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1 Prediction of Individual Finger Forces Based on Decoded Motoneuron
2 Activities3
4 CHENYUN DAI,1,2 YIZHOU CAO,1,2,3 and XIAOGANG HU1,2
5 1Joint Department of Biomedical Engineering, University of North Carolina - Chapel Hill, 144 MacNider Hall, Chapel Hill,6 NC 27599, USA; 2North Carolina State University, Raleigh, NC, USA; and 3Medical College of Soochow University, Suzhou,7 Jiangsu, China
8 (Received 2 October 2018; accepted 28 February 2019)
9 Associate Editor Jane Grande-Allen oversaw the review of this article.10
11 Abstract—Accurate prediction of motor output based on12 neural signals is critical in human–machine interactions. The13 objective was to evaluate the performance of predicting14 individual finger forces through an estimation of the15 descending neural drive to the spinal motoneuron pool.16 High-density surface electromyogram (EMG) signals of the17 extensor digitorum communis muscle were obtained, and18 were then decomposed into individual motor unit discharge19 events. The frequency of the composite discharge events at20 the population level was used to derive the descending neural21 drive, which was then used to predict the finger forces. The22 global EMG-based approach was used as a control condi-23 tion. Compared with the EMG-based approach, the exper-24 imental results show that the neural-drive-based approach25 can better predict the individual finger forces with higher R2
26 values across different force levels and across different force27 trajectories (steady and varying forces). These findings28 indicate that the neural drive estimation based on motoneu-29 ron firing activities can be used as a reliable neural-machine30 interface signal involving individual fingers. However, real-31 time implementation of this approach is needed for future32 clinical translation.
33 Keywords—EMG signal processing, Motor unit decomposi-
34 tion, Finger force, Hand function, Muscle activity.35
36 ABBREVIATIONS
37 MU Motor unit
38 MUAP Motor unit action potential
39 EMG Electromyogram
40 sEMG Surface electromyogram
41HD High density
42EDC Extensor digitorum communis
43MVC Maximal voluntary contraction
44SNR Signal to noise ratio
45ANOVA Analysis of variance
4647
4849INTRODUCTION
50Decoding the desired motor output is a key com-
51ponent in human–machine interactions. This decoded
52signal can allow individuals with neuromuscular dis-
53orders to interact with machines, such as exoskeletons,
54prostheses, or neural-stimulation systems, which can
55help restore lost or diminished motor functions. Re-
56cent advancement in neurally implantable micro-elec-
57trodes or thin-film electrode arrays have facilitated the
58ability to decode neural signals sent directly from the
59brain to the periphery.2 However, there is still con-
60siderable challenge in applying these techniques to
61humans, specifically in clinical populations, largely due
62to the invasive nature of the procedures and the lack of
63long-term stability of the system interface.3
64Alternatively, non-invasive muscle activity record-
65ings, such as surface electromyogram (sEMG) signals,
66have been widely used as the neural control signals of
67human wearable robots or neuroprostheses.1,21 One
68common strategy uses the amplitude of sEMG as a
69proportional control input of different degrees of
70freedom of the machine.9 Another typical strategy uses
71different features/patterns of EMG to identify different
72preset movements involving multiple degrees of free-
73dom.25
74Nevertheless, these global EMG-based approaches
75have several limitations. First, the global sEMG sig-
Address correspondence to Xiaogang Hu, Joint Department of
Biomedical Engineering, University of North Carolina - Chapel Hill,
144 MacNider Hall, Chapel Hill, NC 27599, USA. Electronic mail:
xiaogang@unc.edu
Annals of Biomedical Engineering (� 2019)
https://doi.org/10.1007/s10439-019-02240-1
BIOMEDICALENGINEERING SOCIETY
� 2019 Biomedical Engineering Society
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76 nals consist of the summation of hundreds of motor
77 unit action potentials (MUAPs) generated from
78 motoneuron discharge events, it may not accurately
79 reflect the cortical control of muscle activation.
80 Namely, the measured EMG amplitude can be biased
81 by intrinsic physiological factors, such as the cancel-
82 lation of superimposed MUAPs20 and the natural
83 variations of action potential amplitude from the
84 complex conductive processes of different tissue lay-
85 ers.11 Second, EMG signals can be further deterio-
86 rated/corrupted by external interference during signal
87 acquisition, such as changes in electrode–skin contact,
88 the crosstalk of multi-channel EMG,10 motion arti-
89 facts, and the variation of baseline noise. All these
90 factors can ultimately limit robust communications
91 between humans and machines.
92 In contrast, recent studies have used motoneuron
93 discharge timings as a promising neural control input
94 for human–machine interactions.12,27 Instead of using
95 the global sEMG signals, a decomposition step was
96 performed on the multiple-channel sEMG signals to
97 extract spinal motoneuron discharge events. Although
98 individual motoneurons have distinct discharge prop-
99 erties that may not directly reflect the descending drive,
100 the frequency/probability of the motoneuron discharge
101 at the population level can directly reflect high level
102 neural drive from the brain to the muscles. Essentially,
103 the spinal cord output signal (motoneuron discharge
104 spikes as a whole) is used to estimate/decode the spinal
105 input signal (descending command from the brain).
