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EEG Correlates of Motor Control Difficulty in Physical Human-Robot Interaction: A Frequency Domain Analysis



This study investigates the relationship between electroencephalogram (EEG) activity and motor control difficulty during physical interaction with an admittance controlled robot. Subjects performed a fine cooperative manipulation task, in which the motor control difficulty was manipulated by altering admittance dynamics. To quantify motor control difficulty, an interaction instability index is proposed based on the spectral information of interaction forces. Regression analysis is then performed to construct a model to estimate motor control difficulty from EEG spectral features. The results indicate the reliability of EEG signals as an indicator of motor control difficulty in pHRI.
EEG Correlates of Motor Control Difficulty in Physical Human-Robot
Interaction: A Frequency Domain Analysis
Amirhossein H. Memar and Ehsan T. Esfahani
Abstract This study investigates the relationship between
electroencephalogram (EEG) activity and motor control diffi-
culty during physical interaction with an admittance controlled
robot. Subjects performed a fine cooperative manipulation
task, in which the motor control difficulty was manipulated
by altering admittance dynamics. To quantify motor control
difficulty, an interaction instability index is proposed based
on the spectral information of interaction forces. Regression
analysis is then performed to construct a model to estimate
motor control difficulty from EEG spectral features. The results
indicate the reliability of EEG signals as an indicator of motor
control difficulty in pHRI.
Physical human-robot interaction (pHRI) aims to integrate
the repeatability and accuracy of robots with problem solving
skills of humans to improve the overall performance in
a wide variety of robotic applications such as effortless
cooperative manipulation (co-manipulation) of heavy objects,
robot learning by demonstration and teleoperation. In such
applications, a compliant interaction is essential to achieve a
successful interaction and ensure the users’ safety.
Compliant interaction can be obtained by either imple-
menting active control methods (e.g., impedance/admittance
[1]) or using actuators with passive compliant ele-
ments (e.g., variable stiffness actuators [2], [3]). The
impedance/admittance parameters can be modulated adap-
tively to further improve the pHRI performance. However,
it is challenging to ensure the stability of physical inter-
action due to the lack of information about human dy-
namics and the biomechanical impedance of human arm
which is subject-specific, configuration-dependent, nonlinear,
and time-varying [4]. Moreover, understanding the user’s
intention of pHRI, the so-called human intention inference,
is required to implement an appropriate adaptation. For
instance, a low virtual damping can facilitate cooperative
manipulation in terms of human effort, whereas, an increased
virtual damping may improve the accuracy in fine and
accurate positioning by suppressing disturbances [5].
Although most of the proposed adaptive methods have
demonstrated an improved performance with respect to
a constant high/low compliance, an optimal method that
seeks to maximize perceived comfort by the users requires
objective measures of human mental states. For instance,
Gopinathan et al. [6] showed that a suitable adaptive stiff-
ness strategy depends on the task characteristics and varies
AH. Memar and ET. Esfahani are with the Department of
Mechanical and Aerospace Engineering, University at Buffalo
SUNY, Buffalo, NY, 14260 USA.,
between individuals. Therefore, a reliable and continuous
estimation of motor control difficulty will enable designers to
assess the effectiveness of pHRI. It is specially important in
domains such as robotic rehabilitation [7] where maintaining
the optimal level of workload increases the motor-learning.
The effectiveness of pHRI is traditionally assessed by
subjective measures and questionnaires which require inter-
ruption of tasks. Moreover, participants’ self-reports may be
affected by a posteriori rationalization and desire to satisfy
implicit objectives of the researcher [8]. Another approach
is the use of performance metrics (e.g., completion time) as
an indirect measure of difficulty. Nevertheless, the estimation
of task performance is impractical or prohibitive for many
real-world scenarios where sensing capabilities are limited.
Recently, neuroergonomic measures have been used to
assess the quality of human-machine interaction [9]. Kulic
and Croft [10] used galvanic skin responses to determine
the participant anxiety in HRI. In their study, participants
observed the robot arm motions passively without physical
contacts. A significant difference was revealed between a
safe and an unsafe motion planner due to the participants’
anxiety affected by the robot motions. Dehais et. al [11]
used ocular response, skin conductance and deltoid muscle
activity to evaluate the physical comfort in a hand-over task
with different motion planners. Novak et. al [7] studies a co-
manipulation task with a gravity and friction compensated
robot arm. They altered the difficulty of a virtual game
between trials and measured the workload. The workload
estimation performance for all the physiological modalities
was found to be significantly better than random. Although a
compliant interaction was used in their study, task difficulty
was adjusted in terms of mental arithmetic and temporal
effort rather than the motor control difficulty of the pHRI.
Brain monitoring and in particular EEG has become the
most studied physiological indicator of workload due to its
high temporal resolution [12] and thus has been used in
this study to continuously and unobtrusively estimate motor
control difficulty. For this purpose, participants performed
a path-tracking task in which an admittance controlled arm
is used to enable compliant interaction. Controller intrinsic
dynamics is used to manipulate motor control difficulty by
decreasing the virtual damping and making the precise guid-
ance more difficult. A quantitative measure representing the
instability of the motions is extracted from spectral analysis
of interaction forces to estimate motor control difficulty. EEG
spectral power density and coherence features are used to
investigate EEG correlations with motor control difficulty
using multiple linear regression analysis.
978-1-5386-5424-8/18 c
2018 IEEE Haptics Symp. 2018, San Francisco, USA
Accepted for publication by IEEE. c
2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/
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User Displays
Fig. 1. Experimental setup and the locations of EEG sensors.
- -
Fig. 2. Block diagram of the admittance controller.
