System for planning and/or providing neuromodulation

Abstract

The present invention relates to systems and methods for planning and/or providing neuromodulation. An example system includes a first data input module for stimulation related basic data, a second data input module for stimulation related response data, a transfer module configured and arranged such that the stimulation related basic data received by the data input module are linked and/or translated into and/or with the response data and/or artificial response data created by the transfer module, wherein the data generated by the transfer module are transfer data, the transfer data comprising link data and/or translation data and/or artificial response data, and a mapping module configured and arranged such that based on the stimulation related basic data and stimulation related response data and the transfer data a digital characteristic map is generated, which describes an interrelation between the stimulation related basic data and the stimulation related response data and the transfer data.

Claims

1. A system for planning and/or providing neuromodulation, especially neurostimulation, comprising: at least one processor; and at least one non-transitory memory storing instructions that, when executed by the at least one processor, cause the processor to perform the steps of: receiving stimulation related basic data by a first data input module; storing the stimulation related basic data in a stimulation related basic data storage module; obtaining stimulation related response data by a second data input module; storing the stimulation related response data to a stimulation related response data storage module; a transfer module configured and arranged such that linking and/or translating the stimulation related basic data received by the first data input module to the stimulation related response data and/or artificial response data generated by the transfer module; wherein data generated by the transfer module are transfer data, the transfer data comprising link data, translation data, or the artificial response data; storing the transfer data to a transfer response data storage module; and based on the stimulation related basic data, the stimulation related response data, and the transfer data, generating a digital characteristic map; wherein the digital characteristic map describes an interrelation between the stimulation related basic data, the stimulation related response data, and the transfer data; and wherein the digital characteristic map is configured to be automatically translatable using iterations to determine the design of the neuromodulation.

2. The system according to claim 1, wherein the mapping module is configured and arranged such that the digital characteristic map is generated automatically.

3. The system according to claim 1, wherein the stimulation related basic data comprise one or more of electrode data, stimulation characteristic data, patient data, stimulation data, and treatment application data.

4. The system according to claim 1, wherein the stimulation related response data comprise one or more of sequence of events data, motion data, electromyography (EMG) data, afferent signal data, efferent signal data, impedance data, electroencephalography (EEG) data, and brain control interface (BCI) data.

5. The system according to claim 1, wherein the transfer module is configured and arranged such that one or more of body posture data, static data, dynamic data, task data activity data, time data delay data, rehabilitation data, drug treatment data, data related to the voluntariness of movement, are used to generate the transfer data.

6. The system according to claim 1, further comprising a virtual mapping module, which is configured and arranged to generate the digital characteristic map virtually online.

7. The system according to claim 1, further comprising a correlation and/or simulation module, which is configured and arranged to correlate on the basis of digital characteristic map by way of simulation the stimulation related basic data and the stimulation related response data and the transfer data.

8. The system according to claim 7, wherein the correlation and/or simulation module are configured and arranged such that from a preselected stimulation related basic data the correlating stimulation related response data are identified.

9. The system according to claim 7, wherein the correlation and/or simulation module are configured and arranged such that from a preselected stimulation related response data the correlating stimulation related basic data are identified.

10. The system according to claim 1, further comprising a neuromodulation settings generation module, which is configured and arranged to translate the digital characteristic map into neuromodulation parameter settings for a neuromodulation treatment of a subject.

11. The system according to claim 10, wherein the neuromodulation settings generation module comprises a transfer interface, which is configured and arranged for transferring neuromodulation parameter settings from the system to a neuromodulation device.

12. A method for planning and/or providing neuromodulation, including neuro stimulation, comprising: linking and/or translating stimulation related basic data into and/or with response data and/or artificial response data to generate transfer data, the transfer data comprising link data and/or translation data and/or artificial response data, generating a digital characteristic map based on the stimulation related basic data, the stimulation related response data, and the transfer data, the digital characteristic map describing an interrelation between the stimulation related basic data, the stimulation related response data and the transfer data; and generating neuromodulation signals via a neuromodulation pulse generator based on the digital characteristic map.

