## 4 Discussion  
In the present study, we used a somatosensory and motor stimulation paradigm in order to study the dynamics of evoked cerebral HR, as measured by fNIRS, when the velocity of the somatosensory or motor stimulus is the variable of interest. The primary aim was to identify the stimulus parameters that evoke the maximum HR. Additionally, we trained and evaluated the performance of an RDA-based classifier in discriminating the traverse velocity of the stimulus. To the best of our knowledge, no machine learning algorithm has been previously used to classify the fNIRS-based HR signal when the velocity of the stimulus is the variable of interest. The most-comparable BCI-fNIRS studies involving three-class discrimination tend to consider inherently more discriminable scenarios, e.g., rest vs. left-hand vs. right-hand motor execution [46], or mental arithmetic vs. left-hand vs. right-hand motor imagery [47]. The average accuracies of tri-classifiers that are reported in [46; 47] range roughly from 55% to 90%, depending on the classification task, feature vectors, and the classification algorithms. In comparison, our average accuracies of 51.7% and 55.6% for the passive and active task, respectively (as shown in Figure 3b), for an inherently less discriminable task, demonstrates the potential of fNIRS in revealing and exploiting subtle HR differences. The near-perfect performance (>99%) of two-class discrimination of the stimulus, as illustrated in Figure 3a, makes fNIRS an ideal complementary modality in hybrid BCI solutions, particularly given the ease-of-use and noninvasive nature of the technology. The immediate next step in improving the classification performance, is the fine-tuning of the classifier and the optimization of the feature vectors based on the specific task. Since the M1 and S1 areas of the cortex are extremely responsive to any movement, additional improvements may be achieved by motion-correction solutions and monitoring movement artifacts, e.g., by the inclusion of electromyogram or accelerometer sensors. It is important to note that, even if the fNIRS acquisition device is completely robust against movement artifacts, the elicited HR of such movements in the cerebral cortex still remains, and can only be addressed if comprehensive auxiliary signals point to undesirable movements. The evoked hemodynamic response in extracerebral tissues, e.g., skull and scalp, is another conflicting measurement that is picked up by the NIRS machine. Therefore, the inclusion of short-separation channels for regressing out the extracerebral HR can improve the reliability and accuracy of such experiments. Furthermore, physiological monitoring with a minimal array of EEG electrodes to monitor participant status (i.e., alert, drowsy, sleep) is advisable, as state control can significantly affect cerebral process mechanisms, including HR and blood flow dynamics during somatosensory encoding and motor control [48]. It is known [49] that voluntary sensorimotor tasks, i.e., the active paradigm (motor plus somatosensory), can evoke a larger HR amplitude due to the activation of motor cortex and movement-related inference which engages S1, as opposed to somatosensory-only tasks that typically involve passive tactile stimulation of the extremities. Higher HR amplitude could potentially lead to more discriminable feature vectors, which is a possible explanation behind the slightly higher average accuracy (55.6% vs. 51.7%) in the active versus passive experiment. However, if discriminating the stimulus velocities was the only goal, the inclusion of active paradigm may not be justifiable, as it may bring complicating factors to the study, such as movement artifacts or how the participants vary in performing the task, and negatively impact the reproducibility. Moreover, the applied hand flexion force by participants during the active paradigm, and its likely variation across different velocity trials, is a potentially a contributing factor in evoking HR [50; 51].