## 1 Introduction  
Human biological signals are triggered and controlled by an internally connected neural network, sending and receiving electrical signals to and from each other. Because the brain waves produced in this process are indirect signals delivered in the form of ions inside the skin, the shape and characteristics of the signals vary depending on the distance of movement and the location of the transmission parts of the signals [1]. From the perspective of these signals, the transmission signal of the muscles is made by measurement of an electromyogram (EMG), but its meaning is difficult to derive from the pattern or shape of the control signal. Here, electroencephalogram (EEG) signals are extracted in a similar way to muscle signals. However, because the speed at which the situation or time changes is very fast compared to the EMG, it is difficult to identify the motion or intention with the time domain output. Therefore, it is necessary to properly convert and use EEG output information of that time to analyze motion or activity of a particular muscle. This study is interested in whether the output of brain waves that change during muscle activity can distinguish specific movements of muscles. EMG shows information by muscle contractions as an electrical signal. Therefore, if a brain wave changes at the time of muscle output, the EEG information at that time can be analyzed to understand the meaning of the output signal from the brain. There are a variety of studies on brainwave analysis related to muscle movements. There is a study based on nonnegative matrix factorization (NMF) that analyzes brain signals according to the bending of 10 fingers and records accuracy [2]; there is also a study that analyzes EMG signals related to seven hand movements to predict the users' intentions and analyzes the accuracy [3]. Furthermore, there are various studies on the classification of brain signal patterns for the correct classification of EEG patterns [4; 5]. These analytical techniques allow various processes to be controlled by biological signals and quickly predicted and utilized by brain signals, and for the output states of signals transmitted within a person's body to be accurately determined. Because the EMG contraction voltage signal is mostly output by muscle contraction, as shown in Figure 1, it is likely that a number of signals will be mixed or transmitted in a special form if the size of a particular signal exists. When the output of this signal is divided into sections, the EMGs show different outputs (Figure 1), even though there is no muscle change. Therefore, the process of defining the period of signals delivered through the muscles is important. Also, because these signals are measured by noninvasive methods, they are somewhat less reliable in accuracy than the extraction of electrical signals through needle electrodes or chips inserted into the subcutaneous layer. In addition, it is somewhat unfamiliar to the subjects and difficult to apply the method of measurement by inserting a needle or surgery in a specific way within a person's body. Thus, in the  
Figure 1: EMG (electromyogram) output during muscle movement and EMG output at specific intervals. process of analyzing brain waves or processing bio-signals, it is common to use a safe and easy way to acquire data, even if it is an indirect signal. Also, because these are indirect signals, various research attempts, verification processes and applications are required to identify the actual internally transmitted form. Thus, in this study, the experimental target is selected and the signal extraction method is defined firstly, then the data is processed and analyzed secondarily. After describing the extraction process and data selection process in the secondary process, the large number of collected data, algorithms and conversions for the process are analyzed.