Changes

===Electrophysiological procedure===

===Electrophysiological procedure===

As above mentioned, the transcranial electrical stimulation (_eTCS) of both trigeminal roots induced a neuromuscular response called “bilateral Root-Motor-Evoked Potential” ( R-MEPs). It was performed by an electromyographic device (Nemus -NGF, EBNeuro, Firenze, Italy).<ref>Frisardi G (1992) The use of transcranial stimulation in the fabrication of an occlusal splint. J Prosthet Dent 68: 355-360.</ref><ref>Frisardi G, Ravazzani P, Tognola G, Grandori F (1997) Electric versus magnetic transcranial stimulation of the trigeminal system in healthy subjects. Clinical applications in gnathology. J Oral Rehabil 24: 920-928.</ref>[[File:Finite Elements - electric field within the intracranial brain tissue - FEM.jpg|miniatura|'''Figure 1:''' The figure shows the arrangement of the electrodes on the skull and the distribution of electric fields inside the intracranial brain tissue]]Considering the safety limitations,<ref>IEC60601-2-40 (1998) Medical electrical equipment: Particular requirements for the safety of electromyographs and evoked response equipment.</ref> we computed the energy delivered for each single pulse in our application through this formula: <math>E=P\cdot\Delta T=R\cdot I^2\cdot \Delta T=2.5 mJ</math> per pulse. Since 2 stimulators were used, the limits were ten times lower than those stated in the IEC regulation.

As above mentioned, the transcranial electrical stimulation (_eTCS) of both trigeminal roots induced a neuromuscular response called “bilateral Root-Motor-Evoked Potential” ( R-MEPs). It was performed by an electromyographic device (Nemus -NGF, EBNeuro, Firenze, Italy).<ref>Frisardi G (1992) The use of transcranial stimulation in the fabrication of an occlusal splint. J Prosthet Dent 68: 355-360.</ref><ref>Frisardi G, Ravazzani P, Tognola G, Grandori F (1997) Electric versus magnetic transcranial stimulation of the trigeminal system in healthy subjects. Clinical applications in gnathology. J Oral Rehabil 24: 920-928.</ref>[[File:Finite Elements - electric field within the intracranial brain tissue - FEM.jpg|thumb|'''Figure 1:''' The figure shows the arrangement of the electrodes on the skull and the distribution of electric fields inside the intracranial brain tissue]]Considering the safety limitations,<ref>IEC60601-2-40 (1998) Medical electrical equipment: Particular requirements for the safety of electromyographs and evoked response equipment.</ref> we computed the energy delivered for each single pulse in our application through this formula: <math>E=P\cdot\Delta T=R\cdot I^2\cdot \Delta T=2.5 mJ</math> per pulse. Since 2 stimulators were used, the limits were ten times lower than those stated in the IEC regulation.

The electrodes were arranged as described below. A common anode to the 2 electrostimulators was placed at the vertex, while a cathode electrode was placed on each side at 12-13 cm along the line joining the vertex to the acoustic meatus in the parietal region. The electrical stimulus consisted of a square wave lasting 250 μsec at a voltage of ≅ 300 V and maximum current of 100 mA.

The electrodes were arranged as described below. A common anode to the 2 electrostimulators was placed at the vertex, while a cathode electrode was placed on each side at 12-13 cm along the line joining the vertex to the acoustic meatus in the parietal region. The electrical stimulus consisted of a square wave lasting 250 μsec at a voltage of ≅ 300 V and maximum current of 100 mA.

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===Artifical neural network model===

===Artifical neural network model===

An artificial neural network (ANN) is a general mathematical computing paradigm by which the geometry and functionality of the ANN have been linked to the biological neural system and one of the most interesting characteristic of this paradigm is the self-learning propriety.

An artificial neural network (ANN) is a general mathematical computing paradigm by which the geometry and functionality of the ANN have been linked to the biological neural system and one of the most interesting characteristic of this paradigm is the self-learning propriety.