# InfNets Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task
## 5 Results and Discussion
In order to find the best band-pass cut-off frequency of FIR filter, we took a different band-pass upper cut-off frequency of the FIR filter and evaluated their classification performance using Inception and HbT; the results are shown in Table 1. We evaluated eight different band-pass frequencies (0.01-0.09 Hz, 0.01-0.1 Hz, 0.01-0.2 Hz, 0.01-0.3 Hz, 0.01-0.5 Hz, 0.01-0.8 Hz, 0.01-0.1 Hz and 0.01-0.3 Hz, in which 0.01-0.09 Hz is default setting), and found that different band-pass cut-off frequencies of the FIR filter did not significantly impact the classification performance. This showed that even if the FIR filter has a wide band-pass range, it may filter out the useful feature patterns (such as activation intensity) due to other parameter problems, and cause the classification performance to decrease. We compared the classification of raw data and four preprocessing methods, and found that raw data have the best classification, and can reach 97%. The baseline correction is the second, whose highest accuracy is 94%, and the max-min normalization followed by 92%. The classification of FIR band-pass filter, Butterworth band-pass filter and _Z_-normalization are relatively poor. Although both FIR and Butterworth band-pass filters are effective methods to remove fNIRS noise, in a specific task, it is often necessary to choose different filtering methods and set different filtering parameters to obtain good results [37; 38]. The deep learning methods have achieved good classification results in the rock-paper-scissors task, which proves that the neural network extracts useful pattern features from the input signal. These useful pattern features may be extracted from the noise between different tasks. If this is so, then these noises are actually crucial information for classification, and we should not use filters to remove them. However, we are more convinced that these useful pattern features are extracted from the activation signal, because the three tasks typically have different activation patterns, and the noise interference they received was almost the same. In the task of classifying fNIRS signals based on deep learning neural networks, the neural network could fit a filter that removes noise (that is not related to pattern features) better than the traditional (such as FIR or Butterworth) filter. The filter obtained by learning from task-specific data is more suitable for the fNIRS signal itself. We found that applying the baseline correction slightly degraded the classification performance compared to the raw fNIRS data input. That is because the important pattern feature extracted by neural network is that different channels have different activation intensity in the three tasks. Baseline correction would irreversibly break this pattern feature, and degraded the classification performance. As far as the classification method is concerned, the classification accuracy of Inception is higher than that of ResNet. As far as the three hemodynamic parameters (HbO, HbR and HbT) are concerned, the classification accuracy of HbT is higher than that of HbR or HbO. As shown in Figure 9c, the classification  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Band (Hz)** & **0.01–0.09** & **0.01–0.1** & **0.01–0.2** & **0.01–0.3** & **0.01–0.5** & **0.01–0.8** & **0.01–1** & **0.0  
accuracy of Inception on HbT is 22% higher than that on HbO. In some previous studies, most of the researchers used HbO as input data [40]. This may be because HbO has a relatively pure signal, but for FNIRS classification problems, HbT may be a better choice. In addition, some traditional classifiers (such as Perceptron, Linear Discriminant Analysis (LDA), K-Nearest Neighbor (KNN), Adaboost, Decesion tree, Random Forest (RF), SVM and MLP) with raw data input (HbO, HbR and HbT) were evaluated. Table 2 shows the average accuracy of 10 independent experiments for each classifier. It can be found that the TSC method Inception with the HbT matrix achieved the best result of all the classifiers and hemodynamic matrices.