## 4 Results and Analysis  
### Separating Left and Right Motion Output by EEG  
We experimented to compare the output of EEGs according to the motion of right and left arm. A Support Vector Machine (SVM) [16] was applied to quantify the performance of the classification. Because the performance of the SVM depends on parameters, the cost value (C, the degree to which the data sample is distributed in different classes) is used for linear isolation based on accuracy, and the cost value and gamma (the distance one data sample affects) is used for RBF (Radial Basis Function; Kernel SVM). Cost values and gamma values should be selected by empirically, and a combination of the two parameters was made using a method called grid search, which selects the parameter values with the greatest average accuracy on average. Grid Search was tested for both linear and RBF when the cost values and gamma values had the correct number of arrays (\(1\times 10^{-6}\), \(1\times 10^{-5}\), \(1\times 10^{-4}\), \(1\times 10^{-2}\), \(1\times 10^{2}\)), and applied the most accurate parameter values for each signal source. Furthermore, since there were fewer samples of the right-handed/left-handed total of 160 experimental data, k-fold was applied to obtain the mean classification accuracy and error. The 10-fold method was used for cross-checking, and the mean and error were obtained by repeating the process 10 times to calculate accuracy. All the brain wave signals from eight channels were collected through a band-pass filter of 0.5-200 Hz, and a 60 Hz notch filter was applied during the processing. The results by cross-validation can be summarized in Figure 14. In Figure 14, A had a classification accuracy of 0.718 (71.8%) and an error of \(\pm\)0.234 when raw data was applied without any pre-processing. B represents the CSP-applied data with an accuracy of 0.828 (82.8%) and an error of \(\pm\)0.181. The accuracy of CSP filter data was 11% higher than that of the raw data. The data from C and D were treated with power spectrum density (PSD) processing, respectively, and normalized to a value of 1.0-20 Hz for a value of 0-1. This is the output obtained from the processed data that applies the mean difference between the sample class (right data) and the other class (left data). The results showed that C and D had classification accuracies of 0.731 (73.1%) and 0.828 (82.8%) and error rates of \(\pm\)0.2 and \(\pm\)0.135, respectively, and that the output of CSP filtering increased by 10%. Figure 14: Comparison of classification accuracy of left and right arms EEGs according to the experimental samples. Through the application of CSP filters, the distribution of data appears to have some level of classification efficiency in C3 and C4 areas by analyzing data at the point in the brain at which motion occurs. Although not all points showed efficient results, it is expected that more accurate classification would be possible if more standardized data were obtained by re-establishing data points and diversifying behavior. The frequency output distribution shows that data from the right and left arms were activated in specific areas through the analysis of differences and the summing algorithm following the CSP filter. Figure 15 shows the distribution of characteristic areas by frequency. In the case of raw EEG, the right-hand side operation showed high energy around 4-6 Hz, and some channels showed high energy values of 10-12 Hz. In the case of left-handedness, it was found that energy near 8-14 Hz was high compared to right-handedness. On the other hand, data analyzed by CSP filtering showed that signals activated in the 10-15 Hz band were common for the left-hand side, and that the energy band of 6-8 Hz for the right-hand side was high. It is estimated that the output of the subject's brain waves can be inferred from the brain signals that classify the movements of the muscles.