A. Experimental Setup
Figure 1 shows the experimental setup used in this study.
B-Alert X24 wireless headset (Advanced Brain Monitoring
Inc., Carlsberg, CA, USA) was used to record non-invasive
EEG signals from 20 locations based on the 10/20 interna-
tional EEG system which are shown in Fig. 1. EEG signals
were referenced to linked mastoid electrodes located behind
ears. To provide the visual information regarding visuomotor
tasks and end-effector position, a computer display was
placed in the front of subjects. A light-weight 6-DoF robot
arm, SCHUNK PowerBall [13], equipped with a force/torque
sensor at the end-effector was used for pHRI task.
An admittance controller with null stiffness was imple-
mented to provide a compliant behavior such that users
can lead the robot motions in the Cartesian space. The
controller consisted of two feedback loops (outer and inner)
as depicted in Fig. 2. The outer loop establishes a second
order dynamics between the input force and end-effector
position in the Cartesian space, whereas, the internal loop
tracks the desired velocities in the joint space. In the outer
loop, the interaction forces acquired from the force sensor
are mapped to the desired end-effector velocity based on the
admittance function Ycto establish (1).
where vcR6denotes the desired end-effector veloc-
ity; fhR6is the interaction force and torque vector;
Md,CdR6×6are the admittance parameters indicating
the inertia and damping of the desired compliance respec-
tively. vcis then transformed into the desired velocity in the
joint space ( ˙
qc) using the inverse Jacobian J1(q)as (2),
The robot motion controller (inner loop) tracks the desired
joint velocities based on a closed-loop feedback scheme jD
by applying torques to the joints of the robot with intrinsic
8 cm
y (cm)
-5 0 5
x (cm) -5 0 5
x (cm)
Boundary Tracked Path
(a) (b)
Fig. 3. Sample tracks of co-manipulation with (a) HD, and (b) LD mode.
impedance of jZr. A local PI controller for each joint was
used for tracking the desired velocity.
B. Experimental Scenario
Participants were asked to perform a fine co-manipulation
task by guiding the end-effector between the boundaries of
a star-shaped path that is shown in Fig. 3. The primary error
metric was defined as the total number of times that an
individual crosses the virtual boundaries over the 3 minutes
experiment. Each participant experienced two conditions in
which the controller dynamics was altered using a low
damping (LD) and high damping (HD). Only the damping
parameter of the admittance control was subjected to change
since it has been identified as the most dominant parameter
that affects the interaction with compliant controllers [14].
The robot resistance is lower for the LD thus the human
effort decreases. However, low damping may cause unstable
interaction since the controller dynamics gets closer to its
unstable region. The resulting over-responsive motions of the
robot will be seen as overshoots in co-manipulation. This
instability is more pronounced in precise co-manipulations
since humans tend to increase arm stiffness to achieve higher
precision and resistance to disturbances [15]. In our study,
we exploit this dynamics between humans and an admittance
controller to increase motor control difficulty via decreasing
the damping and making the co-operation harder.
To compensate the effect of user’s arm weight, the motion
of the end-effector was restricted to the X-Y plane. The
maximum velocity of the joints was also limited to 0.6
rad/sec to increase the safety of interaction. In both con-
ditions, Mdwas set to Diag([3,3,3,0.1,0.1,0.1]), while,
Cdwas set to Diag([80,80,80,10,10,10]) for the HD, and
Diag([20,20,20,6,6,6]) for the LD condition. These values
are obtained experimentally to ensure a stable co-operation
with HD and a more difficult interaction with LD condition.
This difference can be observed in the sample tracked paths
shown in Fig 3.
C. Participants and Procedure
11 subjects (8 males and 3 females) were recruited from
University at Buffalo School of Engineering to participate in
an IRB-approved experiment. Participants’ ages ranged from
23 to 34 (mean age 27.5) and they had normal or corrected to
normal vision. Participants were instructed to guide the robot
end-effector by holding a handle attached to the force/torque
sensor. As the feedback for visuomotor tasks, the position of
the end-effector in the X-Y plane was displaced on the users’
monitor via a circular cursor. After briefing on the objectives
of the experiment, participants received a practice session
of co-manipulation in free space, without any particular
path, to get familiar with the two damping conditions of
the admittance control. The order of experimental conditions
were permuted randomly for each participant to balance the
learning effects. To obtain subjective measures, subjects were
asked to complete a NASA TLX questionnaire [16] upon the
completion of each experimental condition.
D. EEG Data Analysis
EEG signals were band-pass filtered (0.1-70 Hz) and then
digitalized with a sampling rate of 256 Hz. They were
transmitted then from the headset via a Bluetooth link to
a nearby PC. The B-Alert recording software was used to
automatically remove eye blinks and muscle movements
artifacts [17]. Spectral power density and coherence analysis
was then performed on clean EEG signals.
Spectral power density reflects specific regional activity in
an isolated fashion, whereas, the coherence between channel
pairs corresponds to the inter-regional functional connectiv-
ity. It quantifies the level of synchrony between two signals at
a specific frequency and it ranges from 0 to 1. A Hamming
window with 50% overlap was used to extract power and
coherence estimates using Welch’s method from 2-second
epochs. Features were extracted from 6 frequency bands
including: theta (4-7 Hz), lower-alpha (8-10 Hz), higher-
alpha (11-13 Hz), lower-beta (14-22 Hz), higher-beta (23-35
Hz) and gamma (36-44 Hz). EEG alpha-attenuation at low-
alpha is associated with the state of alertness and expectancy,
whereas, high-alpha is mainly related to semantic memory
information processing [18]. Accordingly, the division of
alpha band to low and high range are common in studying
psychomotor efficiency [19].