106 This decoded neural command based on the binary
107 events can potentially overcome some of the disad-
108 vantages of the global EMG patterns. However, the
109 feasibility of using this approach for fine motor con-
110 trol, such as involving individual finger movement has
111 not been evaluated. In addition, the decoding accuracy
112 of the neural-drive-based approach compared with the
113 EMG-based method has not been fully investigated
114 under different conditions (i.e., force level, signal
115 quality, or signal stability). To overcome these limita-
116 tions, the objective of this study was to evaluate the
117 performance of the neural-drive-based approach in
118 estimating the individual finger forces using both
119 simulated and experimental data under different con-
120 ditions, with a wide range of signal quality and muscle
121 contraction levels.
122 MATERIALS AND METHODS
123 Both simulated and experimental data were
124 obtained to verify the parameter selection and the
125 accuracy of the methods. Figure 1 shows the process of
126 the EMG-based (Fig. 1a) and the neural-drive-based
127 (Fig. 1b) estimations.
128Simulated Data
129A classic EMG model22 was used to generate the
130EMG data, in order to evaluate the accuracy of the
131methods. The signal generation were described as fol-
132lows:
133First, the individual spike trains were generated from
134awidely accepted physiologically-basedMUmodel.13A
135progressive recruitment pattern with an exponential
136threshold function and a distributed discharge rate of
137MUs were modeled as described in the original study.13
138The main parameters used were summarized in Table 1.
139Firing variations with a 10%Coefficient of Variation in
140the firing ratewere added to the spike trains. In addition,
141a moderate level (10%) of MU discharge synchroniza-
142tion was added based on Yao et al.29
143Second, for each spike train, waveforms of HD
144MUAP was randomly selected from a HD MUAP
145pool. The MUAP pool was derived from earlier
146experimental data6 using OT Biolab (OT Bioelettron-
147ica, Torino, Italy). The EMG signals were first
148decomposed into constituent spike trains, and the
149corresponding waveforms of HD MUAPs were
150derived from a spike trigger average algorithm.16 In
151addition, a 10% amplitude variation was added to the
152MUAPs for each firing. The MUAP grid was also
153scaled by a coefficient from a uniformly distributed
154random number ranging from 0.1 to 2.
155Third, each HD MUAP array was convolved with
156the corresponding spike train to generate an HD
157MUAP train grid. Then, the 8 9 8 noise free EMG
158was obtained by superimposing all the MUAP train
159grids from different MUs. Additional Gaussian noise
160(band-pass filtered at 10–900 Hz) with different signal-
161to-noise ratio (SNR = 5, 10 and 20 dB) was added.
162Fourth, the twitch force of each MU was also sim-
163ulated. The MU force and the resultant muscle force
164was subsequently calculated based on the excitation
165drive level and the MU discharge frequency using the
166force model described in the same MU model.13 The
167main parameters used for the force simulation was
168summarized in Table 2.
169To match the conditions of experimental data, two
170excitatory drive levels (20% and 50%) were simulated.
171The variation of the drive level was similar to that of the
172experimental data, including both sine-wave and trape-
173zoid. A total of 600 trials (two force levels 9 two trajec-
174tories 9 three SNRs 9 fifty repetitions) were simulated.
175Experimental Data
176Participants
177Nine neurologically intact (with no known neuro-
178logical injuries or disorders) subjects (six males, two
179females; aged 26.3 ± 4.9 years) were recruited. One
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180 was excluded due to a short forearm length. Therefore,
181 the experimental data were acquired from eight sub-
182 jects. A power analysis was performed to ensure that
183 eight subjects were not under powered. All subjects
184 provided written informed consent approved by the
185 Institutional Review Board at the University of North
186 Carolina at Chapel Hill.
187Experimental Setup
188The subjects sat in a height-adjustable chair with
189their dominant forearm comfortably placed on the
190experimental table and the elbow supported on a foam
191pad. Given that a large electrode pad was placed on
192their forearm, also covering the wrist extensor, the
FIGURE 1. Diagram of the EMG-based (a) and neural-drive-based (b) approaches. The diagram of the motor unit decompositionand neural drive estimation (c).
TABLE 1. The main parameters used for MU timing eventtrain generation.
Parameter Value
Total number of neurons in the pool 120
Range of recruitment threshold 30
Coefficient of recruitment threshold ln 30/120
Minimum firing rate 5
Maximum firing rate of first MU 35
Maximum firing rate of last MU 20
TABLE 2. The main parameters used for the forcegeneration.
Parameter Value
Range of peak twitch force (RP) 100
Coefficient of peak twitch force (ln RP)/120
Range of contraction time (RT) 3
Coefficient of contraction time logRT RP
Longest duration rise time 90 ms
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193 subjects were asked to minimize the wrist extensor
194 activation. To restrict the wrist movement, their wrist
195 was immobilized within two padded wooden boards in
196 a neutral pronation-supination position and zero de-
197 gree flexion–extension. The four fingers (index, middle,
198 ring and little) were comfortably abducted. Each finger
199 was secured, using two Velcro straps (each at the
200 proximal and middle phalanges) to one load cell (In-
201 terface, SM-200N, with a resolution of 0.01 N) which
202 measured the finger forces with a 1 kHz sampling
203 frequency. During the experiment, the subjects were
204 asked to isometrically extend one designated finger in
205 each trial. The maximum voluntary contraction
206 (MVC) of each finger was separately measured. The
207 average force of each finger during the contraction
208 plateau of 2 s was calculated as the MVC.
