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Fig. 5 | BioMedical Engineering OnLine

Fig. 5

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

Fig. 5

Neural activity correlates with grasping force. a Normalized average of all the single-unit firing rates, flexor digitorum sEMG, and pressure sensor readout. All the values are expressed as mean ± SEM. b Relation between the force exerted during the grasps and the motoneuron firing rate. The maximum of the two parameters is computed, corresponding to the reaching phase of the grasp. The fitting function described by Eq. (9) is in red (R2 = 0.74). c Force-FR relationship. The two parameters are computed as average over the interval in which the sensor readout is stationary (holding phase). Pressure sensor readout and motoneuron firings are normalized to their maximum. The fitting function described by Eq. (9) is in red (R2 = 0.6). In b, c data are bootstrapped. Signals in (ac) are extracted from Subject 2. d Barplot representing the FRs normalized to their maximum in the grasps, with respect to the variation of the exerted force, for all the subjects, during reaching phase. Colored circles represent the subjects. Data are represented as mean ± SD. In red the fitting function (R2 = 0.97) expressed by Eq. (9). e Barplot describing the relationship between force and motoneuron firing rate for all the grasps and subjects during holding phase. Colored circles represent the subjects. Data are represented as mean ± SD. The overall behavior of motoneurons is described by Eq. (9), in red (R2 = 0.93). Data in (d, e) are the average of 30, 34, 26, 31, 33, 21 trials (selected as in “Methods”) × 13, 14, 11, 13, 16, 14 sorted neurons respectively for Subj. 1, Subj. 2, …, Subj. 6

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