120 spectral power measures (Nch ×Nfwhere Nch = 20
and Nf= 6 are the number of channels and bands, respec-
tively) and 1140 coherence measures (Nch×(Nch1)
were extracted from each 2-sec epoch. However, only the
EEG coherences between frontal midline region (Fz) and the
rest of electrodes were considered for analysis in this study.
Therefore, a feature vector of 114 elements ((Nch 1)×Nf)
was used as the EEG connectivity measure.
EEG activity at Fz is predominantly influenced by premo-
tor area (motor planning) in the 10/20 international EEG sys-
tem. Functional connectivity between premotor and motor,
temporal, parietal and occipital regions are commonly used in
neuropsychology to study sensorimotor control. These func-
tional connectivities have been found to be correlated with
motor planning, somatosensory and visuomotor integration
processes [20]. For instance, the coherences of Fz with other
cortical regions have been found to be significantly affected
by cognitive-motor task difficulty in a Tetris game [18].
E. Quantification of Motor Control Difficulty
The direct and continuous measurement of motor control
difficulty is not easily achievable, therefore, an indirect
measure is used to estimate and quantify the level of motor
control difficulty. A dimensionless index in the frequency
domain is proposed to distinguish between the low frequency
input components of the intended human motions and the
unintended high frequency oscillations of the robot due to the
controller instability. In other words, it is assumed that the
bandwidth of the human arm motions in voluntary controls is
relatively low [21] while robot motions caused by admittance
instability mostly appear in the frequency bands higher than
human voluntary control.
Frequency domain analysis of pHRI in terms of end-
effector position [22] and interaction force [23] have been
investigated in previous studies to develop haptic stability
observers for detecting and suppressing unstable oscillations
of haptic devices. Dimeas and Aspragathos [23] demon-
strated that spectral analysis based on the position data
is mostly valid for back-drivable haptic devices, whereas,
spectral analysis of interaction forces is more appropriate
for an admittance controlled robot. This is because an
admittance controller acts as a low pass filter and attenuate
high frequency components of the control force inputs.
Inspired by [22], [23], we used frequency domain analysis
of force data to quantify instable behaviors as an indirect
measure of motor control difficulty. We used the Fast Fourier
Transform (FFT) of interaction forces to extract the mag-
nitude Pf(ω)of the frequency components ω. Force data
were down-sampled to 128 Hz and then segmented to 2-
seconds data epoch (aligned with EEG signals). A Hamming
window with 50% overlap was applied on each segment.
Periodograms were averaged to estimate spectral power and
results were normalized according to the overall window
power. The index used for the quantification of motor control
difficulty was defined as (3),
where ω0is the smallest non-zero frequency component
of the FFT; ωmis the maximum frequency component of
interest for summation which must be less than Nyquist
frequency; ωcis the cross-over frequency to distinguish
between intended and unintended motions which is between
ω0and ωm. The index (IR) ranges from 0 to 1 and a
larger value indicates lower interaction stability and higher
difficulty in motor control accordingly. To minimize the
effect of sensor noise on the IR, the value of ωmmust be
chosen around the maximum bandwidth of the robot motions.
The selection of ωc, however, is not trivial since an optimal
value is task-specific and varies between individuals with
different arm impedance and skill levels.
F. Regression Analysis
234 EEG measures (120 spectral power and 114 coher-
ences) were obtained from each 2-sec epoch. A Stepwise
Multiple Linear Regression (SWMLR) method was used for
dimensionality reduction and identifying the most dominant
predictors. In this model, the EEG measures were predictor
variables and the motor difficulty index (IR) was the re-
sponse variable. SWMLR is a numerical approach that itera-
tively enters/removes predictors with significant/insignificant
main effect on the regression model. Although SWMLR
cannot handle nonlinearities, it is a simple and robust method
to reduce the dimensionality of large EEG measures [24].
The Mahalanobis distance was used in SWMLR to enter or
remove predictors, with a threshold of 0.05 on p-value for
entering and 0.1 for removing a predictor.
Since the SWMLR assumes predictors belong to a multi-
variate normal distribution, a logarithmic transformation was
applied on the spectral power density measures [25] and
Fisher’s Z-transformation on the coherences to normalize
their distributions [26]. Combined EEG measures of all
the subjects were used for the SWMLR and then selected
predictors are used to fit a multiple linear regression model
to the response variable. To stabilize intra-individual variance
and minimize the effect of individual differences, predictor
variables were transformed into z-scores based on the distri-
bution of each subject. To evaluate the performance of the
constructed linear model to estimate motor control difficulty
and distinguish between the two manipulation conditions,
predicted responses are reported for each subject.
A. Behavioral and Subjective Assessment
Subjective and performance data were analyzed primarily
to examine the experimental design and difficulty manipu-
lation of the tasks. A paired-sample t-test was performed to
compare subjects’ performance between the two conditions.
The number of intersections with the start-shaped boundaries
was used as the primary error metric and the results are
shown in Fig. 4a. Statistical analysis revealed significantly
higher errors (lower performance) with low-damping than
high-damping condition (t(10)= 3.7,p<.005).
To further explore the effect of damping on the interaction,
the average magnitude of interaction forces in the X-Y
plane ( 1
TRT||Fh||dt) was also computed. Fig. 4b shows the
obtained results across the subjects. As expected, a paired-
sample t-test revealed higher interaction forces in the high-
damping than low-damping condition (t(10)=4.2,p<.005)
indicating a lower physical effort for low-damping.
