# Near-Infrared Spectroscopy Applied to Complex Systems and Human Hyperscanning Networking
## 6 Optical Imaging and Methods for fNIRS Hyperscanning Applications
Namely, potential recording biases due to the confounding influence of superficial blood perfusion might affect measured hemodynamic signals. To control for or to reduce such potential bias, specific signal processing and hardware expedients (e.g., spring-loaded sensors able to keep good sensor-skin contact or manipulation of the ideal source-detector distance) are being developed and tested. Further, as mentioned above, the technique only allows for an exploration of the activity of cortical structures, with no possibility to directly measure the contribution of subcortical structures. Such a limitation is structural and cannot be overcome. It then has to be taken into account when devising fNIRS-based investigations. Again, the technique is based on the measurement of hemodynamic responses and, thus, its ability to finely follow the subtlest and quickest modulations of neural activity is poorer with respect to hyperscanning applications based on electrophysiological techniques. Finally, it is important to acknowledge that even if fNIRS recordings are robust against motion artefacts, broad body movements (e.g., sudden postural changes) might still induce confounding events due to venous pooling or orthostatic hypotension [51]. Figure 2 reports a simplified schema of the main steps for fNIRS-based hyperscanning applications. The first crucial step is data recording. Apart from the setup of the recording system and the accurate placement of light sources and detectors thanks to adjustable caps or rubber bands, the most critical point is the design of an appropriate shared task or the definition of a significant naturalistic interaction. Given the complexity of investigated phenomena, functional correlates of social processing and interaction dynamics are indeed maximally informative when they relate to knowable or well-devised  
situations. Collected raw biosignals then have to be processed separately for each participant to obtain individual channel-specific waveforms--which represent local fluctuations of oxygenated, deoxygenated, and total hemoglobin across time due to neurovascular coupling--and individual activation maps--which usually depict the distribution and topography of hemodynamic changes in selected time frames over a head or brain model (first-level single-subject signal processing). Finally, single-subject data and individually-computed indices mirroring hemodynamic activity are integrated, matched, and compared to qualify and quantify inter-brain coupling and to investigate associations and dependencies between the cortical activity of interacting participants (second-level integrated data analysis). Figure 2: Simplified schematic representation of the main steps for fNIRS-based hyperscanning applications with regard to, for example, face-to-face social exchanges. The “Data recording” step refers to the concurrent collection of optical imaging data from multiple participants in a hyperscanning setting. The “First-level single-subject signal processing” step refers to the initial processing of recorded signals to obtain information on individual hemodynamic activities for each participant. Waveforms and activation maps are the two most-used representations for single-brain data. The “Second-level integrated data analysis” step refers to the final integrated analysis of individual data to explore interpersonal synchronization and inter-brain coupling in hyperscanning applications.