RESEARCH Open Access
Decoding of grasping information from neural
signals recorded using peripheral intrafascicular
interfaces
Silvestro Micera1,2*†, Paolo M Rossini8,4†, Jacopo Rigosa1, Luca Citi1, Jacopo Carpaneto1, Stanisa Raspopovic1,
Mario Tombini3, Christian Cipriani1, Giovanni Assenza3, Maria C Carrozza1, Klaus-Peter Hoffmann5, Ken Yoshida6,
Xavier Navarro7 and Paolo Dario1
Abstract
Background: The restoration of complex hand functions by creating a novel bidirectional link between the
nervous system and a dexterous hand prosthesis is currently pursued by several research groups. This connection
must be fast, intuitive, with a high success rate and quite natural to allow an effective bidirectional flow of
information between the user’s nervous system and the smart artificial device. This goal can be achieved with
several approaches and among them, the use of implantable interfaces connected with the peripheral nervous
system, namely intrafascicular electrodes, is considered particularly interesting.
Methods: Thin-film longitudinal intra-fascicular electrodes were implanted in the median and ulnar nerves of an
amputee’s stump during a four-week trial. The possibility of decoding motor commands suitable to control a
dexterous hand prosthesis was investigated for the first time in this research field by implementing a spike sorting
and classification algorithm.
Results: The results showed that motor information (e.g., grip types and single finger movements) could be
extracted with classification accuracy around 85% (for three classes plus rest) and that the user could improve his
ability to govern motor commands over time as shown by the improved discrimination ability of our classification
algorithm.
Conclusions: These results open up new and promising possibilities for the development of a neuro-controlled
hand prosthesis.
I. Introduction
The human hand is a versatile organ that is used for
grasping heavy or delicate objects and for performing
highly complex manipulations on the basis of fine motor
control and precise sensory feedback [1]. The restoration
of these sensorimotor functions after upper limb amputa-
tion is particularly challenging. Two main components
must be developed: (a) hand prostheses able to mimic the
natural hand from both a biomechanical and sensory
points of view [2]; (b) intimate interfaces for online brid-
ging the user’s nervous system and the external prosthesis.
Several solutions are possible and are currently investi-
gated by independent research groups using non-invasive
[3-5], and invasive [6-8] approaches. For instance, intra-
cortical signals can be used to simultaneously control
reaching and one degree of freedom grasping of an artifi-
cial limb as recently shown in non-human primates [8].
In case of amputation, it is also possible to use the resi-
dual part of amputee muscles and peripheral nerve fibers
to control the artificial hand. This approach can be
implemented by processing electromyographic (EMG)
signals recorded using either non-invasive [9] or invasive
[10,11] electrodes. The transposition of residual nerves of
amputees to other muscles in or near the amputation site
can be also implemented [12-14]. This approach (called
“Targeted Muscle Reinnervation”, TMR) is probably the
* Correspondence: micera@sssup.it
† Contributed equally
1BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa (Italy
Full list of author information is available at the end of the article
Micera et al. Journal of NeuroEngineering and Rehabilitation 2011, 8:53
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© 2011 Micera et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
most advanced clinical solution currently available and it
has the interesting advantage that the nerve function cor-
relates physiologically to the motor action controlling in
the prosthesis. Therefore, the control of the prosthesis is
more natural and easier than with other EMG-based
approaches. However, it presents two main limitations:
(i) it requires a major surgical intervention and the use of
a grid of surface electrodes. In this way, the problems
due to the invasiveness are not totally balanced by signifi-
cant advantages in terms of usability and cosmetic
appearance; (ii) it is effective mainly for proximal ampu-
tation levels (i.e., close to the axilla, which are less
common than transradial - adjacent to the elbow - ampu-
tations) because of the characteristics of the surgical
procedure.
The use of invasive neural interfaces directly connected
to the peripheral nervous system (PNS) is potentially
appealing because it is able to provide an almost “physio-
logical” condition in which efferent and afferent fibers,
previously connected with the natural hand, may return
to their role in controlling the prosthetic limb/hand. This
is theoretically possible since a significant amount of per-
ipheral nerve fibers, as well as spinal cord and brain con-
nections dedicated to the control of the amputated limb,
survive in time and remain available to restore a “physio-
logical” condition also thanks to the plastic abilities of
the CNS to reorganize for functional recovery [15-17].