209 During the main testing session, the subjects were
210 provided with a target force trajectory shown on a
211 screen. The finger force output was instantaneously
212 displayed as a feedback to the subjects via a custom-
213 built MATLAB program (MathWorks Inc). Only the
214 designated finger force was displayed, but the forces
215 from all the four fingers were recorded. They were
216 asked to adjust the displayed muscle force of a desig-
217 nated finger to track the trajectory. Two force target
218 trajectories, sine-wave and trapezoid, were tested. The
219 sine-wave force trajectory allowed us to evaluate the
220 force estimation performance of the EMG- and neural-
221 drive- based approaches during varied level of con-
222 tractions. Two contraction force levels (20% and 50%
223 MVC) were used in each target. For the trapezoid
224 force, the subject took 2 or 4 s (2 s for 20% or 4 s for
225 50%) to ramp up to the designated maximum effort,
226 maintained the force for 10 s, and lastly used 2 or 4 s
227 (2 s for 20% or 4 s for 50%) to ramp down to zero
228 effort. For the sine-wave force, the subject took 2 or
229 4 s to ramp up to the designated effort, maintained for
230 5 s, then performed three repeated sine-wave oscilla-
231 tion forces either from 10 to 20% or from 25 to 50%,
232 and finally used 2 or 4 s ramp down to zero effort.
233 Three repeated trials were performed for each condi-
234 tion. The order of the different conditions were ran-
235 domized during the experiment. A minimum of 2 min
236 rest was provided between trials to reduce the influence
237 of muscle fatigue. A total of 48 trials (four fin-
238 gers 9 two force levels 9 two trajectories 9 three
239 repetitions) were recorded for each subject.
240 EMG Recordings
241 Surface EMG signals were recorded over the
242 extensor digitorum communis (EDC) muscle via an
243 8 9 16 channel high density (HD) EMG electrode ar-
244 ray (see Fig. 1c) with 10 mm inter-electrode distance
245 (OT Bioelettronica, Torino, Italy). Each electrode was
246filled with conductive gel to ensure high conductivity
247between the electrodes and the skin surface. Prior to
248the electrode placement, the skin surface was scrubbed
249with alcohol pad to improve the signal quality. To
250standardize the electrode placement location across
251subjects, the HD array was centered at the midway
252between the olecranon process and the styloid process.
253The EMG signals were acquired from EMG_USB2+
254(OT Bioelettronica, Torino, Italy), sampled at 2048 Hz
255with a gain of 1000 and a bandwidth of 10–900 Hz.
256EMG Analysis
257Channel selection was first performed based on
258several previous studies.7,18,28 EMG activities from
259only a localized region of the extensor muscle can be
260obtained during individual finger extension. Therefore,
261a large number of the EMG channels contained pri-
262marily baseline noise. To remove the channels with no
263EMG information, only the EMG channels (64 out of
264128 channels) in proximity to the regions with EMG
265activities for individual fingers were selected for the
266analyses. Specifically, based on the muscle activation
267region of individual fingers, channels from row 1–8, 5–
26812, and 9–16 were used for index, middle, ring, and
269little fingers, respectively.
270EMG Decomposition and Neural-Drive-Based Estima-
271tion
272The EMG signals were decomposed into individual
273MU discharge spike trains using the fast independent
274component analysis (FastICA) method.19 Briefly, the
275algorithm includes four steps: (1) signal extension by
276adding eight delayed replicas of each original channel,24
277(2) signal whitening, (3) FastICA-based deconvolution,
278and (4) action potential timing identification through
279peak detection and clustering. The details of the algo-
280rithmand the parameter selection are described byNegro
281et al.24 Lastly, the silhouette measure (SIL), a separation
282index inclusteranalysis,wasused tofilter thoseMUswith
283low accuracy. Previous literature has shown that the
284decomposed MUs with larger SILs tended to have a
285higher accuracy.24 In the current study, MUs with SIL
286larger than 0.8 were accepted for further analysis.
287During decomposition, the algorithm can converge
288to the same source signal multiple times. To remove
289the duplicated MUs, a post-processing step was per-
290formed. If a pair of MU firing trains had > 50% of
291synchronized firing events within ± 1 ms after adjust-
292ing the time delay, the MU firing train with a lower
293SIL was removed. All the processed firing events trains
294were then pooled into a composite train, and the dis-
295charge rate of the composite firing event train was
296calculated (Fig. 1c).
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297 EMG-Based Estimation
298 The EMG-amplitude-based feature is widely used
299 for EMG-to-force prediction.23 The root-mean-square
300 of EMG has also been verified as the best EMG-am-
301 plitude feature especially at high effort levels (> 25%
302 MVC).5 Therefore, the root-mean-square of EMG was
303 used to estimate the force for experimental data as a
304 control condition compared with the neural-drive-
305 based estimation. Raw EMG signals were band-pass
306 filtered to reduce the influence of motion artifacts and
307 high frequency background noise (4th Butterworth
308 with a cut-off frequency at 50 and 500 Hz),23 and
309 notch filtered to eliminate the power-line interference
310 (2nd IIR filter at 60 Hz with a bandwidth of 1 Hz).
311 The root-mean-square values of all the 8 9 8 pro-
312 cessed EMG signals used for decomposition were cal-
313 culated, and were then averaged across all the
314 channels.