An overall perceived workload score, ranging from 0 to
100, was calculated using the weighted combination of the
six dimensions of NASA TLX. Weights for the combined
workload score were calculated based on a set of 15 paired
comparisons between the 6 dimensions as instructed in [16].
The results of perceived workload are shown in Fig. 4c
(a) (b) (c)
Intersections (#)
Force Magnitude (N)
Combined NASA Score
Fig. 4. (a) error metric, (b) average magnitude of interaction forces, and
(c) weighted NASA score. Error bars represent one standard deviation.
(a) (b)
Power (dB)
Frequency (Hz) Frequency (Hz)
Fig. 5. (a) Unscaled and (b) scaled spectral power of the interaction forces
averaged across the subjects. Shaded error bars is one standard deviation.
and statistical analysis revealed a significantly higher work-
load for the co-manipulation with low-damping than high-
damping (t(10)=4.6,p<.001) which is consistent with the
performance results. The behavioral and self-report results
indicate that although the smaller damping facilitated the co-
manipulation with lower interaction forces, the difficulty of
controlling robot motions was higher in this case.
B. Frequency Analysis of Interaction Forces
The averaged spectral power extracted from FFT for
the two co-manipulation conditions are shown in Fig. 5.
Since the averaged interaction force was higher in the high-
damping (see Fig. 4b), a vertical shift is present between
the two spectral powers in Fig. 5a. To acquire a better
insight for comparison, the FFT magnitude of frequency
components for the low-damping are scaled based on the
ratio of DC magnitudes (Pf(0)) between the two conditions.
The result is shown in Fig 5b and the presence of high
frequency components in the interaction forces with the low-
damping is clear. Based on the results of averaged spectral
powers, an ωmvalue of 10 Hz was chosen to calculate the
instability index IR. However, an optimal ωcwas found to
be subject-dependent and variations in ωcaffected the IR
distribution for each subject, significantly. This is mainly due
to the individual differences in terms of arm impedance and
motor control characteristics. For instance, the differences
between subject 8 and 11 in terms of interaction forces in
the frequency domain are shown in the first row of Fig. 6.
To address this issue, a statistical approach is proposed
to find an individualized ωc. For each subject, the index IR
is calculated for a range of ωcfrom 0.5 to 4 Hz with a
Fig. 6. Spectral powers in the first row represent the individual differences
in terms of interaction forces and motor control in high- and low-damping
conditions. Second row shows the F-statistic values obtained from ANOVA
on the distributions of IRfor different ωcvalues. Vertical Blue lines on the
graphs denote the selected ωcfor each of the subjects.
0.25 Hz step size. Then, at each ωc, the degree of difference
between the IRdistribution of high- and low-damping are
estimated using F-statistics obtained from analysis of vari-
ance (ANOVA). The frequency at which F-statistic reached
its maximum is considered as the optimum ωc. As an
example, the F-statistic curves for subject 8 and 11 and
their maximums are shown in the second row of Fig. 6. The
results of the proposed procedure for the subjects are listed
in Table I. Note that this approach requires the existence of
a significant difference between the low- and high-damping
which is already considered in the experimental design and
verified based on the behavioral and subjective assessment.
Subject 1 2 3 4 5 6 7 8 9 10 11 Ave
ωc(Hz) 3.25 2 2.25 2.5 1.75 2 1 2 3.5 1.5 3.25 2.27
Although calculating IRusing a personalized ωccan pro-
vide a better estimate of motor control difficulty, this imposed
individual differences in the IRdistribution such that a
subject with larger ωchad smaller values of IRand shifted
distribution toward zero. In other words, individualized ωc
values are suitable for within-subjects analysis, however, for
our between-subjects regression analysis which combined
the IRindices of all the subjects, consistent responses were
required. To address this issue, the averaged ωcindex (2.27
Hz) was used to calculate IRfor all the subjects.
According to the performance and subjective assessment,
we expected higher IRvalues for the LD than HD condition.
A paired-sample t-test was performed on the averaged IR
indices of the subjects for comparison purposes. Statistical
analysis revealed a strong difference (t(10)=15.1,p<1e
7) indicating that IRwas higher in the low-damping than
high-damping. This confirms the validity of the proposed
index for the quantification of motor control difficulty.
C. Results of Regression Analysis
The SWMLR yielded a significant result (p<.001,
adj = 0.51) and 29 dominant EEG measures (13 co-
herences and 16 spectral powers) were identified as the
best predictors. Fig. 7 depicts selected EEG measures using
topographic scalp maps. A single linear model is constructed
using the selected measures of all the subjects and its estima-
tion results for the first 8 subjects are shown in Fig. 8. Despite
the existing differences between actual and estimated IR
indices from brain activity, the linear model demonstrates an
overall reliable performance, especially in the discrimination
between low and high motor workload conditions.
A set of 29 EEG spectral measures was appeared as the
most dominant predictors of motor control difficulty based
on an SWMLR method. Most of these measures belonged to
the high frequency bands (beta and gamma). EEG activity
in these bands is mainly correlated with localized sensory
integration and sensory processing demand [27] and it ele-
vates as sensorimotor demand increases. For instance, EEG
Fig. 7. Selected EEG measures from a stepwise multiple linear regression
analysis. Each topographic map corresponds to a certain frequency-band.
t-statistic is used to determine the significance of each measure using a
color-map for the spectral power and line-thickness for the coherence.
( IR )
( IR )
S1 S2 S3 S4
S5 S6 S7 S8
Fig. 8. Results of regression prediction for the first 8 subjects (S1-S8).