Several invasive PNS interfaces have been developed in
the past [18]. Although most of them were originally
used for functional electrical stimulation (FES) in spinal
cord injured persons [19-21], they can also be the key
component of neuro-controlled hand prostheses. In this
case, they are used to record efferent motor signals and
to deliver sensory feedback [22-25].
Among invasive PNS interfaces, longitudinal intra-
fascicular electrodes (LIFEs) - namely intrafascicular
electrodes inserted longitudinally into the nerve [26,27]
- are interesting due to the selective contact with a lim-
ited number of specific nerve fibers and the relatively
low level of invasiveness required for their implantation.
In fact, after appropriate control in experimental models
to test their biocompatibility and efficacy [18], LIFEs
have been recently used to control artificial devices
[6,7,22-25] showing good results during short-term trials
with human amputees. These trials showed that subjects
are able to control a one-degree of freedom hand pros-
thesis through online processing of the efferent neural
signals, and that repeatable sensory feedback may be
received by stimulating afferent fibers [22,23] corre-
sponding to the missing hand/fingers territories. More-
over, as seen in animal models [28], LIFEs allow the
extraction of spikes from the signals recorded signifi-
cantly increasing the decoding ability for online classifi-
cation. Spike shapes associated to different fibers depend
on the size and conduction speed of the fibers, on the
relative distance and orientation between the fiber and
the electrode, and on the inhomogeneity of the intrafasci-
cular space. Therefore, a spike sorting approach can be
used to identify the activity related to different nerve
fibers, significantly increasing the decoding ability.
Starting from these encouraging results, the aim of
this study was to verify whether “hand-related” actions
(such as different grip types or movements of single fin-
gers) can be decoded by processing neural motor-related
signals recorded by LIFEs. This result could allow con-
trol of a dexterous hand prosthesis using the natural
neural “pathway” and increase the usability of actuated
artificial hands.
In particular, the following issues were addressed also as
an extension of a recent study on the same case [24,25]: (i)
how many degrees of freedom (or different grasping tasks)
can be reliably extracted and controlled from efferent
neural signals; (ii) to which extent the combined analysis
of an ensemble of motor LIFE signals recorded when a
motor command is dispatched to the amputated hand/fin-
gers improves movement classification via the interface
processor; (iii) whether there is any learning effect during
the use of the interface; (iv) whether this kind of approach
needs frequent re-calibrations as in the case of invasive
cortical neuroprostheses.
A more general and clinically-oriented paper on the
same subject, concerning the technique of LIFE insertion
and LIFE signal analysis, including results on output con-
trol, sensory perception, clinical outcome and associated
neuroplastic changes has already been published elsewhere
[24].
II. Materials and methods
A. Thin-film LIFEs
A new version of LIFEs, named thin-film LIFEs (tfLIFE),
was used in the experiments [29]. These electrodes were
developed on a micropatterned polyimide substrate, which
was chosen because of its biocompatibility, flexibility and
structural properties. After microfabrication, this substrate
filament (shown in Figure 1) was folded in half so that
each side had four active recording sites. Therefore,
tfLIFEs allow recordings at eight active sites per electrode.
A tungsten needle linked to the polyimide structure was
used for implanting the electrode and was removed imme-
diately after insertion.
B. Experimental protocol
P.P., a 26 year old male, suffered amputation of his left
arm two years before the implantation due to a car acci-
dent. The surgical procedures are detailed elsewhere [24].
Briefly, following epineural micro-dissection, two
tfLIFE4s (Figure 1) were inserted in the ulnar and the
median nerves 45° obliquely to assure stability and to
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increase the probability of intercepting nerve fibers. The
distal handle of the electrode was anchored to the epi-
neurium. Four weeks later, tfLIFE4s were removed as
required by the European Health Authorities. P.P.
worked on the project 4-6 hours/day for 6 days/week,
and did not report any complication during the following
12 months. A complete, clinical report on this case can
be found elsewhere [24].
During the first three weeks of experiments P.P. was
involved in several experiments addressing different bio-
medical and neurophysiological issues related to the effi-
cacy of this approach (e.g., sensory feedback [24], scalp
EEG recordings [30], Trancranial Magnetic Stimulation
(TMS-related) neural signals [31]).
