## 5 Discussion  
### Advantages of Proposed Method  
Our proposed method, using a semi-real simulation with the assumption that INIRS signals were possibly contaminated with global task-related SBF, achieved a significantly higher estimation accuracy than conventional design matrices of GLM, which have been used in several studies [5; 16; 26; 27; 28; 29]. However, brain activity estimations with real fNIRS data are more challenging. To investigate the performance of the present method in real data, we partially tested the data obtained by the real experiment which was executed with the same experimental settings as the simulation (see details in Experiment 2 [14]). Figure 6 shows an example of the estimated cerebral-hemodynamics from Ch 21 and 27, placed on the left (ipsilateral) and right (contralateral) primary sensorimotor cortices, respectively. The estimated waveform of rt-ShortPCA (represented by On-Line ShortPCA in the figure) shows the most similarity with the off-line. In contrast, the waveforms of the other design matrices (i.e., simple and standard) show particularly larger amplitudes of cerebral-hemodynamics in the 4 th-6 th task sections. [PERSON] et al. reported that the off-line ShortPCA GLM achieved a performance very close to the fMRI estimation of the identical real data [14]. Thus, by assuming that the estimated wave by off-line ShortPCA GLM is true, the proposed method reduces the negative effect of task-related scalp-hemodynamics and achieved better estimation accuracy than the conventional design matrices in real time. This result partly indicates that the present method possibly effects for scalp-hemodynamics artifact reduction in real-time. To evaluate the quantitative performance, further studies should evaluate the time course of t-maps of cerebral activity and compare these with fMRI data for more practical use. Applying SWA for real-time artifact reduction improves estimation accuracy without reducing the benefit of off-line ShortPCA GLM (i.e., simplicity and few physical constraints). Hence, this method is possibly useful for real-time applications. Previous studies have shown that fNIRS may be helpful, for both diagnosis and therapy, in epilepsy, dementia and stroke [30; 31; 32; 33]. In clinical practice, the measurement area should be as broad as possible, in order to detect all potential brain activation differences in patients compared with healthy subjects, and stress should be minimized as much as possible. On- and off-line ShortPCA GLM can provide such broad measurement areas with few physical constraints because they need a limited number of short channels to extract the global temporal pattern of SBF. Furthermore, with real-time monitoring of brain activation, diagnosis times should be reduced, which may cause participants to experience less stress. Diagnosis quality should also be improved by real-time artifact reduction. The proposed method may also be utilized in therapeutic situations, especially with neurofeedback training. [PERSON] et al. reported that the effectiveness of neurofeedback training can be reduced if feedback signals are not related to subject-dependent true brain activation [4]. Thus, false  
Figure 6: Typical temporal estimated waveforms of cerebral hemodynamics in ipsilateral and contralateral motor area symmetrically located by rt-ShortPCA GLM for real functional near infrared spectroscopy (fNIRS) data of a representative subject during a left-handed finger tapping task. The first trial samples from 0 to 45 s are not shown because of unstable estimated waves. positive brain activation, affected by task-related scalp-hemodynamics, probably reduces the quality of these training effects. For such cases, our proposed method would be powerful and suitable for supplying high quality feedback signals to a subject. Additionally, we can expect advantages for high-precision brain computer interfaces using task-related cerebral activity as the input signal.