Description

BRIEF DESCRIPTION OF THE FIGURES

(1) Further details and advantages of the present invention shall now be disclosed in connection with the drawings.

(2) It is shown in

(3) FIG. 1 a schematical overview of a possible embodiment for a system for planning and/or providing neuromodulation;

(4) FIG. 2 a (two-dimensional/2D) part of the obtained digital characteristic map;

(5) FIG. 3a-k several details of the anatomical structures to be stimulated and anatomical structures of interest;

(6) FIG. 4 the implantation procedure of a neuromodulation lead of a system according to FIG. 1, here in connection with the example of the implantation of a neuromodulation lead for a rodent (here a Lewis rat);

(7) FIG. 5 further steps of implanting an electrode array to the spinal cord;

(8) FIG. 6a the kinematics and the stimulation in the functional range on flexion stimulation for primates; and

(9) FIG. 6b the kinematics and the stimulation in the functional range on flexion stimulation for rodents.

SYSTEM DESCRIPTION

Detailed Description

(10) FIG. 1 shows as schematical overview of a possible embodiment for a system for planning and/or providing neuromodulation, here neurostimulation according to the present invention.

(11) The patient P is connected to the system 10.

(12) The system 10 comprises at least:

(13) a physiological response measurement sensor 12

(14) a physiological response measurement receiver and processor 14

(15) a computer 16

(16) a software 18

(17) a visualization module 20

(18) a neuromodulation lead 22 and neuromodulation pulse generator 24.

(19) The physiological response measurement sensor 12 and the physiological response measurement receiver processor 14 function as a first data input module 26 for stimulation related basic data.

(20) The computer 16 and the software 18 are connected to a storage being part of the computer 16.

(21) The storage S comprises a stimulation related basic data storage module 28 for storing the stimulation related basic data obtained by the first data input module 26 for stimulation related basic data.

(22) The stimulation related basic data may comprise at least one (or more or all) selected from

(23) electrode data, and/or

(24) stimulation characteristic data, and/or

(25) patient data, and/or

(26) stimulation data, and/or

(27) treatment application data.

(28) In the shown embodiment, the neuromodulation lead 22, the neuromodulation pulse generator 24, the physiological response measurement sensor 12 and the physiological response measurement receiver and processor 14 form also a second data input module 30 for stimulated related response data.

(29) The stimulation related response data are stored in a further stimulation related response data storage module 32, which is also part of the storage S.

(30) The stimulation related response data comprise data comprise at least one (or more or all) selected from

(31) sequence of events data, and/or

(32) motion data, and/or

(33) EMG (electromyography) data, and/or

(34) afferent signal data, and/or

(35) efferent signal data, and/or

(36) impedance data, and/or

(37) EEG (electroencephalograhy) data, and/or

(38) BCI (brain control interface) data.

(39) Moreover, the computer 16 comprises a transfer module 34.

(40) The transfer module 34 is configured and arranged such that the stimulation related basic data received by the data input module are linked and/or translated into and/or with the response data and/or artificial response data created by the transfer module 34, wherein the data generated by the transfer module 34 are transfer data, the transfer data comprising link data and/or translation data and/or artificial response data.

(41) The transfer module 34 may configured and arranged such that at least one kind of data selected from

(42) body posture data, and/or

(43) static and/or dynamic data, and/or

(44) task and/or activity data, and/or

(45) time and/or delay data, and/or

(46) rehabilitation data, and/or

(47) drug treatment data, and/or

(48) data related to the voluntariness of movement,

(49) is or are used to generate the transfer data.

(50) Moreover, there is a transfer response data storage module for storing the transfer data, which is also part of the storage S.

(51) Furthermore, the computer 16 comprises for creating a digital characteristic map 36 a mapping module 38.

