A MULTIMODAL BRAIN-ARM INTERFACE FOR OPERATION

OF COMPLEX ROBOTIC SYSTEMS AND UPPER LIMB MOTOR

RECOVERY

Michele Folgheraiter

, Elsa Andrea Kirchner

1,2

, Anett Seeland

, Su Kyoung Kim

1,2

Mathias Jordan

, Hendrik Woehrle

, Bertold Bongardt

, Steffen Schmidt

Jan Christian Albiez

and Frank Kirchner

1,2

German Research Center for Artiﬁcial Intelligence (DFKI), Robotics Innovation Center

Robert-Hooke-Strasse 5, D-28359 Bremen, Germany

University of Bremen, Robotics Lab, Robert-Hooke-Strasse 5, D-28359 Bremen, Germany

Keywords:

Haptic Interface, Bio-Inspired Design, Brain-Computer Interface, Wearable Exoskeleton, Support Vector

Machine, Adaptive Brain Reading Interface, Electroencephalogram, Lateralized Readiness Potential, Bere-

itschaftspotential.

Abstract:

This work introduces the architecture of a novel brain-arm haptic interface usable to improve the operation of

complex robotic systems, or to deliver a ﬁne rehabilitation therapy to the human upper limb. The proposed

control scheme combines different approaches from the areas of robotics, neuroscience and human-machine

interaction in order to overcome the limitations of each single ﬁeld. Via the adaptive Brain Reading Interface

(aBRI) user movements are anticipated by classiﬁcation of surface electroencephalographic data in a mil-

lisecond range. This information is afterwards integrated into the control strategy of a wearable exoskeleton

in order to ﬁnely modulate its impedance and therefore to comply with the motion preparation of the user.

Results showing the efﬁcacy of the proposed control approach are presented for the single joint case.

1 INTRODUCTION

Service robotics systems are becoming more and

more complex both from the manipulation and lo-

comotion capabilities point of view. This inevitably

brings new challenges for their control strategy, which

nowadays on the one hand is still lacking a complete

autonomy and on the other hand is constantly required

to accomplish the intention of the human that is inter-

acting or supervising the machine.

To improve the interaction between the machine

and the human it is therefore necessary to develop

proper interfaces that allow a bidirectional communi-

cation: namely interpret the will of the user and trans-

late it into a proper command for the robot, and reﬂex

to the user the perception of the environment that sur-

round the robot.

Exoskeleton interfaces for the upper and lower

limbs have gained increasing attention in the last

decade. They can be employed in a wide range of

applications: as master devices for teleoperation, as

haptic interfaces that mediate the interaction of the

user with virtual objects, as physiotherapy tools, or

as devices that enhance the strength of the user.

The ESA Exoskeleton (Schiele and van der Helm,

2006), developed at the European Space Agency, is an

example of upper limb interface equipped with 16 De-

grees Of Freedom (DOF), eight of which moved via

a tendon-based system. The device is intended to al-

low astronauts inside the International Space Station

(ISS) to teleoperate EUROBOT, a humanoid robot

that will support extravehicular activities (EVA). Al-

though some of the joints are not directly actuated, the

overall arm-exoskeleton system is fully controllable.

This is due to the parallel structure that is formed dur-

ing the interaction between limb and the device.

At the department of Electrical Engineering, Uni-

versity of Washington, a third generation dual-arm (7

+ 7 DOF) exoskeleton (Rosen et al., 2005) was devel-

oped on the base of anthropometric data. The system

150

Folgheraiter M., Andrea Kirchner E., Seeland A., Kyoung Kim S., Jordan M., Wöhrle H., Bongardt B., Schmidt S., Christian Albiez J. and Kirchner F..

A MULTIMODAL BRAIN-ARM INTERFACE FOR OPERATION OF COMPLEX ROBOTIC SYSTEMS AND UPPER LIMB MOTOR RECOVERY.

DOI: 10.5220/0003135501500162

In Proceedings of the International Conference on Biomedical Electronics and Devices (BIODEVICES-2011), pages 150-162

ISBN: 978-989-8425-37-9

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

is ﬁxed to the ﬂoor via a special frame; each arm has

seven single-axis revolute joints moved by cables and

pulleys that transmit the forces from the actuators lo-

cated in the robot base. A precise placement of the

joint singularities in the periphery of the workspace

guarantees high-force isotropy and avoids workspace

restrictions at the same time.

