## 4 Discussion
CNR could be more useful and effective if a system adopts many more NIRS channels, possibly producing more uninformative or noisy channels. This needs to be examined in future studies. The concentration of HbR helps contribute to classification. The contribution of [HbR] alone is limited in the sense that classification improved when [HbO] was included in the feature vector. The feature compositions that included only [HbR] led to poorer classification performance, which dropped to the level of random guessing (Table 2). Based on our analysis, the worst approach is incorporating only [HbR] into a feature vector after averaging. In contrast, the compositions with both [HbO] and [HbR] improved both classification accuracy and detection latency over those with only [HbO] through all of the tested compositions, across all participants. Most NIRS-BCI systems [10] have not favored [HbR] because of its unreliability, relating to its small dynamic range and its signal-to-noise ratio (SNR). However, [HbR] is conditionally and marginally useful for better classification. The approach and results of this study with the neurologically intact participants could be translated into clinical cases. We consider that the final user of our system will be patients who suffer from hand motor impairments owing to subcortical stroke. Because of their impairments, the patients must perform motor imagery, not motor execution. However, because of the lesion location being subcortical, the ipsilesional cortical areas are intact and can produce motor imagery-induced activations, which are detectable. The MI-evoked hemodynamic responses by a subcortical stroke patient could be similar to that of a neurologically intact participant. Studies of post-stroke patients that use hemodynamics-related modalities, such NIRS, have revealed that trained patients could produce clear motor imagery-induced cortical activity similar to motor execution [12]. In addition, the clinical studies of the BCI systems based on electrophysiological responses, being neurovascularly coupled with hemodynamic responses, demonstrate the existence of classifiable, MI-evoked responses. In the magnetoencephalography (MEG)- and sensorimotor rhythm (SMR)-BCI studies, post-stroke patients having subcortical lesions could learn how to control a BCI system [24; 25]. The successful cases of EEG-BCI-based rehabilitation indirectly support a post-stroke patient's ability to induce and control a task-related cortical activation [26; 27]. Moreover, the pattern of a task-evoked hemodynamic response after a subcortical stroke seems preserved. The fMRI study of patients with cerebral ischemia [28] demonstrated an altered pattern of task-induced activation, i.e., an increase of [HbR] during a task, but this kind of alteration in a patient with a subcortical stroke has not been reported to our knowledge. Even though the altered activation can take place with a subcortical stroke, it implies that there is a task-related activation that exists and that is differentiable from a baseline. Thus, it can be detectable by a machine learning-based classifier, such as LDA. This study has three main limitations. First, the sample size was small, and evaluation only used one technique (LDA) and two features (mean and slope). The inclusion of non-tested features, such as variance, skewness, and so on, and the testing of different classification techniques, such as SVM, neural networks, hidden Markov model, and naive Bayesian with sets of tested and non-tested features could bring different results. Second, we must note that the two features here were calculated within a feature window of the same length. Each specific feature might contribute more to classification with a window of its own optimized length. Further research should be conducted for clarifying these problems. Finally, the influence of task-evoked extracerebral activation on our NIRS-BCI performance was not excluded due to the limitation of our NIRS system setup.