Changes

===_maximalAbsolute Neural Evoked Energy <math>_mANEE</math>===

===_maximalAbsolute Neural Evoked Energy <math>_mANEE</math>===

As already mentioned, the electromyographic signals show high complexity, and the mechanisms underlying the generation of EMG signals appear to be non-linear or even chaotic in nature. Researchers are trying, however, to improve the systems of mathematical filtering<br />

As already mentioned, the electromyographic signals show high complexity, and the mechanisms underlying the generation of EMG signals appear to be non-linear or even chaotic in nature. Researchers are trying, however, to improve the systems of mathematical filtering<br />

[[File:Potenziale Evocato della Radice Trigeminale.jpg|sinistra|miniatura|'''Figure 3:''' The figure shows the signal saturation of the root with respect to latency and amplitude.]]

[[File:Potenziale Evocato della Radice Trigeminale.jpg|left|thumb|'''Figure 3:''' The figure shows the signal saturation of the root with respect to latency and amplitude.]]

like in the most recent Wavelet algorithm,<ref>Bonato P, Roy SH, Knaflitz M, De Luca CJ (2001) Time-frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions. IEEE Trans Biomed Eng 48: 745-753.</ref> but it still remains very difficult, if not impossible in some cases, to separate the EMG signal from the unavoidable noise.

like in the most recent Wavelet algorithm,<ref>Bonato P, Roy SH, Knaflitz M, De Luca CJ (2001) Time-frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions. IEEE Trans Biomed Eng 48: 745-753.</ref> but it still remains very difficult, if not impossible in some cases, to separate the EMG signal from the unavoidable noise.