Red and green box-plots denote high- and low-damping, respectively. The
left two box-plots represent the actual distributions of the difficulty index,
whereas, the right plots show the predictions from EEG measures.
beta and gamma power in temporal, parietal and occipital
region have been found to be significantly affected by only
increasing motor difficulty aspects of a cognitive-motor task
when the cognitive difficulty was maintained constant [18].
Among selected EEG measures, the coherence of Fz
with T3 (left-temporal) and T4 (right-temporal) have strong
relationships with “psychomotor efficiency” hypothesis in
psycho-physiology. This hypothesis indicates that an adaptive
cortical activity and efficient networking (especially reduced
Fz-T3) identifies skilled performance of experts in sports like
golf and rifle shooting [19], [20]. Fz-T3 coherence is associ-
ated with verbal-analytical processes (self-instructing) and is
essential for early stages of motor learning, whereas, Fz-T4
connectivity becomes dominant when movements are highly
learned. It can be assumed that our subjects continuously
attempted to learn the coupled human-robot dynamics to
increase their performance. However, contrary to the learning
of free arm movements, the rate of learning dynamics in the
pHRI with unstable controllers (LD condition) becomes very
slow. Thus, such EEG indices can indicate motor control
difficulty in pHRI, particularly when the interaction is not
intuitive/well-designed and instability is likely to occur.
The linear regression model demonstrated an overall reli-
able estimation performance, however, the quality of estima-
tion varied between subjects. There are at least two factors
contributing to these variations. First, functional differences
in how participants perform the motor control task which
influences their cortical activity. This issue can be potentially
overcame by using a set of benchmark experiments to
personalize a general model (e.g., [28]). Second, individual
differences in terms of motor control characteristics as ob-
served in the calculation of ωcin section III.B.
EEG correlates of motor control difficulty in human-robot
co-manipulation is investigated. Participants performed a fine
manipulation task in which controller intrinsic dynamics is
used to manipulate motor control difficulty (decreasing the
damping and making the precise guidance more challenging).
Based on the frequency domain analysis of the interaction
force, an index is defined to distinguish between the low
frequency human voluntary force inputs and unintended
high frequency motions of the robot due to the improper
admittance. This index is used as an indirect measure of
motor control difficulty and validated through statistical
analysis and comparison with performance and subjective
assessments. An optimal selection of the parameters for the
proposed index (ωmand ωc) requires knowledge about the
robot dynamics as well as human arm impedance and motor
control characteristics. The effects of individual differences
on the spectral power of interaction forces are discussed and
a statistical approach is proposed to find an individualized
ωc. Although the average of ωcis used for the between-
subjects analysis of this study, such personalization can be
used for within-subject analysis and designs.
A stepwise multiple linear regression method is used to
identify the most correlated EEG measures to motor control
difficulty and the results are used to construct a linear model
to predict the proposed index. The relationship of the selected
measures with visuospatial processing and psychomotor ef-
ficiency as well as obtained prediction performance support
the validity of the procedure. This model can potentially be
used as a real-time adaptation strategy to adjust task difficulty
in robot assistance of motor learning and rehabilitation.
This material is based upon work supported by the Na-
tional Science Foundation under Grant No.1502287.
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Supplementary resource (1)

... These signals are useful in controlling brain-controlled exoskeletons designed to augment the user's sensorimotor functions (Agashe et al., 2016;Hong and Khan, 2017;Khan and Hong, 2017;Liu et al., 2018;Asgher et al., 2021). Moreover, a limited number of studies have been conducted to investigate the effect of robot-assisted tasks on cortical reorganization (Youssofzadeh et al., 2016;Saita et al., 2017Saita et al., , 2018Memar and Esfahani, 2018;Berger et al., 2019;Peters et al., 2020). Most of these investigations, however, did not look at the rmPFC activity. ...
... Most of these investigations, however, did not look at the rmPFC activity. Notably, A few EEG studies have reported significant functional connectivity among brain regions within the fronto-centro-parietal network during robot-assisted gait training (Youssofzadeh et al., 2016;Memar and Esfahani, 2018). This network has been regarded as the top-down executive control network, with the rmPFC area being a higher-order component (Peng et al., 2018b), may be involved in human-robot interactions. ...
... Based on recent fNIRS evidence on learning novel skillful movements, such a brainbehavior association may be governed by top-down modulation from the rmPFC (Ota et al., 2020;Kobayashi et al., 2021). Furthermore, previous neuroimaging research revealed that providing robotic assistance for voluntary limb movement may involve the fronto-centro-parietal network (García-Cossio et al., 2015;Youssofzadeh et al., 2016;Memar and Esfahani, 2018), which has been regarded as the top-down executive control network, of which the rmPFC area is a higher-order component (Peng et al., 2018b). Therefore, the current study, combined with earlier findings, suggests that top-down executive control involvement, particularly those relying on the rmPFC, is crucial to human-robot interactions during robot-assisted motor tasks. ...
Full-text available
Assistive exoskeleton robots are being widely applied in neurorehabilitation to improve upper-limb motor and somatosensory functions. During robot-assisted exercises, the central nervous system appears to highly attend to external information-processing (IP) to efficiently interact with robotic assistance. However, the neural mechanisms underlying this process remain unclear. The rostromedial prefrontal cortex (rmPFC) may be the core of the executive resource allocation that generates biases in the allocation of processing resources toward an external IP according to current behavioral demands. Here, we used functional near-infrared spectroscopy to investigate the cortical activation associated with executive resource allocation during a robot-assisted motor task. During data acquisition, participants performed a right-arm motor task using elbow flexion-extension movements in three different loading conditions: robotic assistive loading (ROB), resistive loading (RES), and non-loading (NON). Participants were asked to strive for kinematic consistency in their movements. A one-way repeated measures analysis of variance and general linear model-based methods were employed to examine task-related activity. We demonstrated that hemodynamic responses in the ventral and dorsal rmPFC were higher during ROB than during NON. Moreover, greater hemodynamic responses in the ventral rmPFC were observed during ROB than during RES. Increased activation in ventral and dorsal rmPFC subregions may be involved in the executive resource allocation that prioritizes external IP during human-robot interactions. In conclusion, these findings provide novel insights regarding the involvement of executive control during a robot-assisted motor task.
