###### Abstract
In the analysis of EMG signals, various computational methods are used, depending on the aim, such as variance analysis, Fisher's least square differences posthoc test [27], principal component analysis, and convolutional neural network [28], InterCriteria analysis [5; 6], and correlation analysis [29; 30; 31], as well as different computational methods for data augmentation [32]. SCA and PCA are widely applied in upper arm EMG research. [PERSON] and [PERSON], 2020, studied the hand dexterity motor skills among thirty-eight neurologically healthy participants by assessing the temporal and quantitative variabilities in their muscle activity [33]. They apply [PERSON]'s rank CA to find the relationship between the time of task performance (ball rotation) and the coefficient of variation of the EMG on-phase duration. The next correlation is searching between the ball rotation time and coefficient of variation of the EMG on-phase area over thirty trials for all participants. Other authors uses [PERSON]'s rank correlation to assess the paretic muscles of thirty-two patients in the chronic stroke stage [29]. They evaluate the relationship between the Fugl-Meyer assessment scores for the upper arm and the percentage of the facilitation of forearm EMG activity. Another work examined arm chronic plegia in twenty-four patients [30]. [PERSON]'s rank correlation and [PERSON]'s U-test are used for the following assessed quantities: upper extremity and spinal cord motor and functional independence, capabilities of the upper extremity, muscle strength of the elbow flexors and extensors, and EMG of right m. biceps brachii and m. triceps brachii. Some studies use [PERSON]'s correlation method during EMG examinations. [PERSON] et al. [27] investigate the muscle fatigue of m. biceps brachii during submaximal isometric contractions, using EMG and mechanomyography. Ten healthy men perform the required tasks for the elbow. Different statistical analyses over four signal segments for each contraction are applied. Also, [PERSON]'s CA is applied over similar physiological events to search for strong correlations. Other authors use [PERSON]'s CA to investigate the signal-tonoise ratio in EMG signals that are artificially contaminated [31]. Their results show that lower [PERSON]'s correlation coefficients are associated with low signal-to-noise values. Conducting biomedical research to obtain EMG signals poses several challenges to investigators. Some scientists aim to study healthy individuals, while others focus on people with specific movement disorders. Knowledge of normal human kinetics can help to understand the pathological processes occurring due to trauma and injury. The main task of biomedical research is to create a sufficiently broad and informative protocol that does not burden the subject. It should involve motor tasks that are easy to perform, repeatable, and accessible with the abilities of research subjects who have different motor cultures. However, a detailed explanation and several practice movements are required before the real experimental trial to ensure the movements are right. In our experimental protocol, along with the specific movements, there is another difficulty--participants should perform tasks with a specific duration. This requires more attention, concentration, and training. Increased attention leads to mental fatigue. This means that shortening the time for the entire examination will preserve the participant's condition. In addition to mental fatigue, a long duration of exercise causes muscle fatigue to build up. Fatigue changes the biomechanical characteristics of the muscle. These, in turn, change the interpretation of results, which is critical for biomedical studies. In addition, fatigue also reflects the participant's condition. Sometimes, prolonged motor activity and maintaining poses can lead to inconvenience, discomfort, or even pain. Emotions (confusion, embarrassment, feelings of stress, etc.) can also be affected [34]. These sensations are more pronounced in disordered people due to their diseases.