## 3 Results
### Cognitive Attention Test Pilot Human Study with SCISCCO  
The new probe of Figure 12 and the SCISCCO system of Figure 5a were then used in a pilot human study involving 25 healthy participants and the cognitive attention test protocol. Since the measurement site was on the forehead, where there is limited fat between the skin and skull, the data on the BMI, fat percentage, and subcutaneous fat of the healthy participants were not collected. One problem encountered in the measurement was the slow speed of our current implementation of the SCISCCO system (~17 s per scan), which meant that motion artifacts and other environmental fluctuations within the scan time could corrupt the actual data taken during the measurement. More specifically, the current SCISCCO system implementation of Figure 5a required about 17 s per wavelength scan. In the wavelength range of 750-900 nm, we took 50 data points in 3-nm steps. It took approximately 100 ms for the grating in the spectrometer to move to a particular wavelength step, and then the data acquisition time was about 100 ms once at that step (waiting for the lock-in amplifier to settle, read the output value, etc.). Therefore, 50 data points at a time of 200 ms per point corresponded to 10 s. Furthermore, it took around 7 s for initialization of the instrument and moving the grating back to the starting position. This long data acquisition time was due to the particular scanning spectrometer used in our experiments, but a much faster acquisition time could be achieved if tunable filters were used instead. For example, if we replaced the grating spectrometer with an acousto-optic tunable filter, then the data acquisition time was expected to drop to ~0.5 s [39]. In the future, we intend to upgrade the SCISCCO system to an acousto-optic tunable filter wavelength selection system. The detection time relative to the attention test protocol is illustrated in Figure 15. The attention task period was 60 s, but it took about 5 s for the human participant to respond. Therefore, our detection began about 5 s after the start of the attention task, leaving about 55 s in which to conduct the measurements. In principle, we should have been able to fit 3 of the 17-s scans within the 55 s measurement time. However, because of some timing inaccuracy in starting and stopping as well as other timing jitters, we completed only two scans per attention task period to avoid obtaining only a partial scan or a scan that overlapped the attention period and the rest period. Figure 14: Using the probe of Figure 12 in the set-up of Figure 13, a comparison of the HbO measurements using fNIRS (black curve) and the SCISCCO system (red curve). The slow data acquisition or scan time could lead to data corruption due to motion artifacts, environmental changes, and other fluctuations within the 17-s scan time. For example, if the participant moved within the 17 s (a fairly common occurrence), the current embodiment of the SCISCCO system could not distinguish changes in the hemodynamic or CCO signal from the motion artifact. As a consequence of this timing difference, not all blocks of data collected with the SCISCCO system would be accurate. Therefore, we applied a screening filter to the data collected on the 25 participants to determine which data sets to accept. For the attention study, we screened or filtered our data based on the HbO observation. During the attention test, we expected the activity to require more brain function and a consequent increase in the oxygen sent to the brain (i.e., an increase in HbO). We conducted two-step filtration in our data screening. First, we discarded data from the \"outlier\" participants. There were three types of outliers, which led us to discard the data from eight participants. There were technical errors in data acquisition during two of the participants' measurements. Hence, the data from these two participants were discarded. Three additional participants were removed due to unsatisfactory signal quality, which we believe was due to significant motion artifacts.