The trials on the recording of neural motor LIFE signals
were carried out during the last week of experiments, after
verifying an improvement in the signal-to-noise ratio of
the LIFE signals. In particular, P.P. was specifically asked
to separately and selectively dispatch the order to perform
the following three movements: (a) palmar grasp, (b)
pinch grasp, (c) flexion of the little finger. Pictures repre-
senting these tasks were randomly presented to him on a
computer screen to provide a visual cue. The overall pro-
cessing scheme is shown in Figure 2 and will be briefly
described in the next paragraphs.
LIFE motor signals were recorded via 4-channel ampli-
fiers (Grass QP511 Quad AC; ENG amplified: X10.000, fil-
tered: 100 Hz- 10 kHz; 16 bit, 1 Ms/s analogue-to-digital
converter).
When above a selected threshold, the neural signals
recorded were used to teleoperate a dexterous hand pros-
thesis. The prosthetic hand used was a stand-alone ver-
sion of the CyberHand prototype, already employed in
several research scenarios [32]. This approach was used
as a “biofeedback” to increase the confidence of the
patient in this procotol.
C. Off-line Processing algorithms of LIFE signals
A wavelet denoising technique was used to improve the
signal-to-noise ratio (SNR) in the neural signals recorded.
Wavelet denoising is a set of techniques used to remove
noise from signals and images. The main idea is to trans-
form the noisy data into an orthogonal time-frequency
domain. In that domain, thresholding is applied to the
coefficients for noise removal, and the coefficients are
finally transformed back into the original domain obtain-
ing the denoised signal [33]. A decomposition scheme
based on the translation-invariant wavelet transform [34]
was used as shown in [28]. After the denoising, the differ-
ent classes of spikes were extracted. The algorithm con-
sisted of a two-step process [28]: creation of spike
templates and then comparison of spikes in the signal
with the templates using some similarity indexes as the
correlation coefficient or the mean square difference
between a spike and a template and the power of the
template. This spike-based approach was used because it
already showed to allow high classification accuracy in
animal experiments [28]. The characterization of the
SNR was carried out according to the methodology pre-
viously described in [35].
D. Identification of the desired motor commands
The denoised signals were then used to identify the dis-
patched motor commands by implementing the following
procedure. For each trial, the different recording periods
(’epochs’) related to the movement classes (e.g., grip types
and rest) were labeled. The three desired movements and
the rest were considered as separated classes. The perfor-
mance metrics considered was the ratios between classes
correctly identified out of those presented and the leave
one out validation standard method has been used.
Each epoch was an example that was used to train the
classifier or to test its generalization skills. The feature
Figure 1 Picture and unfolded overview of tfLIFE (17). Total length: 60 mm. Length without pad areas: 50 mm. Each end of the tfLIFE carries
a ground electrode (GND), a reference electrode (L0, R0) and the recording sites (L1-L4, R1-R4).
Micera et al. Journal of NeuroEngineering and Rehabilitation 2011, 8:53
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vector was made of the ratios between the number of
spikes matching each spike template and the total num-
ber of spikes in the epoch [28]. Therefore, the absolute
spike rates were not used, but rather the relative spike
rates of each waveform w.r.t. the others. This should
prevent classification of the motor commands based on
the “quantity of activity” and favor the use of the “qual-
ity of activity” intended in terms of different waveforms
for different stimuli.
In order to infer the type of stimulus applied during a
given epoch from the feature vector F, a classifier based
on support vector machines (SVMs, [36]) was used
making use of the open source library LIBSVM [37]. To
allow SVMs, and other binary classifiers, to handle mul-
ticlass problems, the latter must be decomposed into
several binary problems. In this work we used a one-
against-one approach.
Finally, in order to assess the inter-day robustness of
this approach the classifier was trained by using all the
features extracted for the first day of recordings during
the last week of experiments and used without any
further change for the next days. This was done to
understand whether this approach might be used without
any need for recalibration.
III. Results
An example of a LIFE motor signal before and after the
wavelet denoising is given in Figure 3.
The SNRs for the different channels before and after
the denoising are given in Figure 4. The quality of the
recording after denoising can be considered good simi-
larly to the considerations done in [35].