(52) The mapping module 38 is configured and arranged such that based on the stimulation related basic data and the stimulation related response data and the transfer data digital characteristic map 36 is generated, which describes the interrelation between the stimulation related basic data and the stimulation related response data and the transfer data.

(53) The mapping module 38 may be configured and arranged such that the digital characteristic map 36 is generated automatically.

(54) The system 10 may further comprise a virtual mapping module 40, which is configured and arranged to generate the digital characteristic map virtually online.

(55) Moreover, the system 10 comprises a correlation and/or simulation module 42, which is configured and arranged to correlate on the basis of digital characteristic map by way of simulation the stimulation related basic data and the stimulation related response data and the transfer data.

(56) The correlation and/or simulation module is configured and arranged such that from a preselected stimulation related basic data the correlating stimulation related response data are identified. Also, from a preselected stimulation related response data the correlating stimulation related basic data may be identified.

(57) The system 10 further comprises a neuromodulation settings generation module 44, which is configured and arranged to translate the digital characteristic map into neuromodulation parameter settings for a neuromodulation treatment of a subject.

(58) Furthermore, the neuromodulation settings generation module 44 comprises a transfer interface 46, which is configured and arranged for transferring neuromodulation parameter settings from the system to a neuromodulation device, here the Neuromodulation Pulse Generator 24.

(59) The above system and process may be also set up as a self-learning or machine-learning process. Especially all kind of maps may be generated in a self-learning or machine-learning process.

(60) FIG. 2 shows in 2D a part of the obtained digital characteristic map 36, describing the interrelation between the stimulation related basic data and the stimulation related response data and the transfer data.

(61) On the x-axis the stimulation strength is shown.

(62) On the y-axis the muscle response is shown.

(63) In the digital characteristic map 36, two lines L1 and L2 describing the connection between the stimulation strength (i.e. stimulation related basic data) with the muscle response (stimulation related response data), wherein the connection can be seen as kind of a transfer function (i.e. stimulation related transfer data).

(64) The first line L1 is describing the stimulation response of a first muscle M1 and the dashed line L2 is describing the stimulation response for a second muscle M2.

(65) As can be seen, at a point of stimulation P1 muscle M1 starts to react.

(66) This point P1 is called motor threshold point or onset point.

(67) At this point P1, muscle M2 shows no reaction.

(68) Increasing the stimulation strength will result at some point in a saturation, this point being denoted as point P2, also called saturation point P2.

(69) This point P2, being the saturation point is defining the point at which no further stimulation will receive in stronger muscle activity of muscle M1.

(70) Thus, this point is called saturation point, as increasing the stimulation will not result in better stimulation results and muscle activity.

(71) As can be seen, at point P1′ a second muscle starts to react on the applied stimulation, however, at a lower level and with less activity. So, a specificity point P3 may be defined.

(72) The specificity point P3 defines a point, where muscle M1 shows relatively high response, whereas the response of muscle M2, which is also stimulated by the applied stimulation shows less activity, which is still at a level that can be accepted, as it is not really relevant.

(73) Also shown is the saturation point P2′ for muscle M2.

(74) FIG. 2 shows a part of digital characteristic map for example for a specific subset of electrodes of an electrode array that is placed in the vicinity of the spinal cord, for example to perform epidural electrical stimulation (EES). By already knowing the connection the placement of the electrodes vis-a-vis the spinal cord and the afferent sensory neurons, the necessary muscles or muscle groups needed for a specific movement can be addressed.

(75) When generating the digital characteristic map, the user is confronted with a plurality of degrees of freedom.

(76) Moreover, fast scans are limited by the response time of the muscles (approx. 2 s/0.5 hz).

(77) This will lead to long mapping times for generating the digital characteristic map.

(78) Thus, here optimization might be wanted.

(79) This can be done by optimizing the patients specific mapping procedure, i.e. finding the optimal stimulation settings for a given task.

