## 4 Discussion  
The final goal of the rehabilitation system presented in this study is to bring the functional recovery of the impaired hands of post-stroke patients. The essential element of the system is the NIRS-BCI module, and the most important characteristic is how well NIRS-BCI can detect motor intention. We therefore tried several approaches and compared the results to obtain the best performance of the BCI module. To determine the best accuracy, we compared the classification results of differing (1) delays in hemodynamic responses and lengths of the feature window, (2) channel composition, (3) the NIRS signal, and (4) channel selection. Accuracy and latency depended on \(\mathrm{D_{HR}}\) and \(\mathrm{L_{FW}}\) selections, which turned out to be related to each other. This suggests that we must carefully select \(\mathrm{D_{HR}}\) when \(\mathrm{L_{FW}}\) depending on the purpose of the NIRS-based system. Although a \(\mathrm{D_{HR}}\) between 8 s and 9 s and an \(\mathrm{L_{FW}}\) between 11 s and 13 s guaranteed the maximum classification accuracy, detection latency was distributed between 9 s and 11 s. These combinations are acceptable and could be useful if the system is for offline analysis, where classification accuracy is of importance and detection latency can be ignored. However, these selections are not suitable for real-time use. When a \(\mathrm{D_{HR}}\) is between 0 s and 2 s and an \(\mathrm{L_{FW}}\) is around 1 s, some  
Figure 5: Hemodynamic Responses and Detection Latencies by Feature Vector Compositions (Participant ID: 3): (**a**) The hemodynamic responses of [HbO] and [HbR] during the hand-closing task. The responses are averages of all channels across all trials after preprocessing by moving average convergence/divergence (MACD); (**b**) Visual comparison of detection latencies for each of the 12 feature vector compositions. classification accuracy is lost, but detection latency is shortened to between 2 s and 3 s. These selections are preferred for a real-time NIRS system, such as what we have presented here. Channel-preserved feature vector compositions generated better performance than channel-averaged compositions in terms of classification accuracy and detection latency. Classification after averaging all channels have reportedly produced accuracies of 78% [22] and 65-75% [23]. This approach might be effective and useful for a BCI system with fewer channels that is only concerned with one cortical area. This is indeed the case for our system of only nine channels that are focused on the motor cortex, especially when all channels record similar temporal patterns. However, our comparisons revealed that the channel-preserving composition improved classification accuracy and shortened detection latency. [PERSON] et al. (2010) also reported that the feature space taking account of spatial information (i.e., channel-preserved) increased accuracy by 7.7% and shortened detection latency by 2.4 s. Thus, even though channel-averaging might guarantee an acceptable accuracy and is computationally effective, by reducing the dimensions of feature vectors, we recommend the channel-preserving feature vector for an accurate BCI with short detection latency. Our results also indicated that CNR is not a perfect criterion for selecting informative NIRS channels for classification. CNR-selected channels could not achieve the levels of both classification accuracy and detection latency that were obtained using all channels (Table 2). These results might indicate that there were some channels whose [HbO] or [HBR] correlated with the imagined hand motions but did not meet the CNR criteria. Thus, it might suggest that additional criteria are necessary. The coefficient of variance or the point-biserial correlation coefficient can be a good candidate to investigate. However, applying CNR can diminish the number of channels that are incorporated into a feature vector, thereby reducing computational time and cost.