Nirslab software

Manufactured by NIRx Medical Technologies

NIRSlab software is a data acquisition and analysis tool for near-infrared spectroscopy (NIRS) research. The software provides functionality to record, visualize, and analyze NIRS data from NIRx medical devices. It supports real-time data processing and offers various data analysis features.

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Lab products found in correlation

Matlab 2017, by MathWorks (1 mentions) Nirscout, by NIRx Medical Technologies (1 mentions)

4 protocols using nirslab software

Visual Responses to Radial Checkerboard in Children

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To confirm that the baseline movie and its contrast do not affect the emergence of visual responses to the radial checkerboard in children, we measured hemodynamic signals in response to 2 different blended RS-animated cartoons freely decided by the subject: cartoon 1 was presented at both low (20%, L1) and high (80%, H1) contrast, while only low contrast was recorded for cartoon 2 (L2; Fig. S1). In this case, the presentation order was decided by the child, in order to maximize subject compliance.
During the experimental sessions, data were quickly analyzed and visualized using nirsLAB software (NIRx Medical Technologies LLC, v2019.4).

Mazziotti R., Scaffei E., Conti E., Marchi V., Rizzi R., Cioni G., Battini R, & Baroncelli L. (2022). The amplitude of fNIRS hemodynamic response in the visual cortex unmasks autistic traits in typically developing children. Translational Psychiatry, 12, 53.

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Finger Tapping Task Analysis using fNIRS

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We used the NIRSlab Software (v2014.12; NIRx Medical Technologies LLC) to process raw data. The raw fNIRS signal was digitally bandpass-filtered offline at 0.01-0.3 Hz. Moreover, the optical density data were converted into relative oxyhemoglobin concentration variations, according to modified Beer-Lambert Law [20 (link)]. To evaluate the cerebral regions of interest, we considered the oxyhemoglobin concentration variation, in accordance with several experimental studies, which indicate this parameter as the most sensitive for the assessment of the cortical metabolism during finger tapping task execution [21 (link), 22 ]. We thus considered the difference between the mean values of oxyhemoglobin concentrations in the resting state and finger tapping tasks, divided for the standard deviation of mean baseline values [23 (link)]. For each subject and each recording channel, the effect size (Cohen's d) was calculated.
The speed during fast finger tapping task was calculated through the number of clicks per second by manual computation.

Gentile E., Ricci K., Delussi M., Brighina F, & de Tommaso M. (2019). Motor Cortex Function in Fibromyalgia: A Study by Functional Near-Infrared Spectroscopy. Pain Research and Treatment, 2019, 2623161.

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fNIRS Signals Acquisition and Analysis

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The signals generated in the brain were sampled at a frequency of 7.81 Hz. A single-phase continuous-wave fNIRS system NIRScout (NIRx Medical Technologies, New York, NY, USA) was used for the acquisition of fNIRS signals. The experiments were performed by the medical doctors and staff in Dunsan Korean Medicine Hospital, Daejeon University. Subsequently, the data analyses were done by the researchers in Pusan National University, Republic of Korea. Two different wavelengths were utilized by the system: 760 and 850 nm. The Modified Beer-Lambert’s law was used to convert the raw intensities into changes in oxy-hemoglobin (ΔHbO) and deoxy-hemoglobin (ΔHbR) (Kocsis et al., 2006). This conversion was performed using the NIRSlab software (NIRx Medical Technologies).
After the data were converted into ΔHbO and ΔHbR, they were preprocessed to remove the effects of physiological noises. For this, a 4^th -order Butterworth filter was utilized. A low-pass cutoff frequency of 0.15 Hz was used to remove cardiac, respiratory, and low-frequency drift signals (Fekete et al., 2011; Kainerstorfer et al., 2015). The cutoff frequency for the high-pass filter was selected according to the longest period of a single trial (i.e., 38 seconds (1/38 seconds = 0.026 Hz)) (Pinti et al., 2018; Zafar and Hong, 2018). For analysis, the software MATLAB 2017 was used (MathWorks, Naticks, MA, USA).

Khan M.N., Ghafoor U., Yoo H.R, & Hong K.S. (2022). Acupuncture enhances brain function in patients with mild cognitive impairment: evidence from a functional-near infrared spectroscopy study. Neural Regeneration Research, 17(8), 1850-1856.

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fNIRS Data Pre-processing and Analysis

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fNIRS data were first pre-processed using NIRSlab software (NIRx Medical Technologies LLC, v2016.01, Windows 64 bit).
For each subject, channels that presented a gain greater than 8 and coefficient of variation greater than 7.5 were excluded from the analysis, as these characteristics are associated with high signal noise^{76 (link)–78 (link)}. As a consequence of the automatic rejection of channels with high signal noise, a different number of couples was available for analyzing each channel and condition. Spike artifacts, which are signal components with an abnormal change in amplitude, normally produced by head movements, were replaced with the nearest surrounding signals⁷⁹, and discontinuities in the signals, if any, were corrected using the remove_discontinuities function on NIRSlab. Finally, a band-pass filter of 0.01–0.2 Hz was applied to eliminate baseline shift variations, and hemoglobin concentrations were determined using the modified Beer-Lambert law. Finally, the signal was visually inspected by two independent experts for validation.
NIRS time-series of oxygenated haemoglobin (oxy-Hb) for each subject, condition, stimulus, and channel were exported from NIRSlab to be analysed.

Azhari A., Lim M., Bizzego A., Gabrieli G., Bornstein M.H, & Esposito G. (2020). Physical presence of spouse enhances brain-to-brain synchrony in co-parenting couples. Scientific Reports, 10, 7569.

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