# InfNets Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task
## 5 Results and Discussion  
For each channel, we averaged the data of all subjects and displayed the waveform. Figure 7 shows the averaged responses of raw data for the different tasks, and Figure 8 shows the responses after four kinds of data preprocessing. In each subfigure, the horizontal axis represents the time range (15 s for resting state, 5 s for task state), and the vertical axis represents the relative concentration change in HbO. Red lines represent the motor imagery task of Paper, green lines represent the task of Rock, and blue lines represent the task of Scissors. Figure 8a-d show the averaged responses after Z-normalization, max-min normalization, FIR filter and Butterworth filter, respectively. In the raw data, the 24 th and 28 th channels showed the strongest activation signals. They correspond to the somatosensory motor cortex (SMC) area of brain [39]. Preparing a system with only a few channels could potentially reduce the cost of the BCI system. In addition, although exercise-related BCI usually focuses on the primary motor cortex, the channels with the best classification result are located in the SMC, which may indicate that SMC is an active area in sports imaging tasks. In the Z-normalization data, the 27 th channel, which originally had a very weak signal, become very strong. For this channel, Z-normalizaiton amplifies the noise and suppresses the useful signal. Thus, Z-normalizaiton may not be a good data-preprocessing method. In the max-min normalization preprocessing method, the data are normalized to a range between 0 and 1, which may lose activation intensity information and may affect the classification. In the FIR and Butterworth band-pass filter methods, the signal has a significant amplitude attenuation, and it becomes very smooth, which may lead to the loss of a large part of the detailed information that originally existed in the raw signal. Figure 7: The averaged responses of raw fNIRS data for the different tasks among all subjects. Figure 8: _Cont._  
Figure 8: The averaged responses for the different tasks among all subjects after four kinds of data preprocessing. **(a)** The averaged responses after \(Z\)-normalization, **(b)** The averaged responses after Max-min normalization, **(c)** The averaged responses after FIR band-pass filter, **(d)** The averaged responses after Butterworth band-pass filter. In order to check the robustness of the algorithm, we independently trained and evaluated the network 10 times, and calculated the accuracy of the classification each time. Figure 9 shows the accuracy box plot of the classification using HbO, HbR and HbT matrices, where each blue rhombus represents the average of 10 independent experiments, each red point represents an outlier, and the orange bar represents the result of Inception classifier, while the gray bar represents the result of ResNet. Figure 9a shows the classification of Inception and ResNet classifiers on the raw data, Figure 9b shows the classification on the data after Z-normalization, Figure 9c shows the classification on the data after max-min normalization, the classification of Inception and  
Figure 9: The accuracy box plot of the classification using HbO, HbR and HbT matrices. (**a**) The classification of raw data, (**b**) The classification of data after Z-normalization, (**c**) The classification of data after Max-min normalization, (**d**) The classification of data after FIR band-pass filter, (**e**) The classification of data after Butterworth band-pass filter, (**f**) The classification of data after baseline correction. ResNet classifiers on the data after FIR band-pass filter and Butterworth band-pass filter are shown in Figure 9d-f shows the classification of the data after baseline correction.