The SARCOS MasterArm (from SARCOS Inc.)

(Mistry et al., 2005) is a 7 DOF hydraulically actuated

arm-exoskeleton. The system is hung up by a ﬁxed

platform and the user operates the device by holding

it at the most distal joint. The shoulder in this case

remains unconstrained, but is positioned such that the

three rotation axes of the exoskeleton approximately

intersect with the center of rotation of the articulation.

Another important ﬁeld where exoskeletons can

bring signiﬁcant beneﬁts is represented by rehabilita-

tion. In particular the usage of robotic devices is suit-

able for the long-term therapy of patients with neu-

rodegenerative diseases or trauma-inﬂicted impair-

ments (Reinkensmeyer et al., 2001; Harwin et al.,

2006).

Robotic rehabilitation is expected to allow thera-

peutic interventions that are tailored to patients’ con-

ditions, enabling repeatable training curricula, objec-

tiﬁed assessment of progress, and can be used by pa-

tients independently. Neuro-plasticity, i.e., the reorga-

nization of tissue in the central nervous system (Har-

win et al., 2006), is a key motivation for robotic re-

habilitation. Stimulation of the sensorimotor system

with increased levels of proprioceptive input gener-

ated by robotic rehabilitation devices has been argued

(Reinkensmeyer et al., 2004) to yield higher levels of

functional recovery than those achieved by conven-

tional approaches in some studies.

The MANUS system, initially developed at MIT,

has been in daily operation since 1994 delivering

therapy to stroke patients at the Burke Rehabilitation

Hospital (Krebs et al., 2004). MANUS is a planar

module which provides two translational degrees-of-

freedom for elbow and forearm motion. The mod-

ule is portable and consists of a direct-drive ﬁve

bar-linkage SCARA (Selective Compliance Assem-

bly Robot Arm). The system has been commercial-

ized and is currently marketed by Interactive Motion

Technologies Inc.

ARMin II is the second prototype of a robot for

arm therapy applicable to train activities of daily liv-

ing in the virtual reality, developed at ETH Zurich.

ARMin II has a semi-exoskeletal (grounded) struc-

ture with seven active degrees of freedom, ﬁve ad-

justable segments to ﬁt different user sizes, and is

equipped with position and force sensors. The sys-

tem is back-drivable and enables the implementation

of impedance or admittance-based patient cooperative

control strategies via analysis of force and torque sen-

sor data.

The analysis of brain activity for the purpose of

rehabilitation is mainly realized by the application of

Brain-Computer Interfaces (BCIs) to restore the abil-

ity to communicate, manipulate, or move (Wolpaw

et al., 2002). One of the ﬁrst BCIs was based on

the detection of the ERP P300 (Farwell and Donchin,

1988) which is elicited in the brain whenever a seldom

and important stimulus is processed (Squires et al.,

1975). Similarly, brain activity related to movement

preparation or the imagination of movement is used

to directly control computer programs, machines, or

protheses by the use of BCIs. For that, analysis fo-

cuses on the detection of event-related synchroniza-

tion (ERS) or desynchronization (ERD) in certain fre-

quency bands of the EEG (Leeb et al., 2006) or ERPs

that are related to movement preparation (Blankertz

et al., 2006), like the BP or the LRP (Kornhuber and

Deecke, 1965; Masaki et al., 2004).

Movement related potentials (such as BP/LRP),

evoked during voluntary movement tasks, have been

analyzed by several BCI research groups. For in-

stance, Blankertz et al. showed that it is possible to

distinguish single trials of left vs. right ﬁnger move-

ment in a self-paced keyboard typing task (Blankertz

et al., 2003; Krauledat et al., 2004). The data pro-

cessing for every channel is done ﬁrst by emphasizing

the end of each window of length 1280 ms sampled at

100 Hz with a cosine function of the form

w(n) := 1 − cos(

n · π

128

) for n = 0, . . . , 127, (1)

since for a fast detection of the LRP always the last

points in time of the extracted signal are most im-

portant. After that, a frequency ﬁltering with a pass-

band from 0.4 to 3.5 Hz and a subsampling to 20 Hz

is applied. Then the last 4 data samples in each win-

dow are used as features and are classiﬁed by Regu-

larized Fisher Discriminant. For a pseudo-online test

in a feedback session, where subjects see the classiﬁer

output as a moving trace on a screen, sliding windows

are extracted every 40 ms.

