Skip to main content

Advertisement

Table 1 Features set exploited in the cases of velocity and force custom decoder

From: Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses

  Forces Velocities
Feature 1 \(Feat1\left( {t_{i}} \right) = - 1 + \frac{1}{{\left( {AFR\left( {t_{i} } \right) - 1.5} \right)^{2} }}\) \(Feat1\left( {t_{i}} \right) = \frac{{AFR\left( {t_{i} } \right) - AFR\left( {t_{min} } \right)}}{{t_{min} - t_{i} }}\)
Feature 2 \(\left\{ {\begin{array}{*{20}c} {1, \wedge Feat1\left( {t_{i} } \right) \ge T_{u1}} \\ {0, \wedge Feat1\left( {t_{i} } \right) < T_{u1}} \\ \end{array} } \right.\) \(\left\{ {\begin{array}{*{20}c} {1, \wedge Feat1\left( {t_{i} } \right) \ge T_{u1}} \\ {0, \wedge Feat1\left( {t_{i} } \right) < T_{u1}} \\ \end{array} } \right.\)
Feature 3 \(\left\{ {\begin{array}{*{20}c} {1, \wedge Feat1\left( {t_{i} } \right) \le T_{l1}} \\ {0, \wedge Feat1\left( {t_{i} } \right) > T_{l1}} \\ \end{array} } \right.\) \(\left\{ {\begin{array}{*{20}c} {1, \wedge Feat1\left( {t_{i} } \right) \le T_{l1}} \\ {0, \wedge Feat1\left( {t_{i} } \right) > T_{l1}} \\ \end{array} } \right.\)
  1. The choice of the exponential of the AFR in the case of force decoding was driven by the fact that the FR-force relation (Eq. 9) approximates a logarithmic profile. ti represents the time instant. AFR = (FRs average). \(t_{min}\) represents the minimum firing rate identified before subjects’ activity. Tu1, Tl1 are thresholds chosen to identify the intervals in which the neuronal activity is high (as during the execution of motions) or low (as during rest). In particular they are used by the decoder to identify the time points in which giving a prediction output