315 A 350 ms moving window with a moving step of
316 50 ms was used in both the neural-drive-based and
317 EMG-based approaches. The force prediction was
318 performed using a linear regression method. Specifi-
319 cally, a single trial from trapezoid condition was used
320 to calculate the regression coefficients from the neural-
321 drive-based or EMG-based estimate for each force le-
322 vel. The remaining trials were then used to evaluate the
323 force prediction. A cross-validation were also per-
324 formed such that any single trial was used for the
325 regression coefficient calculation, and the remaining
326 trials were used for evaluation/testing. Only the eval-
327 uation results were reported. The performance of the
328 two estimation approaches was evaluated by calculat-
329 ing the R2 values between the estimated values (EMG-
330 based or neural-drive-based approaches) and the ac-
331 tual force output. Prior to the R2 calculation, the time
332 series of the neural-drive-based or EMG-based esti-
333 mations were adjusted with the force recordings to
334 account for the neural-mechanical delays, using a
335 cross-correlation analysis.8
336Statistical Analysis
337Standard errors were used for all the figures, and
338standard deviations of the data were used for all other
339data analyses. The performance differences were tested
340using a repeated measures analysis of variance (AN-
341OVA) in SPSS 24 (IBM). Since the R2 values were
342bounded at 1, an arcsine-square-root transformation
343was performed to satisfy the normal distribution
344assumption of the ANOVA and regression analysis.
345For the simulated data, the performance of the neural-
346drive-based approach was examined on three factors
347[force level 9 trajectory 9 SNR]. For the experimental
348data, the evaluation was examined on four factors
349[force level 9 trajectory 9 finger 9 estimation meth-
350od]. Post hoc pairwise comparisons with Bonferroni
351corrections were conducted when a significance was
352found. A significance level of p < 0.05 was used.
353RESULTS
354Simulation Results
355The overall decomposition results (both accuracy
356and number of MUs detected) are shown in Table 3.
357Overall, the decomposition accuracy and yield revealed
358similar pattern across force trajectory. As the force
359level increased and the SNR decreased, the decompo-
360sition accuracy decreased from 97.33 ± 0.27% (the
361least challenging condition: 20% trapezoid shape at 20
362SNR) to 68.90 ± 0.76% (the most challenging condi-
363tion: 50% trapezoid shape at 5 SNR). Similarly, the
364corresponding number of MUs detected also dropped
365from 19.00 ± 0.24 to 11.92 ± 0.21.
366After decomposition, discharge events from all the
367MUs with SIL > 0.8 were pooled to estimate the
368neural drive. The sample time-series plots in Figs. 2a
369and 2b show that the estimated neural drive can largely
370match the variation of the simulated muscle force. The
371overall estimation results are summarized in Fig. 3.
372The R2 values varied from 0.90 to 0.96 across different
TABLE 3. The overall results of decomposition.
5 SNR 10 SNR 20 SNR
Sine 20% 78.90 ± 0.82% 88.32 ± 0.60% 95.32 ± 0.37%
(15.14 ± 0.21) (15.60 ± 0.19) (18.90 ± 0.24)
Sine 50% 71.49 ± 0.83% 81.26 ± 0.64% 92.58 ± 0.43%
(11.92 ± 0.21) (16.02 ± 0.20) (18.86 ± 0.27)
Trapezoid 20% 79.06 ± 0.84% 89.25 ± 0.67% 97.27 ± 0.27%
(15.36 ± 0.23) (15.66 ± 0.19) (19.00 ± 0.24)
Trapezoid 50% 68.73 ± 0.78% 79.61 ± 0.63% 92.18 ± 0.40%
(12.40 ± 0.27) (16.06 ± 0.20) (18.80 ± 0.21)
The accuracy (mean ± standard error) are shown. The numbers of motor units (MUs) detected are shown in brackets.
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373 conditions. A three-way repeated measures ANOVA
374 [force level 9 trajectory 9 SNR] showed that all the
375 three main factors (p < 0.05) had significant differ-
376 ences. Further post hoc comparisons revealed that the
377 estimate became less accurate as the SNR decreased or
378 the force level increased, largely due to a decline of the
379 decomposition accuracy. In addition, the sine-wave
380 trajectory and lower force level showed higher R2
381 values.
382 To quantify the sensitivity of the R2 to the decom-
383 position performance, Fig. 4 illustrates the relation
384 between the R2 of the drive estimation and the
385 decomposition performance (the accuracy and the
386 number of MUs detected) after pooling all 600 simu-
387lated trials. The results indicated that a higher per-
388formance in both the accuracy and yield led to a higher
389R2 value. The decomposition accuracy seemed to be
390more critical in that a small number of MUs can lead
391to an accurate neural drive estimation, as long as the
392discharge timings were accurate. In contrast, a large
393number of inaccurate discharge timings can still lead to
394relatively low R2 values.
395Experimental Results
396A linear regression method was used to predict the
397muscle force. Figure 5 shows two representative
398examples of the time-series of EMG and neural drive,
FIGURE 2. Example time-series plots of two different drive shapes for simulated data. (a) Illustration of the detaileddecomposition results. The red trace illustrates one channel of EMG with the largest root-mean-square value. Each blue barrepresents one discharge event. (b) Sine wave. (c) Trapezoid.
