Functional near-infrared spectroscopy (fNIRS) is a noninvasive, easy-to-use, and portable brain imaging technology that enables studies of normal brain function and alterations that arise in disease, both in the laboratory as well as in real-world settings.^{1 – 4} In 1977, Jöbsis used the technique for the first time to noninvasively assess changes in human brain oxygenation due to hyperventilation.⁵ Since then, the tool has evolved into an established noninvasive brain imaging modality and has been applied to a wide range of different populations and research questions.

This review celebrates the 30th anniversary of the first in vivo near-infrared (NIR) spectroscopy (NIRS) publication, which was authored by Professor Frans Jöbsis. At first, NIRS was utilized to experimentally and clinically investigate cerebral oxygenation. Later it was applied to study muscle oxidative metabolism. Since 1993, the discovery that the functional activation of the human cerebral cortex can be explored by NIRS has added a new dimension to the research. To obtain simultaneous multiple and localized information, a further major step forward was achieved by introducing NIR imaging (NIRI) and tomography. This review reports on the progress of the NIRS and NIRI instrumentation for brain and muscle clinical applications 30 years after the discovery of in vivo NIRS. The review summarizes the measurable parameters in relation to the different techniques, the main characteristics of the prototypes under development, and the present commercially available NIRS and NIRI instrumentation. Moreover, it discusses strengths and limitations and gives an outlook into the "bright" future.

We present a new method for automatic detection of peaks in noisy periodic and quasi-periodic signals. The new method, called automatic multiscale-based peak detection (AMPD), is based on the calculation and analysis of the local maxima scalogram, a matrix comprising the scale-dependent occurrences of local maxima. The usefulness of the proposed method is shown by applying the AMPD algorithm to simulated and real-world signals.

Since the first demonstration of how to simultaneously measure brain activity using functional magnetic resonance imaging (fMRI) on two subjects about 10 years ago, a new paradigm in neuroscience is emerging: the assessment of the inter-brain coupling between two or more subjects, termed “hyperscanning”. The hyperscanning approach has the potential to enable a new view on how the brain works and will reveal as yet undiscovered brain functions based on brain-to-brain coupling, since the single-subject setting cannot capture them. In particular, functional near-infrared imaging (fNIRI) hyperscanning is a promising new method, offering a cost-effective, easy to apply and reliable technology to measure inter-personal interactions in a natural context. In this short review we report on fNIRI hyperscanning studies published so far and summarize opportunities and challenges for future studies.

For brain computer interfaces (BCIs), which may be valuable in neurorehabilitation, brain signals derived from mental activation can be monitored by non-invasive methods, such as functional near-infrared spectroscopy (fNIRS). Single-trial classification is important for this purpose and this was the aim of the presented study. In particular, we aimed to investigate a combined approach: 1) offline single-trial classification of brain signals derived from a novel wireless fNIRS instrument; 2) to use motor imagery (MI) as mental task thereby discriminating between MI signals in response to different tasks complexities, i.e. simple and complex MI tasks.

Functional near-infrared spectroscopy (fNIRS) enables the noninvasive measurement of human brain activity by monitoring concentration changes of oxygenated hemoglobin (${\mathrm{O}}_2\mathrm{Hb}$) and deoxygenated hemoglobin (HHb) in the blood.^{1 – 4} fNIRS has evolved from a tool for basic research to a widely used technique to investigate brain activity in nonconstrained environments.^5,6 Despite its versatile use, there remain several challenges, in particular, the sensitivity of continuous-wave fNIRS to hemodynamic changes of non-neuronal origin.^{2,7 – 10} These are often referred to as physiological “noise” or “interference” and include systemic activities, such as cardiac pulsation (1 to 2 Hz), respiration (0.2 to 0.4 Hz), low-frequency oscillations ($\sim 0.1\mathrm{Hz}$) and very low-frequency oscillations (0.01 to 0.05 Hz),¹¹ and an increase in blood flow through sympathetic nervous activity.¹² These artifacts generate signal changes that may mimic or mask true task-evoked hemodynamic responses (HRs) and may lead to false positives or false negatives.^8,10,13 This challenge has been acknowledged and its significance recognized in the recent years by the fNIRS community.⁸ Although the susceptibility to non-neuronal signals is specific to the measurement principle of fNIRS, all technologies that infer brain activity via hemodynamic changes, i.e., fNIRS, functional magnetic resonance imaging, and positron emission tomography, are affected.