In Figure 5, the raster plots for different classes of
spikes (different colors) for the different movements are
provided. A different modulation for different tasks can
be seen showing that the neural signals recorded and, in
particular, the classes of spikes extracted can be consid-
ered related to different motor commands. It is also
interesting that we have a double-peak spike rate espe-
cially for the palmar grasp. This could allow in the
future the use of the two peaks for the pre-shaping
(opening) and closing of the prosthetic device.
In Table 1, the best performance achieved with the
best combination of tfLIFE channels is shown for the
Figure 2 Scheme of the algorithms used to process the LIFE motor signals and to identify the desired class of movement. The signals
recorded were denoised. This is necessary and quite useful for the commonly quite low signal-to-noise ratio of the LIFE signals [21]. The classes
of spikes extracted from the LIFE signals were used as inputs for an SVM classifier able to identify the desired voluntary activity. Results about
sensory feedback experiments based on electrical stimulation of afferent nerve fibers are reported in [24].
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decoding of rest plus one, two, or three movement
classes are shown in the Table 2. In most cases, a com-
bination of features coming from all the three tfLIFE
channels is required to obtain the best recognition ratio
(rr). Most of the channels we used were the “expected
ones” from a neurophysiological viewpoint but some
unexpected channels were also used. This could be due
to some inability of the user to deliver the natural ("cor-
rect”) neurophysiological commands.
In Table 2, the analysis of the advantages connected
with the daily retraining of the classifier is shown. In
particular, the first row shows the best results achieved
when the classifier was trained only during the first day
while the second row provides performance when the
algorithm was re-trained everyday.
The improvement of performance could be due not
only to a limit in the robustness of the approach (i.e., the
time-variance of the LIFE signals recorded during the dif-
ferent days) but also to the learning ability shown by the
user. In fact, Figure 6 (right panel) shows the classifica-
tion performance achieved for different classes during
two days of recording. The learning process of the sub-
ject is a distinctive feature of this approach.
In order to understand the importance of using multi-
channel electrodes for neural recordings, classification
Figure 3 The raw (top panel) and the denoised (bottom panel) LIFE motor signals. Superimposed with the signal (black line) are shown
signals, which represent the different tasks the subject was asked to perform (rest = yellow line; flexion of the little finger = blue line; palmar
grasp = red line; pinch grasp = green line).
0 2 4 6 8 10 12
0
2
4
6
8
10
# of channels
)nae
m( 
R
N
S
raw
filt
Figure 4 SNR before and after the denoising for the different
channels.
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2E
ST
0IN
CH
2E
ST
0A
LM
AR
2E
ST
2E
ST
,IT
TLE
0IN
CH
2E
ST
0A
LM
AR
2E
ST
2E
ST
2E
ST
2E
ST
2E
ST
,IT
TLE
0IN
CH
,IT
TLE
0A
LM
AR
0IN
CH
Figure 5 Raster plots and spike rates. Top panel: raster plots for different classes of spikes during the selection of each type of movements by
the subject. Bottom panel: corresponding spike rates for the corresponding classes windows (mean = larger lines; mean ± SD = smaller lines
and colourful areas; areas are colored when not overlapped). Window duration for each class has been normalized (from 0 to 100%). The red
vertical lines delimit the timing of the classes triggering in both top and bottom panels. Only windows with the fixed width of the controlled
experiment have been used.
Table 1 Performance of the classifier (recognition ratio) with re-training of the classification algorithm
Task(s) identified rr M1 channel # M2 channel # U channel #
(%) 1 2 3 4 5 6 7 8 9 10 11 12
rest vs little 93 – – – – X – – – X X – X
rest vs palmar 95 X – – – – X – – X – – –
rest vs pinch 100 – X – X X – – X – X – –
rest vs little vs palmar 92 – X – – X – X – – X – X
rest vs little vs pinch 90 – X – X – X – X – – X –
rest vs pinch vs palmar 87 – – X – X – – – – – – X
rest vs little vs pinch vs palmar 85 – X – – X – X – X X – –
rest vs activity 87 – – – X X X X X – X X X
little vs pinch vs palmar – X X – – – – X – X – X
The parameters of the classifier were selected for each day using data recorded in that specific day for the training. For each classification, the max performance
(rr) and the best combination of channels used are given. rr = recognition ratio of the SVM classifier; M1 an M2 indicate the two tfLIFE implanted in the median
nerve whereas U indicates the tfLIFE implanted in the ulnar nerve.