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399 in comparison with the force output. The neural-drive-
400 based approach for both examples revealed better
401 performance compared with the EMG-based estima-
402 tion. The relation between the SNR and the R2 was
403 examined on both estimations across all the trials. A
404 robust linear regression was performed to quantify the
405 strength of the correlation (Fig. 6). Based on the
406 regression results, the R2 of the EMG-based approach
407 tended to decline as SNR increased (R = 0.32,
408 p < 0.05). However, the neural-drive-based approach
409 showed a weaker association with SNR (R = 0.19,
410 p < 0.05).
411 Four separate two-way (finger 9 estimation method)
412 repeated measures ANOVAs were performed on each
413 condition (each subplot in Figs. 7a–7d). The detailed
414 statistical outcomes are summarized in Table 4. Most
415 of the results showed that the estimation method was
416 significant, except for trapezoid 50% condition
417 (p > 0.05). The values of power analysis for the sig-
418nificant factors of the four two-way ANOVAs ranged
419from 0.80 to 0.95. Post hoc comparisons found that the
420neural-drive-based approach always had higher R2
421values than the EMG-based method for all the fingers.
422Since no significant difference was observed in the
423finger factor and no interaction was found, the mar-
424ginal mean values were calculated by averaging the R2
425values of four fingers for other different conditions
426(trajectory, force level, and estimation method) as
427shown in Fig. 7e. The R2 values ranged from 0.83 to
4280.92 for the neural-drive-based estimation, and from
4290.73 to 0.88 for the EMG-based estimation.
430A three-way (trajectory 9 force level 9 estimation
431method) repeated measures ANOVA was performed on
432R2 values. The results showed a significant difference for
433trajectory [F(1,7) = 14.355, p < 0.05], and a significant
434interaction [F(1,7) = 19.287, p < 0.05] between esti-
435mation method and force level. The values of power
436analysis of the three factors (trajectory, force level, and
437estimation method) are 0.92, 0.88, and 1, respectively.
438The sine-wave trajectory showed higher R2 values than
439the trapezoid trajectory (p < 0.05). Further Post hoc
440analysis were tested between the two estimation methods
441and the twodifferent force levels. For each force level, the
442neural-drive-based approach always had higher R2 val-
443ues than the EMG estimate (p < 0.05). For the EMG-
444based estimation method, 50% force level had higher R2
445values than the 20% force level (p < 0.05).
446DISCUSSION
447In this study, the feasibility of using motoneuron
448discharge events to estimate the individual finger forces
449was investigated. The simulation results showed that
450the decomposition accuracy was sensitive to the signal
451quality (SNR) and the number of active MUs in the
452signals (associated with the level of excitation), and
453that the decomposition yield was only sensitive to the
454signal quality. The experimental results showed that
455the neural-drive-based approach was consistently bet-
456ter than the EMG-based approach in estimating mus-
457cle forces across different conditions (individual finger,
458force level, and force trajectory). Overall, the superior
459performance of the neural-drive-based estimation of
460individual finger forces offers a promising neural
461interface signal for intuitive and robust control of
462rehabilitative/assistive techniques that can help restore
463individual finger movement.
464Implications of Simulation Results
465Given that the ground-truth of the neural drive
466(input) and MU firings (output) are known in the
467simulation, a direct evaluation on the performance of
FIGURE 3. The R2 values of the simulation results indifferent conditions. The bars represent the standard errors.The asteroids represent significant differences (p < 0.05).
FIGURE 4. The influence of the decomposition performance(accuracy and the number of MUs detected) on the R2 of theneural-drive-based estimation. The color bar shows thevalues of R2s. Note: the map was interpolated linearly threetimes just for visual presentation.
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468 the neural-drive-based estimation in relation to the
469 performance of the MU decomposition was per-
470 formed. The descending neural drive can be estimated
471 from the discharge frequency/probability of the MU
472 pool. In order to reliably estimate the neural drive,
473 accurate discharge timings from a certain number of
474 MUs sampled from the MU pool are required to reflect
475 the pool behavior. Based on the simulation results,
476 both the accuracy and yield of the decomposition in-
477 deed affect the neural-drive-based estimation, in that a
478 lower decomposition accuracy and yield can lead to
479 inaccurate force estimations. However, the degree of
480influence of the decomposition accuracy and yield on
481neural drive estimation differs, with decomposition
482accuracy being the more sensitive variable. Specifically,
483if the decomposition accuracy is above approximately
48485%, a large range of decomposition yield (as low as 8
485MUs) can lead to an accurate estimation of the neural
486drive. In contrast, if the decomposition accuracy is low
487(below 80%), a large number of MUs (~ 15) would be
488needed to derive an accurate neural drive.
489On the other hand, the performance showed
490improvement as the number of MUs increased. This
491effect can arise from several factors associated with the
FIGURE 5. Example time-series plots of two different drive trajectories for experimental data. (a) Illustration of the detaileddecomposition results. The red trace illustrates one channel of EMG with the largest root-mean-square value, and thecorresponding waveforms of motor unit action potentials (MUAPs). Each blue bar represents one discharge event. SIL is thesilhouette measure. (b) Sine wave. (c) Trapezoid.
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492nature of the decomposition errors. First, the neural
493drive estimate is based on the population firing
494behavior of composite spike trains from multiple MUs.
