Functional Near-Infrared Spectroscopy (fNIRS)
Functional near-infrared spectroscopy (fNIRS) is a non-invasive method for monitoring brain activity through changes in blood oxygenation. It estimates how concentrations of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) vary over time, offering an indirect view of neural activity in the outer layers of the cortex.
How fNIRS Works
Below is a video from the manufacturer of the equipment available in the infrastructure, explaining the basic principles of fNIRS and how the technology works.
fNIRS systems use optodes placed on the scalp:
- sources emit near-infrared light
- detectors measure the returning light
As the light moves through the scalp, skull, and cortex, it is absorbed and scattered. HbO and HbR absorb light differently at specific wavelengths. By measuring light at two or more wavelengths, the system can estimate changes in hemoglobin concentration. This process relies on the modified Beer–Lambert Law, which links changes in light intensity to chromophore concentration while accounting for scattering in tissue.
The Hemodynamic Signal
Neural activity increases local energy demand. In response, blood flow rises, leading to:
- an increase in HbO
- a decrease in HbR
This vascular response is known as neurovascular coupling. It appears after a delay of around 1–2 seconds and peaks roughly 4–6 seconds after activation. The signal is slower than EEG but provides a stable measure of sustained or evolving cognitive activity.
Hardware and Measurement Setups
Most devices used in HCI and neuroergonomics are continuous-wave systems. They:
- emit constant-intensity light
- measure relative concentration changes
- typically sample between 10–25 Hz
- use source–detector separations of about 3 cm
This configuration allows measurement of cortical activity up to about 1.5 cm deep. Many systems target the prefrontal cortex because it relates to working memory, attention, and mental workload. Portable and wearable systems enable studies outside controlled laboratory settings.
Noise and Signal Quality
The fNIRS signal is affected by several noise sources:
- instrumental noise (sensor stability, environmental light)
- motion artefacts from head or optode movement
- physiological rhythms such as heartbeat, breathing, and blood pressure waves
- superficial blood flow in the scalp
Common processing steps include:
- band-pass filtering to remove slow drifts and high-frequency noise
- motion correction methods such as TDDR or CBSI
- regression using short-separation channels when available
These steps help isolate cortical activity and improve the signal-to-noise ratio.
Strengths and Limitations
Strengths of fNIRS include:
- portability and wearable form factors
- tolerance to moderate motion
- suitability for interactive or naturalistic tasks
- measurement of both HbO and HbR
- safe, repeatable recordings
Limitations include:
- limited penetration depth (only cortical surface)
- slower hemodynamic signal compared to neural firing
- sensitivity to motion and physiological noise
- lower spatial resolution compared to fMRI
Despite these constraints, fNIRS offers a practical balance of data quality and usability for many research contexts.
Applications of fNIRS
fNIRS is widely used to study cognitive processes such as:
- working memory
- mental workload
- attention
- task engagement
- problem solving
Its portability and robustness make it useful for adaptive interfaces, workload monitoring, naturalistic studies, and passive BCIs where mental states are inferred without conscious control by the user.
