**3.2. AIEMG signals**

Computational Intelligence in Electromyography Analysis – 324 A Perspective on Current Applications and Future Challenges

(N = 200)

**Figure 12.** AIEMG signals regarding "paper"

We next calculated the averaged IEMG (AIEMG) signals from IEMG according to eq. (1). They are stable and noiseless compared to the IEMG, because the feature prevents instantaneous noise of IEMG signals. It is important to adopt the optimum sampling number, N, to obtain the ideal AIEMG feature. When the number is too small, the signal intensity fluctuates and it is difficult to obtain consistent feature at every measurement. If it is too large, the AIEMG signal becomes blunt and thus its original waveform is lost.

Figure 10 shows the examples of AIEMG derived from IEMG signals indicated in Fig. 5, with regard to the number of sampling, N as 1, 50, 100, 200, 500, 1000, 2000, and 5000.

Figure 10(c1\_1), for instance, displays the AIEMG signal measured by channels 1 with sampling number of 1, where the horizontal axis expresses time, and the vertical represents magnitude of the AIEMG signal.

Figures 11 and 12 are representations of the AIEMG signals regarding channels 2 and 3, respectively, whose sampling number is 200.