495Even though the timing of discharge in individual MUs
496may be inaccurate due to an incorrect random shift in
497discharge timings, a comparable number of false pos-
498itive and false negative errors from individual MUs can
499be averaged out in the average window. Second, the
500decomposition error can also arise from incorrect
501assignment of the discharge events. For instance, the
502spike of a MU is incorrectly assigned to a different
503MU, which would lead to two errors. However, the
504errors are cancelled out in the composite spike train,
505and would not affect the neural drive estimations.
506Nevertheless, to ensure an accurate estimation of the
507neural drive, a high decomposition accuracy is neces-
508sary and can provide confidence on the neural drive
509estimations.
FIGURE 6. The relation between SNR and R2 for bothapproaches. Each circle represents one individual trial.
FIGURE 7. (a)–(d) The mean values of the two estimates in different conditions. (e) The marginal mean values for the twoestimates on different conditions after averaging across different fingers. The bars represent the standard errors. The asteroidsrepresent significant differences (p < 0.05). The average SNR of the EMG signals in each condition are also shown.
TABLE 4. The summary of four two-way (finger 3 estimation method) repeated measures ANOVAs.
Condition Finger Estimation method
Sine 20% F(3,21) = 0.095, p = 0.962 F(1,7) = 12.590, p= 0.009
Sine 50% F(3,21) = 1.777, p = 0.182 F(1,7) = 10.677, p= 0.014
Trapezoid 20% F(3,21) = 2.837, p = 0.063 F(1,7) = 17.733, p= 0.004
Trapezoid 50% F(3,21) = 2.307, p = 0.106 F(1,7) = 2.823, p = 0.137
The values are marked in bold when significance was found.
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510 Consistent with earlier studies,4,24 the simulation
511 results also showed that the SNR of the EMG signals
512 and the force level (i.e., the number of MUs in the
513 EMG signals) can affect the decomposition results. A
514 large number of MUs can increase the degree of
515 superposition and pose challenges to the source sepa-
516 ration. The simulation results can provide a reference
517 for the decomposition of experimental data. Namely, a
518 high SNR and a low-to-moderate effort is desirable in
519 order to ensure accurate decomposition results.
520 Implications of Experimental Results
521 The experimental results show that the neural-drive-
522 based approach was consistently superior to the EMG-
523 based approach for force estimation. The neural-drive-
524 based approach is not affected by different MUAP
525 features, such as amplitude, duration, or cancellation
526 due to superposition, as in the EMG-based approach.
527 For example, at low contraction levels, the EMG sig-
528 nals tend to be sparse, and the EMG-based approach
529 has large estimation biases, which could be even more
530 prominent in clinical populations, because the EMG
531 signals are often sparse and unstable due to disordered
532 control of MU activations.14,17
533 In addition, external factors, including high baseline
534 noise or variations in electrode–skin contact, can fur-
535 ther decrease the reliability of the EMG-based
536 approach. Although the simulation results show that
537 SNR can affect the estimation performance of the
538 neural-drive-based approach to some degree, the
539 EMG-based approach is more sensitive to SNR with
540 high estimation errors at low SNR levels (Fig. 5).
541 These findings indicate that the neural-drive-based
542 approach is more robust to low signal quality, com-
543 pared with the EMG-based estimates. The less strin-
544 gent requirement on the signal quality of the neural-
545 drive-based approach can facilitate wide clinical
546 applications for human–machine interactions, because
547 the quality of the signals obtained from clinical pop-
548 ulations tend to vary depending on pathology.26
549 The force estimation showed comparable perfor-
550 mance across individual fingers. These outcomes have
551 critical implications for clinical applications involving
552 human–machine interactions. First, assistive/rehabili-
553 tation strategies focusing on fine control of individual
554 finger movement are still a challenge in the field. The
555 ability to estimate individual finger forces reliably
556 shown in this study can help improve the performance
557 of human–machine interactions involving fine motor
558 control of finger movement. Together with the devel-
559 opment of advanced control frameworks, this
560 approach can help better utilize the high degrees of
561 freedom in exoskeleton or prosthetic hand, and further
562 improve the functional outcomes for individuals with
563neuromuscular disorders. Second, the robust perfor-
564mance across different task conditions involving steady
565grip or dynamic force variations can also facilitate
566applications during daily activities. However, the
567varying forces were still produced in isometric condi-
568tions. Additional studies involving dynamic finger
569movements are needed to further evaluate the perfor-
570mance in different dynamic movements with muscle
571fibers shifting substantially beneath the recording
572electrodes. Nonetheless, these findings suggest that the
573neural-drive-based estimation on individual finger
574force can be a promising approach for robust control
575of hand exoskeletons, prosthetic hands, or neuro-
576prostheses, which can help restore individual finger
577control and eventually could facilitate the utility of
578advanced devices in disabled individuals.