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performance was calculated and compared using differ-
ent recording channels. The results shown in Figure 6
(left panel) indicate that increasing the number of chan-
nels used for the classification enhances the performance
because of the increased amount of information. Above a
certain limit, performance starts degrading again, given
the classic overfitting issues of classifiers (e.g., there is an
excessive number of parameters to be optimized).
IV Discussion
Among the different approaches for restoring the link
between the external world and the nervous system, the
use of implantable intrafascicular PNS interfaces is of
great potential, as confirmed in previous experiments in
humans [6,7]. The evolution of peripherally-controlled
hand prostheses seems to go towards a more natural
and ideal approach which is the general evolution of
artificial devices. In fact, differently from the processing
of surface EMG signals, which prevents the use of more
natural neural “channels”, the TMR approach is more
natural because of the reconnection of peripheral nerves
previously linked to the amputated limb but it still relies
on surface EMG signals. To this respect, the use of
implantable interfaces into the PNS could allow the
restoration of the pre-existing neural connection com-
pleting this evolution.
Table 2 Performance of the classifier (recognition ratio) without and with re-training of the classification algorithm
Grip type(s) identified and controlled (plus rest) Little, pinch, palmar Little, palmar Little, pinch Palmar, pinch Little Palmar Pinch
No Retraining 0.72 0.86 0.84 0.8 0.9 0.76 0.9
Retraining 0.85 0.92 0.9 0.87 0.93 0.95 1
In case of “re-training” the parameters of the classifier were selected for each day using data recorded in that specific day for the training. In case of “no
training”, the definition of the parameters happened only for the first day.
2 4 6 8 01 21
0
02
04
06
08
001
M
ax
 P
er
fo
rm
an
ce
 (
%
)
slennahc fo #
1 2 3
0
02
04
06
08
001
sessalc fo #
82 yad
03 yad
Figure 6 Classification performance. Right Panel: Improvement of the classification performance for one, two, or three classes during two
training days. Left Panel: Maximum performances are shown as a function of the number of channels simultaneously acquired during the motor
commands delivered to the missing limb.
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The aim of our study was to increase the understand-
ing of the basic mechanisms, as well as of the possibili-
ties and limits of this approach, by processing the motor
signals recorded the nerve fibers as final output to the
target muscles (via tfLIFE signals analysis).
Two main assumptions were tested: (1) the extraction
of spike templates can allow good decoding performance;
(2) it is possible to extract more than a simple one degree
of freedom control signal (as previously done by other
researchers [7]). In particular, more complex information
related to grip types can be decoded from motor signals
recorded using tfLIFEs.
Concerning the first assumption we moved from pre-
vious knowledge [28] that an algorithm based on spike
sorting can improve the decoding performance with
respect to other more classical approaches based on the
extraction of power-related information from the signal.
In particular, the spike-sorted data can effectively increase
the resolution of the information that can be extracted
from a single electrode. This could improve the decoding
performance and to some extent reduce invasiveness since
fewer electrodes would be required to obtain the needed
number of information channels.
The idea of decoding complex high-level information
(i.e., grip types) represents the main novelty of this study.
It is a disruptive approach for decoding and potentially
quite challenging because of the characteristics of the sig-
nals to process. In fact, signals recorded from the periph-
eral nerves are related to low-level information (i.e., the
commands to control muscular contraction). Therefore,
this “high-level” decoding could be considered similar to
understand a global picture (the grip type) with only a few
pieces (related to specific muscle commands) of it. This
strategy would allow an easier, more efficacious and faster
(namely online) control of a dexterous hand prosthesis. In
fact, this could increase the number of motor commands
which could be dispatched to the hand without a very pre-
cise decoding necessary for example for the simultaneous
control of the kinematics of several joints.