... In addition to the task type, human physiological information provides a useful insight into the quality of interaction. Neuroergonomic approaches involving human physiological signals such as electromyography (EMG) [3], [6], and electroencephalography (EEG) [7]- [9] have been studied in relation to pHRI. For instance, Grafakos et al. [3] used muscle co-activation information to adjust the damping parameter of an admittance controlled robot and showed that variable admittance control significantly decreased the energy consumption of the operator and also improved the performance during a fine motor task. ...
... For instance, Grafakos et al. [3] used muscle co-activation information to adjust the damping parameter of an admittance controlled robot and showed that variable admittance control significantly decreased the energy consumption of the operator and also improved the performance during a fine motor task. Memar and Esfahani [7] used EEG monitoring system to identify subject-independent brain bio-markers to reliably distinguish between the high and low levels of workload in the co-manipulation task. While other modalties such as interaction force failed to capture the subject's perceived level of workload (measured via NASA-TLX), their identified brain biomarkers follows the same pattern as NASA-TLX even when the experimental conditions were changed. ...
... In particular, neuroergonomics (Mehta and Parasuraman, 2013)-especially computational neuroergonomics (Farahani et al., 2019)-can advantageously exploit twinning for understanding how the human nervous system works in real contexts (Cheng et al., 2022), and improving the design of any item interacting with it. This is certainly true about neuroergonomics in HRI contexts (Cassioli et al., 2021) for applications like monitoring motor control difficulties (Memar and Esfahani, 2018), providing robots with adaptive features (Lim et al., 2021), and improving brain-robot interfaces (Mao et al., 2019). Overall, the exploitation of DTs in this field can inherit the corpus of knowledge in neuroscience, especially when human-machine interactions are investigated (Gaggioli, 2018;Ramos et al., 2021). ...
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This opinion paper proposes the introduction of phygital features in twinning design for neuroergonomics in human-robot interaction.
... Kumar et al. (2020) have emphasized the use of the regression analysis controller for path planning of a biped robot in an unknown environment. Memar & Esfahani (2018) have studied the frequency analysis of mechanism control in the movement of a humanoid robot in the cluttered environment to draw out various conclusions for implementation of an optimum solution. Soeterik et al. (2020) have emphasized the study of nerve mechanisms in a robotic structure for an efficient mechanized solution. ...
Humanoid robotics is an emerging area of interest in the current engineering research scenario, owing to its ability to impersonate human deportment and emulate various jobs. The given article emphasizes the development and implementation of a hybrid navigational controller to optimize the path length, energy demand, and time spent for accomplishing assigned tasks. The proposed navigational controller is developed by hybridizing the metaheuristic Improved Spider Monkey Optimization (ISMO) approach and the Regression Analysis (RA) approach. Various input parameters like obstacle and target locations are fed to the RA approach that implements a proper navigational direction selection. And it forwards to the SMO approach that is improved using piecewise B-Spline path smoother, which exercises further refinement of the output turning angle and smoothness of path around obstacles. Simulations and real-time experiments are undertaken using different controllers involving single robot systems, which shows the proposed controller’s superiority. An average improvement of 13.72 % and 13.94 % in path length against RA in simulation and experiment, respectively, and an average improvement of 7.59 % and 7.5 % in path length against ISMO in simulation and experiment, respectively, is obtained. It is further evaluated for navigation by implementing in a single robot having a multi-target problem. Multiple robot navigation has to deal with the self-collision situations that are solved by prioritizing the specified robot using the dining philosopher controller. It is implemented in the proposed controller for navigation of multiple robots to solve the conflict. Both scenarios are tested in the simulation environment and ratified in the experimental environment. Average deviation under 5 % for path length and time spent for single robot navigation and multiple robot navigation is obtained, which shows a good agreement with each other. Energy efficiency test has been performed in contrast to default controller of NAO for various joints, and an average improvement of 8.16 %, 5.9 % and 20.57 %, has been recorded in torque for ankle, knee and hip, respectively. Comparison is carried with an established navigational controller in a similar environmental setup shows an improvement of 8.6 % and 10.365 %, respectively, in path length and time spent. The results obtained from these setups prove the proposed hybrid controller to be robust, efficient and superior while performing path planning.
... However, the solution is not unique for a given set of end effector position. For more information, the reader is referred to [26]. ...
Full-text available
This study investigates the effect of haptic control strategies on a subject’s mental engagement during a fine motor handwriting rehabilitation task. The considered control strategies include an error-reduction (ER) and an error-augmentation (EA), which are tested on both dominant and nondominant hand. A noninvasive brain–computer interface is used to monitor the electroencephalogram (EEG) activities of the subjects and evaluate the subject’s mental engagement using the power of multiple frequency bands (theta, alpha, and beta). Statistical analysis of the effect of the control strategy on mental engagement revealed that the choice of the haptic control strategy has a significant effect ([Formula: see text]) on mental engagement depending on the type of hand (dominant or nondominant). Among the evaluated strategies, EA is shown to be more mentally engaging when compared with the ER under the nondominant hand.
