# InfNets Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task
## 1 Introduction
Since HbO and HbR have different absorption spectra, by applying the Modified Beer-Lambert Law, the change in the optical density can be converted into the change in the concentration of HbO and HbR. Motor execution and motor imagery involve the motor function control area of the cerebral cortex. The research usually focuses on measuring motor imagery activities to extract commands from the brain to control limbs. The prefrontal cortex (PFC) is closely related to cognition, decision-making and working memory. Introducing the PFC area into BCI research helps to solve the control command extraction of patients with impaired brain motor function. [PERSON] et al., believed that the change in PFC blood flow was related to the movement commands sent by the advanced cognitive system [14]. In 2013, [PERSON] et al., discovered the functional connectivity between the left dorsal prefrontal lobe and the motor area [15]. In the same year, [PERSON] et al. explored the gender differences in blood flow changes in the PFC area of finger tapping [16]. [PERSON] et al. found that the average 5-fold cross-validation accuracy of the NIRS-based BCI using time-domain features was 87.5% using the linear support vector machine (SVM), and the average 5-fold cross-validation accuracy using the hidden Markov model was 93.4% [17]. On the same dataset, [PERSON] et al. improved these data. The classification accuracy of linear SVM was 93.4%, and the classification accuracy of Gaussian SVM was 93.9% [18]. In a study of 40 patients with amyotrophic lateral sclerosis (ALS) (including 17 fully locked-in), patients were asked to answer \"yes\" by performing mental calculations and \"no\" by staying relaxed. A total of 70% of ALS patients who were not fully locked and 40% of ALS patients who were fully locked could successfully decode, and the classification accuracy was about 80% [19]. [PERSON] et al. carried out a study in which 10 healthy subjects were asked to imagine moving objects. The average accuracy of the SVM classifier in four directions, up-down selection and left-right selection were 40.55%, 73.05% and 70.7%, respectively [20]. Classification technology is used to identify the different brain signals produced by the subjects. The most commonly used classification methods are Random Forest (RF) for fNIRS-BCI, and SVM, HMM and Artificial Neural Network (ANN) for the hybrid EEG-fNIRS BCI motor imaging classification. In 2018, [PERSON] et al., applied deep learning and achieved an average accuracy of 83.28% [21]. However, their deep learning method has certain limitations, because they only used a very simple neural network as a classifier, and did not fully consider the relevant information in the time dimension. [PERSON] et al., used SVM, ANN, and Convolutional Neural Network (CNN) to classify right-hand and left-hand motor execution tasks on eight healthy subjects, and the classification accuracy of SVM, ANN, and CNN were 86.19%, 89.35%, and 92.68%, respectively. The CNN-based scheme provided up to 6.49% and 3.33% higher accuracy over SVM and ANN [22]. [PERSON] et al., demonstrated that CNN performs better with respect to classification accuracy in the classification of four motor imagery tasks (right-fist clenching, left-fist clenching, right- and left-foot tapping) compared to SVM and Multi-Layer Perceptron (MLP) neural network [23]. [PERSON] et al. [24] classified the finger tapping task (task duration is 10 s) of left and right hand motion imagery using CNN-based deep learning methods. In this paper, we tried to use a shorter (task duration is 5 s) task, with more classes (rock-paper-scissors is a three-classification problem, while finger tapping of the left and right hand is a two-classification problem), which is more complex (rock-paper-scissors task is more complex than the finger tapping task) task to verify the performance of the deep learning classification methods, and achieved good results. Rock-paper-scissors (RPS) is a two-player game. The purpose of playing RPS is usually (but not always) to trick the opponent.