The results of this study, in terms of rate of correct
classification of motor tasks, show that the two assump-
tions were correct, based on good performance achieve-
ment and a state control algorithm implementation. This
control approach is commonly used in the EMG-based
control of hand prostheses [38] and has been recently
implemented in cortical neuroprostheses [39]. For the
first time, three different hand movements were identi-
fied with good performance. This could allow the full
usability of dexterous prostheses by the user who simply
dispatches motor commands for hand movements as he
normally did before amputation. Moreover, it could be
possible to use the double-peak distribution shown for
the palmar grasp (Figure 5) to separately control the pre-
shaping (opening) and closing of the hand prosthesis.
Of great interest was that our amputee subject was
able to improve his performance during the consecutive
trials. This was particularly evident during the experi-
ments by looking at the SNR and was also confirmed by
the classification results. However, this was achieved
only for two consecutive days and this learning ability
has to be confirmed during longer tests.
The performance also improved by using several
intrafascicular recording channels. This is due to the
intrinsic blindness of the implantation procedure: a
large number of channels can increase the probability of
picking up enough signals to reach good classification
levels and to maintain such a rate of appropriate classi-
fication stable in time. In fact, an important characteris-
tic of our approach was the robustness of classification
during the trials. This kind of interface seems to be
quite stable and makes the daily re-calibration of the
algorithm - which affects other implantable neuro-
prostheses - unnecessary. This is very important and
could represent a significant advantage during daily
activities carried out outside a controlled laboratory
environment.
Unfortunately, due to the time limitations of our experi-
ments it was not possible to increase the number of grip
types that the subject was asked to perform. However,
results clearly indicate that multiple movements can be
governed, probably more than the three used in the pre-
sent study. Moreover, it is also possible to combine the
state control approach presented in this paper to select
different grasping tasks together with a proportional con-
trol (already achieved in [6,7]). This will allow both the
modulation of force during grasping and the simultaneous
control of hand and elbow functions.
In the near future, the possibility of verifying these
findings during chronic experiments with an implanted
hand prosthesis for daily activity uses, will be investi-
gated. The analysis of the performance of the LIFE elec-
trodes during long-term implants is very important to
understand the potentials and shortcomings of this
technology in terms of chronic usability, stability, etc.
Moreover, particular attention will be given to charac-
terize long-term changes in the cortical activation and
new approaches will be explored for characterizing the
long-term usability and for improving the efficacy of
intrafascicular electrodes. For example, as previously
shown for the central nervous system [40], actuation of
the active sites of the electrodes [41,42] may help to
increase the SNR and selectivity both for stimulation
and recording.
Acknowledgements
This work was partially supported by the EU within the NEUROBOTICS
Integrated Project (IST-FET Project 2003-001917: The fusion of NEUROscience
and roBOTICS).
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Author details
1BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa (Italy. 2Institute for
Automation, Swiss Federal Institute of Technology, Zurich (Switzerland.
3Campus Biomedico University, Rome (Italy. 4Casa di Cura S. Raffaele, and
IRCCS S. Raffaele-Pisana, Rome (Italy. 5Fraunhofer Institute for Biomedical
Engineering, St. Ingbert (Germany. 6Indiana University-Purdue University,
Indianapolis (USA. 7Universitat Autònoma de Barcelona, and CIBERNED,
Barcelona (Spain. 8Institute of Neurology, Catholic University, Po. Gemelli,
Rome, Italy.
Authors’ contributions
SM and PMR contributed to all the stages of this work (i.e., design of the
protocol, following all the experiments, data interpretation and writing). JC,
LC, SR, and JR developed decoding algorithms for signal processing and
classification. MT and GA contributed to the in-vivo experiments and all the
clinical training. CC and MCC designed the algorithms for the control of the
hand prosthesis. PD, XN, and KY contributed to the design of the protocol.
KPH designed tf-LIFEs. All authors read and approved the final manuscript.
Competing interests
CC hold shares in Prensilia Srl, the company that manufactures robotic
hands as the one used in this work, under the license from Scuola Superiore
Sant’Anna.
Received: 3 January 2011 Accepted: 5 September 2011
Published: 5 September 2011
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doi:10.1186/1743-0003-8-53
Cite this article as: Micera et al.: Decoding of grasping information from
neural signals recorded using peripheral intrafascicular interfaces.
Journal of NeuroEngineering and Rehabilitation 2011 8:53.
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