... Participants felt fear induced by the possibility of colliding with the real-environment obstacles and therefore decided to manipulate their movements with caution. Although there is a previous study which has correlated physiological activity with motor control [47], we cannot adamantly support that electrodermal activity could function as an index for human movement behavior due to the weak correlations. Such correlations demand further investigation in order to conclude whether a walking task with matching and mismatching conditions could be used as a method to determine human behavior. ...
... Participants felt fear induced by the possibility of colliding with the real-environment obstacles and therefore decided to manipulate their movements with caution. Although there is a previous study which has correlated physiological activity with motor control [47], we cannot adamantly support that electrodermal activity could function as an index for human movement behavior due to the weak correlations. Such correlations demand further investigation in order to conclude whether a walking task with matching and mismatching conditions could be used as a method to determine human behavior. ...
... However, the solution is not unique for a given set of end effector position. For more information, the reader is referred to [26]. ...
From early adoption in rehabilitation, the brain–machine interfaces (BMIs) have dovetailed into applications empowering humans in controlling external devices such as prosthesis and wheelchairs with a high level of autonomy. The success of such brain–machine interfaces depends on the decoding algorithms that translate the brain activity into the human’s intention or cognition state. Taking advantage of this decoding, a machine can have a robust perception of human’s cognitive state and modify its actions accordingly. This decoding process can be viewed as a machine learning problem where features of brain activities are mapped to some labeled events or classes in a controlled environment. This mapping traditionally relies on the subject and task-specific signal processing approaches. Thus, the conventional machine learning methods fail to generalize well and transfer the learned features between different tasks and subjects, especially in out-of-the-lab applications. Recently, deep learning (DL) has shown great success in learning the patterns from very large data and generalizing well on different applications. With respect to brain activity analysis, deep learning and reinforcement learning (RL) techniques can significantly simplify analysis pipelines and facilitate better generalization between subjects, tasks, and also learn intricate coupled dynamics. In this regard, this paper provides information on the state of the art and challenges in implementing deep learning and reinforcement learning algorithms in brain–machine interfaces. In order to demonstrate the use of deep learning techniques in BMIs, we also present a case study of physical human–robot interaction where the brain activity is used to classify the task difficulty while interacting with the robot.
Efficient human-robot collaboration during physical interaction requires estimating the human state for optimal role allocation and load sharing. In this study, we present a transfer learning approach based on convolution neural networks (CNN) to predict motor control difficulty from muscle activities of subject (surface EMG) data in a physical human-robot interaction (pHRI) task. Twenty six individuals participated in a pHRI experiment where a subject guides the end-effector with different levels of motor control difficulty. The motor control difficulty is varied by changing the damping parameter of the robot from low to high and constraining the motion to gross and fine movements. A CNN network with raw EMG as input is used to classify the motor control difficulty. The CNN transfer learning is compared against Riemann geometry-based Procrustes analysis (RPA). With very few labeled samples from new subjects, we demonstrate that the CNN-based transfer learning approach (avg. 69.77%) outperforms the RPA transfer learning (avg. 59.20%). Moreover, we observe that the subject's skill level in the pre-trained model has no significant effect on the transfer learning performance of the new users.
Full-text available
An ideal physical human–robot interaction (pHRI) should offer the users robotic systems that are easy to handle, intuitive to use, ergonomic and adaptive to human habits and preferences. But the variance in the user behavior is often high and rather unpredictable, which hinders the development of such systems. This article introduces a Personalized Adaptive Stiffness controller for pHRI that is calibrated for the user’s force profile and validates its performance in an extensive user study with 49 participants on two different tasks. The user study compares the new scheme to conventional fixed stiffness or gravitation compensation controllers on the 7-DOF KUKA LWR IVb by employing two typical joint-manipulation tasks. The results clearly point out the importance of considering task specific parameters and human specific parameters while designing control modes for pHRI. The analysis shows that for simpler tasks a standard fixed controller may perform sufficiently well and that respective task dependency strongly prevails over individual differences. In the more complex task, quantitative and qualitative results reveal differences between the respective control modes, where the Personalized Adaptive Stiffness controller excels in terms of both performance gain and user preference. Further analysis shows that human and task parameters can be combined and quantified by considering the manipulability of a simplified human arm model. The analysis of user’s interaction force profiles confirms this finding.
Conference Paper
Full-text available
This paper presents the design of a novel variable stiffness gripper with two parallel fingers (jaws). Compliance of the system is generated by using permanent magnets as the nonlinear springs. Based on the presented design, the position and stiffness level of the fingers can be adjusted simultaneously by changing the air gap between the magnets. The modeling of magnetic repulsion force and stiffness are presented and verified experimentally. An experiment is also conducted to demonstrate the functionality of the gripper to improve safety when a fragile object was grasped and the gripper collided with an obstacle.
Full-text available
In the design of a compliant admittance controller for physical human-robot interaction it is necessary to ensure stable and effective cooperation. The stability of the admittance controller is mainly threatened by a stiff environment. Many methods that guarantee stability in arbitrary environments, impose conservative control gains that limit the effectiveness of the cooperation. Inspired by previous work in frequency domain stability observers, a method is proposed in this paper to detect unstable behavior and stabilize the robot with online adaptation of the admittance control gains. The introduced instability index is based on frequency domain analysis, which very quickly detects unstable behavior by monitoring high frequency oscillation in the force signal. To treat the instability, an adaptation scheme of the admittance parameters is proposed, that relaxes conservative gains and improves the cooperation by considering the effect of variable admittance on the operators' effort. We investigate two human-robot co-manipulation tasks; cooperation within a zero stiffness environment and cooperation in contact with a stiff double-wall virtual environment. The proposed methods are validated experimentally with a number of subjects in cooperation with an LWR manipulator.
