## 4 Results  
The sEMG signals from the four muscles were successfully measured and used for controlling the elbow. Figure 4 shows the predicted error curve of a subject. It can be seen that the model predictive angle curve performs a good regression on the actual motion angle curve, and it is feasible to use the sEMG signal as the control signal of interactive devices. The regression curve shows that there is still some error in the local point during the motion, especially at the starting point and the inflection point of flexion and extension. This may be because the muscle response at these points is delayed or a large noise is generated. It can be seen from the deviation of the regression curve that the recognition \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Experiment** & **Abbreviation** \\ \hline
1 & External visual focus of attention (blinking) horizontal direction experiment & EVA-HD \\
2 & Internal visual focus of attention (blinking) horizontal direction experiment & IVA-HD \\
3 & Proprioception no visual feedback (closed eyes) horizontal direction experiment & NVF-HD \\
4 & External visual focus of attention (blinking) vertical direction experiment & EVA-VD \\
5 & Internal visual focus of attention (blinking) vertical direction experiment & IVA-VD \\
6 & Proprioception no visual feedback (closed eyes) vertical direction experiment & NVF-VD \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Protocol information. Figure 3: sEMG electrode position and direction of motion. error of motion angle under the internal focus of attention condition is the lowest. Figure 5 shows the predicted error curve. The curves and shadows indicate which time period errors are greater in the time series of the entire motion process. Table 2 shows the average RMSE of all subjects under different conditions. It can be seen that the recognition results under the three psychological factors all show a consistent trend in the two directions of motion. In comparison, the recognition results in the proprioceptive condition experiment are preferably in the two directions of motion. Among them, the highest recognition accuracy is the proprioception experiment in the vertical direction (the RMSE mean is 6.8061\({}^{\circ}\)). The mean RMSE in the external focus of attention condition experiment was 1.81\({}^{\circ}\) and 1.34\({}^{\circ}\) lower than the internal focus of attention condition experiment, which means that the recognition accuracy is higher. The lowest recognition accuracy is the experiment of the internal focus of attention condition in the horizontal direction (the RMSE mean is 11.734\({}^{\circ}\)). Comparing the elbow motion in both directions, the angle estimate in the vertical direction is generally better than the horizontal direction, and the RMSE value is maintained between 6-10\({}^{\circ}\). The recognition model has a good predictive effect. The RMSE value in the horizontal direction is between 8-13\({}^{\circ}\). This also shows that the stability of the elbow joint in the vertical direction is higher than the horizontal direction. The two-way analysis of variance showed that both psychological factors and direction of motion had significant effects on RMSE values. This statistical test was normally distributed at the 95% confidence level. Among them, the main effect of psychological factors was significant, F(2, 84) = 96.87, \(p\) < 0.001. The main effect of the direction of motion was significant, F(1, 24) = 280.48, \(p\) < 0.001. However, there was no significant interaction between the two factors, F(2, 84) = 1.75, \(p\) > 0.05. From the average values of the coherence coefficients of all the experiments in Table 3, there was no coherence between the sEMG and EEG signals under the proprioceptive condition, and coherence was observed under both the internal and external conditions. The largest value of the coherence coefficient is the C3 channel of EEG and the biceps channel of sEMG (0.2285) in the proprioception experiment in vertical motion.