(80) Therefore, the following options can be used alternatively or in combination:

(81) By applying specific search function instead of a current step-wise approach, the time consuming step-wise approach can be avoided. Possible approaches in connection with this search function approach are particle swarm, genetic, steepest gradient, optimization algorithms.

(82) A model fitting approach may be used. Here, a patient specific or generic model or the like may be used that predicts muscle response for a specific stimulation and uses the actual mapping to fine-tune and/or register and/or adapt this model to the individual/specific patient.

(83) There may be a data base of patients. Here iterative/machine learning methods may be used for mappings from previous patients to suggest (patient-specific) stimulation settings, probabilistic/statistics can be used, e.g. if one use those settings, then the probability of an effective stimulation may be a certain percentage X % and the crosstalk may be another certain percentage Y %.

(84) For the above three methods, certain quality indicators/optimization object functions may be used such as sensitivity index, cross-talk, muscle onset, muscle saturation or the like.

(85) The above three approaches may improve the generation of the digital characteristic map (the so called mapping procedure) by: reducing the mapping times creating patient specific optimum results potential reduction of the number of EMG's required, making the procedure easier and faster theoretically one can abandon the use of EMG's at all by fine-tuning of the used motion sensors.

(86) FIG. 3a-k show several details of the anatomical structures to be stimulated and anatomical structures of interest.

(87) FIG. 3a-e relates to the example of Rhesus monkeys.

(88) FIG. 3f-k relate to rodents, here Louis rats.

(89) FIG. 3a shows the anatomy of the lumbar spinal cord of a Rhesus monkey to be stimulated.

(90) Here this spatial organization of spinal segments of the Rhesus monkey in relation to the vertebrae is shown.

(91) FIG. 3b shows the dorsal roots trajectory.

(92) Here the 3D-dorsal roots' trajectory in relation to the lumbar spinal segment is shown.

(93) FIG. 3c shows the innervation of leg muscles, in particular the location of the motor neurons of the principle leg muscles in relation to the segments of the lumbar spinal cord.

(94) Shown are extensor muscles with the denotation EXT, flexor muscles with the reference sign FLEX and the articular muscles with the reference sign B.

(95) The muscles are denoted as follows:

(96) ST—SEMITENDINOSUS

(97) RF—RECTUS FEMORIS

(98) GLU—GLUTEUS MEDIUS

(99) GM—GASTROCNEMIUS MEDIALES

(100) FHL—FLEXOR HALLUCIS LONGUS

(101) IL—ILIOPSOAS

(102) TA—TIBIALIS ANTERIOR

(103) EDL—EXTENSOR DIGITORUM LONGUS.

(104) FIG. 3d shows the design of a selective array of electrodes of for example a neuromodulation lead 22.

(105) Here, the design of an epidural array in relation to the vertebrae and roots of the spinal cord is shown.

(106) FIG. 3e shows a polyamide-based realization.

(107) Here, the polyamide-based array and position in relation to the vertebrae is shown.

(108) FIG. 3f-k show respectively the corresponding drawings for rodents, here Lewis rats.

(109) In particular, it is shown in FIG. 3f the anatomy of the lumbar spinal cord of a rodent,

(110) FIG. 3g the dorsal roots trajectory,

(111) FIG. 3h the innervation of the leg muscles,

(112) FIG. 3i the design of the selective array, and

(113) FIG. 3k the polyamide-based realization.

(114) FIG. 4 shows the implantation procedure of the neuromodulation lead 22, here in connection with the example of the implantation of a neuromodulation lead for a rodent (here a Lewis rat).

(115) The implantation of a neuromodulation lead for other mammals like monkeys or human beings is similar.

(116) In step ST1 the needles are prepared.

(117) In step ST2 the EMG electrodes are prepared.

(118) In step ST3 a skull fixation is done.

(119) In step ST4 the lead wires are pulled.