579The signal conditions may affect the EMG- and
580neural-drive- based approaches in different ways. For
581the neural-drive-based approach, no significant differ-
582ence was found across the two force levels. Although
583the signals from the 50% force level had a higher SNR
584than that from the 20% level, more superposition from
585more MUs is expected at higher forces. These two
586contrasting factors can balance out the effect on the
587decomposition performance. On the other hand, the
588EMG-based estimate showed a better performance at
589higher force level. The EMG envelop tended to be
590smoother at higher forces, which decreased the varia-
591tion of EMG-based estimate. In addition, the trajec-
592tory also influenced the two estimations, largely
593because the small variation of force estimate during the
594steady contraction can potentially decrease the R2
595values. The overall R2 of the neural-drive-based
596approach on simulation results exceeded the values
597shown in the experimental data. Because all the char-
598acteristics of EMG signals cannot be fully captured in
599the simulation, which can lead to higher decomposi-
600tion errors. Specifically, the EMG signals obtained
601from experiments may have sporadic action potential
602variations in amplitude and/or duration, and external
603factors (shift of electrode locations or motion artifacts)
604can also alter the signal properties.
605Limitations and Future Work
606The current study has several limitations. First, the
607subjects were instructed to only extend the designated
608finger in each trial. However, the subjects may still
609inevitably perform co-contractions due to finger
610enslaving,30 especially for the ring finger extension.
611The EMG signals from muscle co-contraction can
612potentially bias the force estimations. Second, the fin-
613ger activations were not classified. However, previous
614works6,18 have showed that the muscle activation of
615individual finger movement was localized and distin-
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616 guishable for both finger extension or flexion using
617 high-density EMG grid. The EMG activities of indi-
618 vidual finger contraction can be separated using pat-
619 tern recognition techniques. Third, the linear
620 regression was performed using the steady contraction
621 trial within each force level, but still exhibited a good
622 performance when tested on the varying force trials.
623 Since the mean firing rate of the decomposed MU pool
624 at each force level is not linearly related with the force
625 level, the regression was performed at individual force
626 levels. Future work will investigate whether the
627 regression performed on a single force level (e.g., close
628 to the maximum force) can be generalized across dif-
629 ferent force levels. Fourth, this study does not allow us
630 to dissect the specific levels of the central nervous
631 system in contribution to the neural drive estimations.
632 For example, different cortical regions, cerebellum,
633 basal ganglia, and brainstem can all play a role for the
634 neural drive estimation. Lastly, the force estimation is
635 performed through post-processing, and the compu-
636 tation load is a factor during real-time estimation. The
637 EMG-based approach is suitable for real-time esti-
638 mation. The neural-drive-based approach, however,
639 requires more computation time, largely at the EMG
640 decomposition step. Real-time decomposition has been
641 investigated previously.15 The strategy of online neu-
642 ral-drive-based approach is to obtain the separation
643 matrix for the extraction of neural drive information
644 using an initial batch (approximately 5-10 s). Then, the
645 separation matrix can be multiplied to the incoming
646 data to obtain the neural drive information in real-
647 time. The separation matrix will need to be updated
648 periodically during long-term use.
649 CONCLUSIONS
650 The current study shows that the neural-drive-based
651 approach out-performances the EMG-based approach
652 in predicting individual finger forces under different
653 scenarios. The boundary conditions for reliable neural
654 drive estimation were also provided. These findings can
655 potentially provide an intuitive and robust neural
656 interface for future studies that investigate the appli-
657 cations of rehabilitative or assistive devices for clinical
658 populations, with a goal to promote their indepen-
659 dence of living and enhance their quality of life. Future
660 work focusing on the real-time implementation of this
661 approach is needed for future clinical translation.
662
663
664 CONFLICT OF INTEREST
665 The authors have no financial relationships that
666 may cause a conflict of interest.
667REFERENCES