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This paper presents an experimental study on human–robot comanipulation in the presence of kinematic redundancy. The objective of the work is to enhance the performance during human–robot physical interaction by combining Cartesian impedance modulation and redundancy resolution. Cartesian impedance control is employed to achieve a compliant behavior of the robot's end effector in response to forces exerted by the human operator. Different impedance modulation strategies, which take into account the human's behavior during the interaction, are selected with the support of a simulation study and then experimentally tested on a 7-degree-of-freedom KUKA LWR4. A comparative study to establish the most effective redundancy resolution strategy has been made by evaluating different solutions compatible with the considered task. The experiments have shown that the redundancy, when used to ensure a decoupled apparent inertia at the end effector, allows enlarging the stability region in the impedance parameters space and improving the performance. On the other hand, the variable impedance with a suitable modulation strategy for parameters’ tuning outperforms the constant impedance, in the sense that it enhances the comfort perceived by humans during manual guidance and allows reaching a favorable compromise between accuracy and execution time.
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In the context of task sharing between a robot companion and its human partners, the notions of safe and compliant hardware are not enough. It is necessary to guarantee ergonomic robot motions. Therefore, we have developed Human Aware Manipulation Planner (Sisbot et al., 2010), a motion planner specifically designed for human–robot object transfer by explicitly taking into account the legibility, the safety and the physical comfort of robot motions. The main objective of this research was to define precise subjective metrics to assess our planner when a human interacts with a robot in an object hand-over task. A second objective was to obtain quantitative data to evaluate the effect of this interaction. Given the short duration, the “relative ease” of the object hand-over task and its qualitative component, classical behavioral measures based on accuracy or reaction time were unsuitable to compare our gestures. In this perspective, we selected three measurements based on the galvanic skin conductance response, the deltoid muscle activity and the ocular activity. To test our assumptions and validate our planner, an experimental set-up involving Jido, a mobile manipulator robot, and a seated human was proposed. For the purpose of the experiment, we have defined three motions that combine different levels of legibility, safety and physical comfort values. After each robot gesture the participants were asked to rate them on a three dimensional subjective scale. It has appeared that the subjective data were in favor of our reference motion. Eventually the three motions elicited different physiological and ocular responses that could be used to partially discriminate them.
Supervisory control environments, such as the NASA control room can induce high workload levels in situations where a single error is capable of costing millions of dollars. An intelligent system can improve human supervisor performance by monitoring the human’s workload levels and intelligently adapting the system capabilities, such as adapting the interaction medium or reallocating roles and responsibilities between the human and the system. Systems capable of responding promptly and accurately to the human’s changes in workload require a workload assessment algorithm that can detect changes to all components of workload in real time. A review of 24 workload assessment algorithms across six task domains is provided. Each algorithm is reviewed based on four criteria: sensitivity, diagnosticity, suitability, and generalizability. The majority of the reviewed algorithms were developed for a specific task domain and are unable to generalize different tasks. Further, the majority of the algorithms do not account for individual differences, only assess one or two workload components, and do not classify underload. IEEE
Conference Paper
This paper presents the modeling and dynamic parameter identification of the 6-DoF SCHUNK Powerball LWA 4P robotic arm. Precise positioning, zero backlash and compact design of the joints which integrate two perpendicular axes, make this robot ideal for service robotics applications and human-robot interaction. Due to the significant effect of the lubricant temperature on the behavior of viscous friction in the harmonic drives, a systematic procedure is developed to overcome this problem. A series of experiments have been conducted to model the friction at each joint, then the procedure of identification has been applied based on an inverse dynamic model and linear least-square techniques. Finally, a verification trajectory is executed by the robot to validate the estimated parameters of the system.
Cortical activity and perception are not driven by the external stimulus alone; rather sensory information has to be integrated with various other internal constraints such as expectations, recent memories, planned actions, etc. The question is how large scale integration over many remote and size-varying processes might be performed by the brain. We have conducted a series of EEG recordings during processes thought to involve neuronal assemblies of varying complexity. While local synchronization during visual processing evolved in the gamma frequency range, synchronization between neighboring temporal and parietal cortex during multimodal semantic processing evolved in a lower, the beta1 (12-18 Hz) frequency range, and long range fronto-parietal interactions during working memory retention and mental imagery evolved in the theta and alpha (4-8 Hz, 8-12 Hz) frequency range. Thus, a relationship seems to exist between the extent of functional integration and the synchronization-frequency. In particular, long-range interactions in the alpha and theta ranges seem specifically involved in processing of internal mental context, i.e. for top-down processing. We propose that large scale integration is performed by synchronization among neurons and neuronal assemblies evolving in different frequency ranges.
This paper uses physiological measurements to estimate human workload and effort in physical human–robot interaction. Ten subjects performed 19 consecutive task periods using the ARMin robot while difficulty was varied along two scales. Three physiological modalities were measured: electroencephalography, autonomic nervous system (ANS) responses (electrocardiography, skin conductance, respiration, skin temperature) and eye tracking. After each task period, reference workload and effort values were collected using the NASA Task Load Index. Machine learning was used to estimate workload and effort from physiological data. All three physiological modalities performed significantly better than random, particularly using nonlinear estimation algorithms. The most important ANS responses were respiration and skin conductance, while the most important electroencephalographic information was obtained from frontal and central sites. However, all three physiological modalities were outperformed by task performance and movement data. This suggests that future studies should try to demonstrate advantages of physiological measurements over other information sources.