# InfNets Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task
## 6 Conclusions  
In the present work, we recruited 22 subjects, designed an RPS task with a total duration of 25 min and 40 s, and then collected the fNIRS data of all subjects under this task. Since fNIRS data have typical time series properties, we applied a TSC method. With the development of deep learning, at present, the CNN-based TSC method achieved a great performance. We applied two top CNN-based TSC methods, Inception and ResNet, to the task. Among them, the Inception method achieved the best classification on fNIRS data with RPS, and its classification accuracy reached 97%. We also evaluated the classification of fNIRS data in the RPS task of traditional classifiers (such as SVM). The experimental results have shown that deep-learning-based methods have more powerful classification abilities than traditional methods. Before classification, we also performed three kinds of data preprocessing. The experiments found that, in the deep learning method, data preprocessing degrades the classification. We compared the performance of HbO, HbR and HbT as inputs, and found that HbT has the best classification, which may be related to the fact that it contains more useful information. In short, since the deep learning method can theoretically approximate any function, it has natural noise suppression capabilities, and data preprocessing can be omitted without reducing the classification performance. The experimental results show that we should input as much information as possible to the algorithm to improve the accuracy of classification.  
Conceptualization and supervision S.H.; methodology, T.M. and W.C.; software, T.M.; validation, Y.X., X.L. and X.Z.; formal analysis, T.M. and W.C.; investigation, T.M., W.C. and X.L.; resources, W.C. and X.L.; data curation, W.C. and X.L.; writing--original draft preparation, T.M. and W.C.; writing--review and editing, T.M., W.C., X.Z. and S.H.; visualization, Y.X.; project administration, T.M.; funding acquisition, S.H. All authors have read and agreed to the published version of the manuscript.  
This work was partially funded by the Key Research and Development Program of Zhejiang Province (2021C03107), the National Key Research and Development Program of China (No. 2018 YFC1407506), Ningbo Science and Technology Project (2020Z077), Ningbo Science and Technology Plan Project (2020G012), the National Natural Science Foundation of China (No. 11621101), the Fundamental Research Funds for the Central Universities (Zhejiang University NGICS Platform), and Guangdong Innovative Research Team Program. The authors are grateful to [PERSON] for valuable discussion.  
Ethical approval was obtained from the South China Normal University's Ethical Review Board.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.