The data set generated by Blankertz et al. was also

published for the NIPS*2001 (BCI) post-workshop

Competition 2001 (Sajda et al., 2003) and analyzed

by several other authors with different approaches.

For example, Pires et al. ﬁltered below 5 Hz and gen-

erated features using a regression model. These fea-

tures were then classiﬁed with Linear Discriminant

Analysis (Pires et al., 2007). In contrast Li et al. used

the amplitude difference between left and right elec-

trodes as features, after a ﬁltering from 0 to 3 Hz. Sub-

sequently spatial ﬁltering is applied and features are

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LIMB MOTOR RECOVERY

151

Figure 1: The Overall Control Framework Architecture.

classiﬁed with a perceptron. Furthermore they used

additionally frequency features for the detection of

ERD/ERS and improved classiﬁcation performance

(Li et al., 2004). Nevertheless both approaches were

not tested in an online manner.

Beside the discrimination of right vs. left move-

ment, it is in an online scenario of particular impor-

tance to detect the movement per se. For this pur-

pose, Blankertz and colleagues outlined another clas-

siﬁer, which is tested in combination with the clas-

siﬁer performing the left-right movement discrimina-

tion. However, the authors admit, that further research

has to be done to improve the distinctness of the clas-

siﬁer output (Blankertz et al., 2002).

2 THE MULTI-MODAL

CONTROL ARCHITECTURE

One main goal of our project is to develop a multi-

modal control interface that will enable a single hu-

man operator to control such complex robotic sys-

tems in an intuitive way. The interplay between a

safe wearable exoskeleton, an adaptive Brain Reading

Interface (aBRI), and a multipurpose virtual environ-

ment will lift the control of different robotic systems

up to the next level, enabling to dissolve the bound-

aries between robot and operator, as well as combin-

ing the cognitive abilities of the human with the ro-

bustness of the robotic system. Figure 1 depicts the

overall control scenario where the user, at the cen-

ter of the architecture, is wearing the three interfaces.

This paper mainly focuses on the exoskeleton and the

aBRI; the 3D-Immersion and Control Center modules

are therefore not described in detail. In particular, a

control scheme is proposed that uses the information

coming from the detection of movement preparation

(Section 4) to modulate the behavior of the haptic in-

terface and therefore to improve the interaction expe-

rience between the user and the robotic system. How-

ever, for the acquisition of the electroencephalogram

(EEG), a virtualized scenario is used as explained in

Section 4.2.

Due to the fact that the kinematics of the exoskele-

ton and its control system were tailored on the base of

the anatomy and neurophysiology of the human limb

(Section 3), such interface is also suitable for deliv-

ering a precise and customized rehabilitation therapy

for the human arm.

In particular, we want to introduce an approach

for the support of patients suffering from a partial

lack of the control of their motor system. For sev-

eral reasons, e.g., after an accident, it might be hard

for a patient to move an injured limb properly with

the required force and precision. To assist recovery of

the patient, we want to apply a wearable exoskeleton

whose functionality is based on a bio-inspired con-

trol strategy that allows a natural integration of pre-

dictions regarding the prepared movements of the pa-

tient. Pre-knowledge about movements allows the

system to support the patient more accurately. The

movement prediction is based on the on-line analy-

BIODEVICES 2011 - International Conference on Biomedical Electronics and Devices

152

sis of brain activity. The integration of this informa-

tion into the control architectures allows to support

the patient even in situations where he or she can-

not move his/her limb by him-/herself, but brain ac-

tivity can still be measured. For this purpose, single

trial online-detection of movement-related brain ac-

tivity without knowing movement onset has to be ac-

complished. To achieve this, our framework for aBRI

(Kirchner et al., 2009; Kirchner et al., 2010) was ex-

tended to allow movement prediction based on the de-

tection of certain event related potentials (ERPs), i.e.,

the lateralized readiness potential (LRP) and the Be-

reitschaftspotential (BP) (Section 4), and integrated

into the brain-arm interface. For best results several

combinations of ﬁlters and their related data process-

ing times are compared.

