Explaining Variance
In fNIRS recordings, we primarily want to see the signal from the brain. In reality, we also measure non-evoked, systemic activity from the surrounding tissue too (Tachtsidis and Scholkmann, 2016). You can regress out this noise using data from short-channels and physiological sensors. These sensors might include HR, BR, Tonic/Phasic Skin Conductance, Blood Pressure, SpO2 amongst others.
Multi-Modality
Being able to simultaneously measure several global body parameters could enrich your research with more data. It enables you to asses novel mechanisms and concurrent phenomena. A mobile device would allow for naturalistic experiments in complex real-world environments.
Some examples could be: emotional arousal in response to presented stimulus (dos Santos et al 2021), orthostatic hypotension and falls (Mol et al, 2020), cardiac output and respiration (Anh et al, 2016), or concurrent muscle activation (Ortega et al, 2020).
Introducing NIRxWINGS
The NIRxWINGS module for peripheral physiology measurements extends the NIRSport2. Signal processing algorithms can be optimized for artefact rejection (von Lühmann et al, 2019). It enables new experiments and amplifies your datasets with additional biosignal inputs.
Wireless data transfer allows the participant to move freely. Although the device stores data onboard, it also wirelessly streams for real-time display.
Mobililty
With NIRxWINGS, you are able to move outside the lab to conduct systemic physiology augmented fNIRS (SPA-fNIRS). You can access complex, dynamic and multi-sensory real-world environments.
Sensors
Pulse oximetry (PPG)
Heart-rate
Heart-rate variability (HRV),
Oxygen saturation (SpO2),
Respiration,
Temperature,
Galvanic skin response (GSR),
Bipolar signals such as EMG and ECG.
The physiology data from NIRxWINGS coupled with short-channels and motion sensors are highly effective in explaining the error variance in your fNIRS signal.
Check out this page to find more NIRxWINGS features.
Want to know more about this product? Send us an e-mail at support@nirx.net
References:
Tachtsidis, I., & Scholkmann, F. (2016). False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward. Neurophotonics, 3(3), 031405. https://doi.org/10.1117/1.NPh.3.3.031405
**Technical References:**
von Lühmann, A., Boukouvalas, Z., Müller, K. R., & Adalı, T. (2019). A new blind source separation framework for signal analysis and artifact rejection in functional near-infrared spectroscopy. NeuroImage, 200, 72-88. https://doi.org/10.1016/j.neuroimage.2019.06.021
von Lühmann, A., Li, X., Gilmore, N., Boas, D. A., & Yücel, M. A. (2020). Open Access Multimodal fNIRS Resting State Dataset With and Without Synthetic Hemodynamic Responses. Frontiers in Neuroscience, 14. https://doi.org/10.3389/fnins.2020.579353
**Use Cases**
Useful to identify user attention, stress, and vigilance, to detect and differentiate evoked systemic physiology in fNIRs.
Ahn, S., Nguyen, T., Jang, H., Kim, J. G., & Jun, S. C. (2016). Exploring neuro-physiological correlates of drivers' mental fatigue caused by sleep deprivation using simultaneous EEG, ECG, and fNIRS data. Frontiers in human neuroscience, 10, 219. https://doi.org/10.3389/fnhum.2016.00219
Ortega, P., Zhao, T., & Faisal, A. A. (2020). HYGRIP: Full-Stack Characterization of Neurobehavioral Signals (fNIRS, EEG, EMG, Force, and Breathing) During a Bimanual Grip Force Control Task. Frontiers in Neuroscience, 14. https://doi.org/10.3389/fnins.2020.00919
Mol, A., Maier, A. B., van Wezel, R. J., & Meskers, C. G. (2020). Multimodal monitoring of cardiovascular responses to postural changes. Frontiers in physiology, 11, 168. https://doi.org/10.3389/fphys.2020.00168
dos Santos, F. R., Bazán, P. R., Balardin, J. B., de Aratanha, M. A., Rodrigues, M., Lacerda, S., ... & Kozasa, E. H. (2021). Changes in Prefrontal fNIRS Activation and Heart Rate Variability During Self-Compassionate Thinking Related to Stressful Memories. Mindfulness, 1-13. https://doi.org/10.1007/s12671-021-01789-0