# Analyzing Brain Waves of Table Tennis Players with Machine Learning for Stress Classification
## 1 Introduction  
The psychological quality of competition sports players is critical for them to achieve peak performance, but psychologically, they are liable to stress regardless of everyday stresses or the stresses of competition [1, 2]. While appropriate levels of stress help men and women achieve optimal performance, improper stresses impair performances [3, 4, 5]. When a person is subject to external stimulation that threatens the balance within his/her body, an adaptation of physical and mental status emerges; in that sense, stresses are an adaptive reaction [6]. Despite different sources of stress, in order to maintain a physical-mental balance, certain similar physiological reactions occur in people due to stress: the secreted adrenaline increases, along with increased heartbeats, fast breathing, increased blood pressure, increased blood sugar content, and other changes [7]. Though stresses cannot be measured directly, individual physiological reactions and changes can be detected through physiological parameters such as electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) to analyze the status of stresses to which the individuals are subject [8]. Stressors are the stimulating contexts that cause stress reactions and can be applied in stress detection. The Stroop color and word test (SCWT) was, at first, widely used in experiments of clinic research of neuropsychology and then discovered to induce an increase in adrenaline, heartbeats, respiratory rates, anxiety, etc., which are all that emerge as a result of stress reactions caused by stressors and, thus, can be effectively applied in stress detection [9, 10, 11]. In the Montreal imaging stress task (MIST), whichoriginated from the Trier social stress test (TSST), difficulty in mental arithmetic (MA) and a time limit were used in the experiment groups, and it showed a significant increase in the level of cortisol in saliva compared with the control groups and the rest groups. Cortisol is also called a stress hormone, as cortisol begins to increase when stressors appear, with the effect of increasing the blood pressure and blood sugar. Hence, MIST can serve as a tool for stress detection to be used in research on the influences of social-psychological stresses [12]. EEGs reflect the psychological and physiological status of a person. EEG measurements work to detect the stress condition of a subject promptly, making an objective and quantifiable assessing tool [13]. However, the original signals of brain waves have a low signal-to-noise ratio (SNR), are not stable signals, and have high variations among individuals; thus, how to analyze the physiological signals from the brain is an important issue in increasing the accuracy of stress assessments [14]. According to the International Federation of Clinical Neurophysiology (IFCN), by the most common categories, the frequencies of brain waves, are divided, from low to high, into the Delta band (0.1-\(\spherical  
were 88% and 96% in the cases of SCWT and MA. The authors of [22] utilized SCWT as a stressor to induce four levels of stress and extracted combinations of features from EEG signals, including fractal dimensions and statistical features; the use of SVM as a classifier was able to identify two-level, three-level, and four-level stress classifications, where the accuracy for two-level stresses was 85.17%. Past documents suggested that the measurements by different methods of mental stresses through EEG usually involved three stages, namely, data collection, data preprocessing, and classification [16; 17; 18; 19; 20; 21; 22]. (1) Data collection: in regard to short-term stresses, stressors are used to induce acute stresses, e.g., SCWT [16; 17; 21; 22] and MA [17; 18; 19; 20; 21]. (2) Data preprocessing: intended to eliminate artifacts and noises and to extract useful features, where the methods available for eliminating artifacts and noises are the ICA and filters. (3) Classification: as classifiers, there is machine learning [17; 18; 19; 20; 21; 22] and deep learning [16].