts. This makes it suitable for studying populations that may have difficulty remaining still during data acquisition, such as children or individuals with movement disorders.

Applications in Cognitive Neuroscience: fNIRS has been extensively used in cognitive neuroscience research to study various cognitive processes, including memory, attention, language processing, decision-making, and motor control. It provides valuable insights into the neural basis of human cognition.

Clinical Applications: In clinical settings, fNIRS has applications in studying and diagnosing neurological disorders, monitoring cerebral oxygenation in neonates, and assessing brain function in individuals with conditions such as stroke, traumatic brain injury, and neurodegenerative diseases.

Brain-Computer Interfaces (BCIs): fNIRS has been integrated into the development of BCIs, allowing individuals to control external devices using their brain activity. This has promising applications in assistive technology and neurorehabilitation.

Neurofeedback: fNIRS is used in neurofeedback studies, enabling individuals to learn to self-regulate their brain activity. This has implications for mental health interventions and cognitive training.

Affordability: Compared to some other neuroimaging technologies, fNIRS systems are relatively cost-effective. This affordability enhances accessibility, particularly in research and clinical settings with budget constraints.

Multimodal Imaging Integration: Researchers often combine fNIRS with other neuroimaging modalities, such as electroencephalography (EEG) or fMRI, to obtain a more comprehensive understanding of brain activity. This multimodal approach can provide complementary information.

Key Discoveries using Functional Near-Infrared Spectroscopy

Functional Near-Infrared Spectroscopy (fNIRS) has been a valuable tool in various fields, leading to several key discoveries in neuroscience, psychology, clinical research, and other areas. Here are some notable discoveries and findings enabled by fNIRS:

  1. Localization of Brain Activation: fNIRS has been used to map and localize brain activation during various cognitive tasks. This includes the identification of regions associated with language processing, memory, attention, and motor control.
  2. Cortical Activation in Infants: Studies using fNIRS have provided insights into the development of cortical activation in infants. Researchers have examined how neural responses to visual and auditory stimuli evolve during the early stages of life.
  3. Brain-Computer Interface (BCI) Applications: fNIRS has played a crucial role in the development of BCIs, allowing individuals to control external devices using their brain activity. This has implications for assistive technology, rehabilitation, and communication for individuals with motor disabilities.
  4. Neurofeedback and Cognitive Training: fNIRS has been employed in neurofeedback studies, enabling individuals to learn to regulate their own brain activity. This has applications in cognitive training, stress reduction, and the treatment of conditions such as attention-deficit/hyperactivity disorder (ADHD).
  5. Understanding Social Interactions: Researchers have utilized fNIRS to investigate neural responses during social interactions. Studies have explored brain activation patterns related to empathy, cooperation, and theory of mind, providing insights into the neural basis of social cognition.
  6. Clinical Applications in Psychiatry: fNIRS has been applied in psychiatry to study brain function in individuals with various mental health disorders. Research has focused on conditions such as depression, anxiety, and schizophrenia, contributing to our understanding of the neural mechanisms underlying these disorders.
  7. Assessment of Pain Perception: Studies have used fNIRS to assess and measure neural responses associated with pain perception. This has implications for understanding pain processing in various populations, including individuals with chronic pain conditions.
  8. Monitoring Cognitive Load: fNIRS has been employed to monitor cognitive load in real-time, providing insights into mental workload during tasks such as driving, learning, and decision-making. This has applications in human factors research and the design of user-friendly interfaces.
  9. Rehabilitation and Motor Learning: fNIRS has been utilized in rehabilitation settings to study brain activation patterns during motor tasks. Understanding neural plasticity and motor learning is essential for designing effective rehabilitation programs for individuals recovering from stroke or other motor impairments.
  10. Assessment of Brain Hemodynamics in Neonates: fNIRS has been used to assess and monitor cerebral oxygenation and hemodynamics in neonates. This is particularly valuable in understanding brain function in preterm infants and those at risk of neurological complications.
  11. Contribution to Basic Neuroscience Research: fNIRS has contributed to basic neuroscience research by providing a non-invasive means of studying brain function in humans. It has complemented other neuroimaging techniques, offering insights into the dynamics of neural activity in different cognitive processes.

Academic References on Functional Near-Infrared Spectroscopy

  1. Hoshi, Y., & Tamura, M. (1993). Detection of dynamic changes in cerebral oxygenation coupled to neuronal function during mental work in man. Neuroscience Letters, 150(1), 5–8.
  2. Villringer, A., Planck, J., Hock, C., Schleinkofer, L., & Dirnagl, U. (1993). Near infrared spectroscopy (NIRS): A new tool to study hemodynamic changes during activation of brain function in human adults. Neuroscience Letters, 154(1–2), 101–104.
  3. Ferrari, M., & Quaresima, V. (2012). A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application. NeuroImage, 63(2), 921–935.
  4. Boas, D. A., Elwell, C. E., Ferrari, M., & Taga, G. (2014). Twenty years of functional near-infrared spectroscopy: Introduction for the special issue. NeuroImage, 85(Part 1), 1–5.
  5. Scholkmann, F., Kleiser, S., Metz, A. J., Zimmermann, R., Mata Pavia, J., Wolf, U., … & Wolf, M. (2014). A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. NeuroImage, 85(Part 1), 6–27.
  6. Ferrari, M., & Quaresima, V. (2016). Functional near infrared spectroscopy (fNIRS) application in cognitive neuroscience and neuropsychology. Cognitive Processing, 17(3), 163–166.
  7. Quaresima, V., Bisconti, S., & Ferrari, M. (2012). A brief review on the use of functional near-infrared spectroscopy (fNIRS) for language imaging studies in human newborns and adults. Brain and Language, 121(2), 79–89.
  8. Schroeter, M. L., Zysset, S., Kupka, T., Kruggel, F., & Yves von Cramon, D. (2002). Near-infrared spectroscopy can detect brain activity during a color-word matching Stroop task in an event-related design. Human Brain Mapping, 17(1), 61–71.
  9. Cooper, R. J., Selb, J., Gagnon, L., Phillip, D., Schytz, H. W., Iversen, H. K., … & Boas, D. A. (2012). A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy. Frontiers in Neuroscience, 6, 147.
  10. Funane, T., Atsumori, H., Katura, T., Obata, A., Sato, H., Tanikawa, Y., … & Kiguchi, M. (2014). Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis. NeuroImage, 85(Part 1), 150–165.
  11. Pinti, P., Tachtsidis, I., Hamilton, A., Hirsch, J., Aichelburg, C., Gilbert, S., … & Burgess, P. W. (2017). The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience. Annals of the New York Academy of Sciences, 1405(1), 5–29.
  12. Scholkmann, F., Holper, L., Wolf, M., & Wolf, U. (2013). A new methodical approach in neuroscience: Assessing inter-personal brain coupling using functional near-infrared imaging (fNIRI) hyperscanning. Frontiers in Human Neuroscience, 7, 813.
  13. Lloyd-Fox, S., Blasi, A., & Elwell, C. E. (2010). Illuminating the developing brain: The past, present and future of functional near infrared spectroscopy. Neuroscience & Biobehavioral Reviews, 34(3), 269–284.
  14. Haeussinger, F. B., Heinzel, S., Hahn, T., Schecklmann, M., Ehlis, A. C., & Fallgatter, A. J. (2011). Simulation of near-infrared light absorption considering individual head and prefrontal cortex anatomy: Implications for optical neuroimaging. PloS One, 6(10), e26377.
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