# InfNets Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task
## 1 Introduction  
The brain-computer interface (BCI) is a hardware and software communication system that enables people to interact with the surrounding environment by using control signals generated by the brain without the involvement of peripheral nerves and muscles [1]. BCI has created a new non-muscle channel for communicating human intentions to external devices such as computers, speech synthesizers, auxiliary devices, and neuroprostheses. Such a system can be used as a means of communicating and restoring motor function (through neuroprostheses), helping patients with amyotrophic lateral sclerosis (ALS) and spinal cord injuries to move around, such as by controlling prosthetic limbs [2]. This is particularly attractive for people with severe movement disorders. Such tools will improve their quality of life while reducing the cost of intensive care [3]. BCI is an artificial intelligence system that interacts with brain signals in five steps: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and control interface [4]. The signal acquisition phase uses appropriate brain imaging methods to capture brain signals. Generally speaking, the collected physiological signals are relatively weak and contain noise and artifacts, so noise reduction and artifact correction are required. In the preprocsnsing stage, the signal is further processed by a suitable method. The feature extraction stage extracts the features of the signal. The collected brain signal will be mixed with other signals, and the signal is usually not fixed. In order to reduce the complexity of the feature extraction stage without losing relevant information, the feature vector should have low dimensionality. The classification stage considers the feature vector to classify the signal. Therefore, choosing good discriminative features is essential to achieving effective pattern recognition in order to decipher the user's intention. Finally, the control interface stage converts the classified signals into control commands for any connected devices (such as wheelchairs or computers). A variety of modes have been used for brain electrical signal acquisition, including electroencephalography (EEG) [5], magnetoencephalography (MEG) [6], functional magnetic resonance imaging (fMRI) [7] and functional near infrared spectroscopy (fNIRS) [8]. Among them, EEG can provide excellent time resolution, making it an ideal choice for real-time applications, but the technology is still limited by its poor spatial resolution and inherent sensitivity to motion artifacts [9]. Motion artifacts may affect the spectral content of EEG in the frequency range below 20 Hz, and may cause large spikes in the signal that are difficult to correct for [10]. Functional near-infrared spectroscopy (fNIRS) is a promising alternative because it can achieve a good balance between spatial and temporal resolution. It uses near-infrared light to measure oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), and total hemoglobin (HbT) [11]. There are many activation tasks used in the BCI applied research, including motor imagination, mental arithmetic, working memory and other mental activities [12; 13]. fNIRS's main advantages are its relatively low-cost, portability, safety and insensitivity to electrical noise. This technique is based on the strong penetration of red light in biological tissues, and hemoglobin is the predominant light-absorbing substance in the near infrared region (650-950 nm). Since hemoglobin is a kind of oxygen carrier, changes in the concentration of HbO and HbR after neuron activation may be related to the discharge of neurons. fNIRS uses near-infrared (NIR) light emission-detector pairs that operate at two or more wavelengths. The near-infrared light is emitted to the scalp and diffuses through the brain tissue, causing multiple photon scattering. Some of these photons return to the surface of the scalp after absorption and scattering by the tissue and are detected by the detection optical fiber.