6681Al-Timemy, A. H., G. Bugmann, J. Escudero, and N.669Outram. Classification of finger movements for the dex-670terous hand prosthesis control with surface electromyog-671raphy. IEEE J. Biomed. Health. Inform. 17:608–618, 2013.6722Boretius, T., J. Badia, A. Pascual-Font, M. Schuettler, X.673Navarro, K. Yoshida, and T. Stieglitz. A transverse674intrafascicular multichannel electrode (TIME) to interface675with the peripheral nerve. Biosens. Bioelectron. 26:62–69,6762010.6773Callier, T., E. W. Schluter, G. A. Tabot, L. E. Miller, F. V.678Tenore, and S. J. Bensmaia. Long-term stability of sensi-679tivity to intracortical microstimulation of somatosensory680cortex. J. Neural Eng. 12:56010, 2015.6814Chen, M., and P. Zhou. A novel framework based on682FastICA for high density surface EMG decomposition.683IEEE Trans. Neural Syst. Rehabil. Eng. 24:117–127, 2016.6845Clancy, E. A., and N. Hogan. Probability density of the685surface electromyogram and its relation to amplitude686detectors. IEEE Trans. Biomed. Eng. 46:730–739, 1999.6876Dai, C., and X. Hu. Extracting and classifying spatial688muscle activation patterns in forearm flexor muscles using689high-density electromyogram recordings. Int. J. Neural690Syst. 29:1850025, 2019.6917Dai, C., H. Shin, B. Davis, and X. Hu. Origins of common692neural inputs to different compartments of the extensor693digitorum communis muscle. Sci. Rep. 7:13960, 2017.6948Dai, C., Y. Zheng, and X. Hu. Estimation of muscle force695based on neural drive in a hemispheric stroke survivor.696Front. Neurol. 9:187, 2018.6979Davoodi, R., C. Urata, M. Hauschild, M. Khachani, and698G. E. Loeb. Model-based development of neural prosthe-699ses for movement. IEEE Trans. Biomed. Eng. 54:1909–7001918, 2007.70110De Luca, C. J., and R. Merletti. Surface myoelectric signal702cross-talk among muscles of the leg. Electroencephalogr.703Clin. Neurophysiol. 69:568–575, 1988.70411Farina, D., L. Mesin, S. Martina, and R. Merletti. A sur-705face EMG generation model with multilayer cylindrical706description of the volume conductor. IEEE Trans. Biomed.707Eng. 51:415–426, 2004.70812Farina, D., I. Vujaklija, M. Sartori, T. Kapelner, F. Negro,709N. Jiang, K. Bergmeister, A. Andalib, J. Principe, and O.710C. Aszmann. Man/machine interface based on the dis-711charge timings of spinal motor neurons after targeted712muscle reinnervation. Nat. Biomed. Eng. 1:25, 2017.71313Fuglevand, A. J., D. A. Winter, and A. E. Patla. Models of714recruitment and rate coding organization in motor-unit715pools. J. Neurophysiol. 70:2470–2488, 1993.71614Gemperline, J. J., S. Allen, D. Walk, and W. Z. Rymer.717Characteristics of motor unit discharge in subjects with718hemiparesis. Muscle Nerve 18:1101–1114, 1995.71915Glaser, V., A. Holobar, and D. Zazula. Real-time motor720unit identification from high-density surface EMG. IEEE721Trans. Neural Syst. Rehabil. Eng. 21:949–958, 2013.72216Hu, X., W. Z. Rymer, and N. L. Suresh. Reliability of spike723triggered averaging of the surface electromyogram for724motor unit action potential estimation. Muscle Nerve72548:557–570, 2013.72617Hu, X., A. K. Suresh, W. Z. Rymer, and N. L. Suresh.727Altered motor unit discharge patterns in paretic muscles of728stroke survivors assessed using surface electromyography.729J. Neural Eng. 13:46025, 2016.
Journal : ABME MS Code : 10439 PIPS No. : 2240 h TYPESET h DISK h LE h CP Dispatch : 2-3-2019 Pages : 124 4
BIOMEDICALENGINEERING SOCIETY
Finger Force Estimation from Motoneuron Activities
Au
tho
r P
ro
of
UNCORRECTEDPROOF
730 18Hu, X., N. L. Suresh, C. Xue, and W. Z. Rymer. Extracting731 extensor digitorum communis activation patterns using732 high-density surface electromyography. Front. Physiol.733 6:279, 2015.734 19Hyvarinen, A., and E. Oja. Independent component anal-735 ysis: algorithms and applications. Neural Netw. 13:411–430,736 2000.737 20Keenan, K. G., D. Farina, K. S. Maluf, R. Merletti, and R.738 M. Enoka. Influence of amplitude cancellation on the739 simulated surface electromyogram. J. Appl. Physiol.740 98:120–131, 2005.741 21Kuiken, T. A., G. A. Dumanian, R. D. Lipschutz, L. A.742 Miller, and K. A. Stubblefield. The use of targeted muscle743 reinnervation for improved myoelectric prosthesis control744 in a bilateral shoulder disarticulation amputee. Prosthet.745 Orthot. Int. 28:245–253, 2004.746 22LeFever, R. S., A. P. Xenakis, and C. J. De Luca. A pro-747 cedure for decomposing the myoelectric signal into its748 constituent action potentials-part II: execution and test for749 accuracy. IEEE Trans. Biomed. Eng. 29:158–164, 1982.750 23Merletti, R., and P. Di Torino. Standards for reporting751 EMG data. J Electromyogr. Kinesiol. 9:3–4, 1999.752 24Negro, F., S. Muceli, A. M. Castronovo, A. Holobar, and753 D. Farina. Multi-channel intramuscular and surface EMG754 decomposition by convolutive blind source separation. J.755 Neural Eng. 13:26027, 2016.756 25Richard, P. D., R. E. Gander, P. A. Parker, and R. N.757 Scott. Multistate myoelectric control: the feasibility of 5-758 state control. J. Rehabil. R&D 20:84–86, 1983.
75926Santello, M., and C. E. Lang. Are movement disorders and760sensorimotor injuries pathologic synergies? When normal761multi-joint movement synergies become pathologic. Front.762Hum. Neurosci. 8:1050, 2015.76327Thompson, C. K., F. Negro, M. D. Johnson, M. R.764Holmes, L. M. McPherson, R. K. Powers, D. Farina, and765C. J. Heckman. Robust and accurate decoding of766motoneuron behaviour and prediction of the resulting force767output. J. Physiol. 596:2643–2659, 2018.76828van Beek, N., D. F. Stegeman, J. C. Van Den Noort, D. H.769E. J. Veeger, and H. Maas. Activity patterns of extrinsic770finger flexors and extensors during movements of instructed771and non-instructed fingers. J. Electromyogr. Kinesiol.77238:187–196, 2018.77329Yao, W., R. J. Fuglevand, and R. M. Enoka. Motor-unit774synchronization increases EMG amplitude and decreases775force steadiness of simulated contractions. J. Neurophysiol.77683:441–452, 2000.77730Zatsiorsky, V. M., Z.-M. Li, and M. L. Latash. Enslaving778effects in multi-finger force production. Exp. Brain Res.779131:187–195, 2000.
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