(120) In step ST5 subcutaneous wire passage is prepared and provided.

(121) In step ST6 a dorsal position with leg fixed is performed.

(122) In step ST7 a skin opening is performed.

(123) In step ST8 a fascia opening is performed.

(124) In step ST9 the wires are subcutaneously pulled.

(125) In step ST10 the optimal spot is found.

(126) In step ST11 needles are passed through the muscles.

(127) In step ST12 wires are passed inside the needles.

(128) In step ST13 notes at wires extremity are provided.

(129) In step ST14 the fascia is provided with a suture.

(130) In step ST15 a suture to the skin is performed to close the implantation side.

(131) FIG. 5 shows further steps of implanting an electrode array to the spinal cord.

(132) In step ST100 the exposure of the vertebrae is done.

(133) In step ST110 laminectomies are done to expose the spinal cord.

(134) In step ST120 a preparation for the orthosis is done by using 4 screws.

(135) In step ST140 a tunneling of the connector is prepared and provided.

(136) In step ST 150 a ushape suture is provided for anchoring the electrode array of the neuromodulation lead 22.

(137) In step ST160 the array is pulled into the epidural space.

(138) In step ST170 a control position the array is done.

(139) In step ST180 a housing of the array is provided in the orthosis.

(140) In step ST190 a complete orthosis is performed by using dental cement. This orthosis is used for the rodents to support them during “walking”. It is not needed for other mammals like primates (e.g. monkeys or humans).

(141) In step ST200 a suture of the back muscles is provided to close the implantation side.

(142) In FIG. 6a the kinematics and the stimulation in the functional range on flexion stimulation for primates is shown. The corresponding relationship for rodents is shown in FIG. 6b.

(143) Method of Functional Mapping

(144) The method of functional mapping may be performed for example as follows:

(145) Evaluation of the spatial specificity of epidural arrays is achieved by simple electrophysiological testing. A single supra-threshold current pulse of EES, applied through an electrode contact at the lumbosacral level, produces mono- and poly-synaptic electromyographic responses in some leg muscles termed spinal reflexes (FIG. 6a and FIG. 6b).

(146) In particular, the mono-synaptic component of these responses, appearing at the lowest threshold, is related to the direct activation of the Ia afferent fibers. These fibers have excitatory synaptic connections to all the motoneurons of their homonymous muscle. Therefore, given the location of motoneuron pools in the spinal cord (cf. e.g. FIG. 3c and FIG. 3h) and which muscles are activated by single pulses (or low-frequency stimulation, e.g. 0.5-2 Hz) of epidural stimulation, it is possible to infer which roots are stimulated by each of the active sites. This procedure enables to estimate the region of the spinal cord that is preferentially targeted by a specific contact (cf. e.g. FIG. 6a and FIG. 6b).

(147) Indeed, the specificity of epidural arrays for spatiotemporal neuromodulation is not defined by the ability to stimulate single muscles, but rather by the recruitment of specific spinal segments innervating several muscles at the same time. Some antagonist muscles, such as the tibialis anterior and gastrocnemius medialis, may be partially innervated by roots emerging from the same segment. However, spinal circuits and interactions with residual descending control will gate the stimulation effects towards functionally relevant muscles during the execution of a specific movement. The excitability of agonist and antagonist muscles is modulated during gait, resulting in increased functional muscle specificity during movement (cf. e.g. FIG. 6a and FIG. 6b) compared to static measurements. This provides additional robustness in the positioning of the implants. During the implantation procedure, the ability to elicit spinal reflexes in the muscles innervated by the most rostral and the most caudal spinal segments innervating leg muscles (such as the Iliopsoas and Gastrocnemius Medialis respectively) ensures a correct longitudinal placement of the array and a full coverage of the entire lumbosacral spinal cord.

(148) Procedure

(149) Implantation of chronic electromyographic (EMG) electrodes and epidural spinal electrode arrays in rats and primates is done as shown in FIG. 4 and FIG. 5.