The brain-arm haptic interface will not only support

the execution of the movement by reinforcing the

strength, but also by supporting the patient for a pre-

cise trajectory execution. Due to the fact that the joint

impedance is modulated via the integration of the

movement preparation signal and the measurement

of the exoskeleton-arm interaction forces, the system

will always respond in a natural way according to the

prepared user movement. The presence - at joint level

- of different safety mechanisms (Folgheraiter et al.,

2009c) will guarantee that the torque/force applied to

the limb will be limited within a proper range. This

is very important in order to avoid uncomfortable or

even dangerous postures for the user.

3 EXOSKELETON

INTERFACE - DESIGN

AND CONTROL

The arm interface is a 9 DOF wearable exoskele-

ton designed to deliver a ﬁne force feedback in three

contact points with the user limb: shoulder, upper-

arm, and forearm. The device is hydraulically as

well as pneumatically actuated; the pump, compres-

sor, and primary power supply are located outside the

exoskeleton to avoid additional weight to the system.

In total there are seven actuated joints: ﬁve lo-

cated in the shoulder/upper-arm and two in the fore-

arm (Figure 2). Two additional passive joints allow

the wrist supination-pronation, ﬁnally two adjustable

links permit the adaptation of the shoulder and upper

arm lengths to the speciﬁc user anthropometry.

The haptic interface is intended to be easily worn

by the user due to the presence of adjustable strips and

belts around the upper-body, upper arm, and wrist.

During operation, all reaction forces are loaded on the

Figure 2: The 9-DOF arm exoskeleton.

back of the operator, therefore the weight of the sys-

tem is not directly perceived on the limb. Although a

big effort was dedicated to keep the structure as light

as possible, at the moment the full prototype has a

weight of 14 Kg, as thus for usage as rehabilitation

tool needs to be supported by a tutor, which can be

connected in the back part of it.

3.1 Exoskeleton Kinematics Design

To deﬁne the kinematic architecture of the exoskele-

ton, at ﬁrst we modeled the human arm using a com-

mon notation from robotics, this enables us to easily

combine the two systems in order to study the over-

all mobility. Figure 3 depicts, that the coupled arm-

exoskeleton system shows different closed kinematics

loops. This in general brings restriction to the move-

ments of the limb, requesting a proper analysis of the

combined system.

Through a series of simulations, and by the in-

tegration of real arm trajectories, it was possible to

set the requirements to design the ﬁnal device (Fol-

gheraiter et al., 2009b), e.g., number and conﬁgura-

tion of the DOF, angular range for each joint, opti-

mal link dimensions, joint-articulation alignment, and

maximum joint torque required. The kinematic archi-

tecture, depicted in Figure 3 (blue parts), includes a

total of 9 DOF. Starting from the back connection to-

ward the upper arm contact point (where Ja5 is lo-

cated), the presence of 5 actuated DOF guaranty free

mobility of the shoulder as well as the possibility to

deliver a three-dimensional force feedback. From the

upper-arm toward the forearm other 4 DOF allow the

elbow ﬂexion-extension and the forearm pronation-

supination. To notice that the additional DOF that

let the wrist ﬂexion-extension are not consider in this

study; this is due to the fact that the last contact point

between the haptic interface and the arm is located

before the wrist articulation.

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153

Figure 3: The simpliﬁed human arm kinematic model (right

side in gray ) combined with the exoskeleton model (in

blue).

Figure 4: Exoskeleton single joint control scheme; (left

square) force controller; (right square) cascade PID posi-

tion controller

3.2 Single Joint Control System

This section focuses on the description of the sin-

gle joint position/force controller. Both elements are

based on classical controller approaches building the

basis of the bio-inspired control scheme (Folgheraiter

et al., 2009a), (Folgheraiter et al., 2009c) as well as

the connection to the aBRI interface. Figure 4 shows

a schematic of the low-level joint controller consist-

ing of these two parts.

In Figure 4, the right square marks the cascade

PID position controller, while the left square marks

the part including the adaptive admittance controller.

Within the position controller structure, q

describes

the desired actuator position given by a trajectory gen-

erator located in a superior part of the overall con-

trol system, and q

act

is the actual actuator position.

Results for single joint position control are shown in

Section 5.1.