(150) For primates or humans the implantation of the neurostimulation lead is done likewise the implantation of electrode arrays for neurostimulation of the spinal cord in connection with pain treatment.

(151) After the implantation, the following exemplary steps for Intra-operative electrophysiology and finalization of the implantation procedure for the epidural array of the neuromodulation lead 22 are performed.

(152) The EMG electrodes are connected and the epidural array to the Real-Time electrophysiology unit.

(153) The system 10 set up to visualize on a monitor and store 50 ms of EMG signals triggered by each stimulation pulse delivered through the epidural array.

(154) Then, the neural stimulator with the neuromodulation pulse generator 24 and the neuromodulation lead 22 is set to current mode (voltage mode can also be used but is not preferred). The stimulation frequency may be chosen at e.g. 0.5 Hz. In general, a current range from 0 to 600 μA in rats and 0 to 5 mA in primates or humans at 200 μs pulse-width may be expected.

(155) After this, one may proceed by stimulating the most rostral sites to verify that the Muscle Evoked Potential of the iliopsoas in response to the epidural stimulation is recruited at lower threshold than the other leg muscles. Stimulation of the most rostral lumbar segments of the spinal cord should induce isolated hip flexion movements associated to each stimulation pulse when the stimulation is applied above motor threshold.

(156) In the next step it is continued by stimulating the most caudal sites to verify that the Muscle Evoked Potential of the Medial Gastrocnemius in both rats and primates (or another most caudally innervated muscle) in response to the epidural stimulation is recruited at lower threshold than other leg muscles. A current amplitude range from e.g. 0 to 300 μA in rats and 0 to 2 mA in primates or humans at 200 μs pulse-width for the stimulation of the caudal spinal cord may be expected. Stimulation of this region should induce isolated ankle dorsi-flexion movements associated to each stimulation pulse when the stimulation is applied above motor threshold.

(157) Then, the longitudinal position of the array may be adjusted by e.g. sliding it under the vertebra and previous steps may be repeated until both conditions are met.

(158) Following to this step/these steps, the medio-lateral positioning of the array is checked by verifying that stimulation of lateral sites at the same spinal level selectively recruits the muscles of the leg ipsilateral to the stimulation site at lower current levels than the muscles of the contralateral leg. The position of the array is adjusted by using the openings provided by the laminectomies at various spinal levels.

(159) Spatial Specificity: Post-Surgical Selection of Optimal Electrode Configurations

(160) Firstly, the epidural spinal stimulation system is set up. In rats, the headplug receiving the wires from the epidural electrode array is connected to to a multichannel stimulator controlled by a computer or real-time processor (e.g. RZ2 Bioamp Processor, Tucker-Davis Technologies). In primates or humans establishing communication with an Implantable Pulse Generator (IPG) (e.g. Activa RC, Medtronic). Communication occurs via a telemetry system consisting of an antenna linked to an interface worn by the animal and placed in a custom-made jacket. This interface should be able to transmit information wirelessly (e.g. by Bluetooth) to an external computer. Such systems with real-time communication capabilities do not readily exist as commercial system but can be used as investigational devices through collaborations with biomedical companies such Medtronic.

(161) Optionally, a video recording or motion capture system may be used to record the movements that will be induced by epidural stimulation (as described in the following point).

(162) The spatial selectivity of the electrode array is characterized following a procedure similar to that described on connection with the verification of the Muscle Evoked Potential of muscles of interest. The stimulation is set by selecting an electrode site and send single bipolar electrical pulses (200-μs pulse width) at a frequency of 0.5 Hz. The electrode site being tested is selected as the cathode (negative polarity).

(163) Then, the stimulation amplitude is manually increased from until a motor evoked potential is observed. A motor potential elicited by the stimulation should occur within about 3-8 ms in the rats and 5-15 ms in the primates after the stimulation pulse. Take note of the minimum intensity eliciting a motor potential as the motor threshold.

(164) The intensity is increased until the motor responses on all muscles saturate in amplitude and take note of the saturation amplitude.

(165) A recording of the EMGs is performed while systematically ramping up the stimulation amplitude from 0.9× the motor threshold found until the saturation amplitude found.

(166) The above steps are repeated for each electrode of the spinal implant, until muscle responses evoked by each of the electrode contacts are recorded.

(167) Optionally, a testing of additional multipolar electrode configurations may be performed. In the case in which leg specificity or muscle specificity is considered insufficient, multipolar configurations can be used to increase it. For example if all the electrodes on the left side of the array induce responses in both limbs, multipolar configurations may be tested with the cathode on the left side and the anode on the midline or on the right side in order to steer the activating field towards the desired limb. Likewise, if there is a lack of rostro-caudal selectivity, for example if the iliopsoas (most rostral muscle) is not specifically recruited by the most rostral electrodes, the cathode may be placed on the most rostral electrode and one or several anodes on the electrodes caudal to it.

(168) When all recordings are completed the local procedures defined for awakening and post-sedation care will be performed.

(169) Then, the recruitment curves and the digital characteristic are calculated and computed offline from the data obtained in the steps described above. Recruitment curves indicate the normalized level of activation of each muscle in response to single electrical pulses of increasing amplitude. The EMG activity is normalized by its maximum across all stimulation amplitudes and all stimulation sites. These recorded motor responses can also be translated into spatial maps of motoneuron pool activation, so-called spinal maps. From the recruitment curves, identify a functional range of stimulation amplitudes in which only the muscles activated at the lowest thresholds are significantly recruited. The spinal maps are computed corresponding to this functional range and use them to define the spatial specificity of each electrode configuration.

(170) By analyzing the computed spinal maps, the electrode configuration is determined that creates the highest activation in the spinal segments responsible for flexion of the leg, especially hip flexion (L1-L2 in rats during bipedal locomotion, L1-L2 in primates) and has unilateral responses over a wide range of amplitudes. This configuration is selected to promote global flexion of the leg. Similarly, the electrode configuration is determined that creates the highest activation in the spinal segments responsible for extension of the leg, especially ankle extension (L4-L6 in rats during bipedal locomotion, L6-L7 in primates) and has unilateral responses over a wide range of amplitudes. This configuration is selected to promote global extension of the leg

(171) Time Specificity: Determination of Stimulation Patterns

(172) The required timing for each type of stimulation is determined. Prior to the planned experiments, first EMG recordings of a few healthy individuals walking in the same conditions as used for the impaired subjects are performed. From these EMG recordings, the spatiotemporal maps (i.e. digital characteristic maps) of motoneuron activation during healthy locomotion are computed and determined. In rats and primates or humans, the analysis of these spinal maps will reveal that the spinal segments associated with flexion should be activated from the beginning of swing (‘foot off’) to the middle of swing. Similarly, the spinal segments associated with extension should be activated from the beginning of stance (‘foot strike’) to the middle of stance.

(173) Then, a system is set up, which is able to detect or predict in real-time the gait events necessary for spatiotemporal neuromodulation: “foot off”, “foot strike”, “mid-stance”, “mid-swing”. This system can be based on a real-time motion capture system in case there is residual voluntary motor control and if the animal can wear infrared-reflective markers or other types of motion sensors. Otherwise, the instantaneous motor state can be decoded from neural signals using intracortical microelectrode arrays, electro encephalograms (EEG) or implanted EEG (Ecog).

(174) Following to that, the sequence of stimulation bursts is programmed based on the detected gait events. In case all the detected events are sufficiently separated in time, all of them can be used to trigger the onset or the end of a particular set of stimulation bursts. However, if the stimulator can only accept stimulation commands up to a maximum rate and if the time interval between some consecutive events is too short to send two separate commands, an alternative solution is to pre-program the duration of the stimulation bursts. In this solution, the gait events only trigger the onset of stimulation, and the bursts are terminated automatically after a certain time has elapsed.

(175) In a further step, initial amplitudes and frequencies are selected. To start with this procedure, e.g. one can select a frequency of about 60 Hz for all electrode configurations used in the program defined above. For each electrode configuration, one can select an amplitude around 1.5 times the motor threshold obtained during recruitment curves. Closed-loop spatiotemporal neuromodulation may be tested with this set of parameters. The amplitudes may be adjusted based on kinematics and EMG activity. Each electrode configuration should have a significant effect on the targeted muscle group without loss of muscle specificity.

(176) The stimulation timing may be refined empirically. Alternatively, this can be done automatically with simulation tools or the like.

(177) One may anticipate or delay the onset of each stimulation burst and see if the effect on kinematics and EMG activity is improved. Kinematic effects can be quantified by looking at key variables such as step height or stride length, or by computing an exhaustive list of kinematic variables and using dimensionality reduction techniques such as Principal Component Analysis (PCA). Similarly, one may extend or reduce the duration of each stimulation burst and examine the effect on kinematics and EMG activity. The process may be iterated until an optimal set of parameters is found.

(178) Also, stimulation amplitudes and frequencies may be refined. The timing obtained in the previous step may be used. One may then re-adjust the amplitudes and frequencies. Each electrode configuration should have a significant effect on the targeted muscle group without loss of muscle specificity.

(179) Note that the example control and estimation routines included herein can be used with various neuromodulation and/or neurostimulation system configurations. The control methods and routines disclosed herein may be stored as executable instructions in non-transitory memory and may be carried out by the control unit in combination with the various sensors, actuators, and other system hardware in connection with a medical neurostimulation system. The specific routines described herein may represent one or more of any number of processing strategies such as event-driven, interrupt-driven, multi-tasking, multi-threading, and the like. As such, various actions, operations, and/or functions illustrated may be performed in the sequence illustrated, in parallel, or in some cases omitted. Likewise, the order of processing is not necessarily required to achieve the features and advantages of the example embodiments described herein, but is provided for ease of illustration and description. One or more of the illustrated actions, operations and/or functions may be repeatedly performed depending on the particular strategy being used. Further, the described actions, operations and/or functions may graphically represent code to be programmed into non-transitory memory of the computer readable storage medium in the control unit, where the described actions are carried out by executing the instructions in a system including the various hardware components in combination with a electronic control unit.

(180) Explicitly disclosed in connection with the above disclosure is the following aspect:

(181) 1. A method for planning and/or providing neuromodulation, including neurostimulation, comprising

(182) linking and/or translating stimulation related basic data into and/or with response data and/or artificial response data to generate transfer data, the transfer data comprising link data and/or translation data and/or artificial response data,

(183) generating a digital characteristic map based on stimulation related basic data and stimulation related response data and the transfer data, the digital characteristic map describing an interrelation between the stimulation related basic data and the stimulation related response data and the transfer data; and generating neuromodulation signals via an actuator based on the digital characteristic map.

(184) 2. The method according to aspect 1, wherein the method further comprises applying machine learning to generate the characteristic map.

REFERENCES

(185) 10 neuromodulation and/or neurostimulation system 12 physiological response measurement sensor 14 physiological response measurement receiver and processor 16 computer 18 software 20 visualization module 22 neuromodulation lead 24 neuromodulation pulse generator 26 first data input module 28 stimulation related basic data storage module 30 second data input module 32 stimulation related response data storage module 34 transfer module 36 digital characteristic map 38 mapping module 40 virtual mapping module 42 correlation and/or simulation module 44 neuromodulation settings generation module 46 transfer interface M1 first muscle M2 second muscle P patient P1 onset point P2 saturation point P3 specificity point P1′ onset point P2′ saturation point S storage