The force controller includes a variable admit-

tance ﬁlter with a speciﬁed stiffness K, damping D,

and inertia M, while q

is the change in actuator refer-

ence position due to an error between the desired in-

teraction force F

and the measured force F

meas

. The

described force controller passively regulates the in-

teraction force between the operator and the exoskele-

ton by introducing a mass-spring-damper relationship

between q

and F (Hogan, 1985). The transfer func-

tion of the admittance ﬁlter is given by the following

equation:

+ Ds + K

· (F

− F

meas

). (2)

The resulting deviation q

caused by a measured con-

tact force F

meas

has to be tracked by the subsequent

position controller described before. Through adap-

tion of the three coefﬁcients K, D, and M using the

result of the movement preparation detection one can

specify the reaction of the device to an occurring

movement. Generally the stiffness K regulates the

amplitude of interaction force for a given trajectory,

while the damping factor D speciﬁes whether more or

less energy is to be dissipated during the movement.

A veriﬁcation of the effect of these two parameters to

admittance ﬁlter characteristic is done in 5.1. The in-

ertia factor M is neglected because the device should

not propagate a virtual mass to the operator. If move-

ment preparation is detected the values of K and D

are adjusted using Equations 3 and 4, causing the ex-

oskeleton to respond more or less sensitively to any

given force input generated by operator movements.

K = K

− (S

LRP

· ∆K

) (3)

D =

(

+ (S

LRP

· ∆D

) , S

LRP

< ε

−

1−ε

LRP

− ε) · ∆D

, S

LRP

≥ ε

(4)

Equation 3, K

is representing the stiffness coefﬁ-

cient when no preparation of movement (LRP and

BP) is detected, S

LRP

∈ [0, 1] is the score value the

LRP detection algorithm provides to the exoskeleton

control system, and ∆K

is the maximum allowed

change of stiffness due to a detected movement prepa-

ration. Further, in Equation 4, D

is representing the

damping coefﬁcient in case of no movement prepara-

tion (LRP and BP) detection, ∆D

is the maximum

allowed change of damping ratio, while ε is repre-

senting a threshold the LRP score has to overcome to

guarantee that a starting movement is quite possible.

In the overall context ε is used to tune the force con-

troller more sensitively according to the LRP detec-

tion algorithm. This means explicitly, that in case of

low LRP scores, smaller than the threshold ε, the pos-

sibility of a movement of the operator is very low. In

any case the stiffness is lowered by Equation 3 result-

ing in a higher sensitivity of the device to any given

BIODEVICES 2011 - International Conference on Biomedical Electronics and Devices

154

input. By a parallel increase of the damping the de-

vice is reacting more conveniently.

If the LRP score overcomes the threshold, stiff-

ness as well as damping are reduced, preparing the

system to react instantaneously to an expected move-

ment of the operator. Results of single joint admit-

tance control using variable stiffness and damping are

presented in Section 5.1.

4 aBRI

INTERFACE - ARCHITECTURE

AND METHODOLOGY

Most applications in the context of rehabilitation that

make use of the EEG, namely brain activity recorded

on the surface of the skull, are situated in the ﬁeld

of BCIs. Those interfaces serve to restore the ability

of disabled patients to communicate (Wolpaw et al.,

2002) by enabling them to control machines or com-

puter programs by brain activity.

Our approach is not to use brain activity to di-

rectly control any devices, but to read the brain ac-

tivity to gain insight into the human mental and/or

cognitive state (Kirchner et al., 2009; Kirchner et al.,

2010). The developed interface is called adaptive

Brain Reading Interface (aBRI) and can be used to

”inform” a control scenario about, e.g., warning pro-

cessing of the user or, like in this application, the

preparation to move a certain limb. In both cases, the

aBRI will work in the background and does not re-

quire any cognitive resources of the user. The aBRI,

utilized for movement prediction, is combined and

linked via a bio-inspired control architecture with a

wearable exoskeleton which together form the brain-

arm haptic interface.

Before a movement is executed, it is planned by

the brain. Especially directed movements, in compar-

ison to, e.g., reﬂexes, are planned and controlled by

the cortex. Several areas of the cortex are involved

in motor planning. By detecting the activity of those

brain areas it is possible to predict the occurrence of

movement as well as which part of the body will be

moved. As presented later on, movement onset pre-

diction is done in real-time by single trial detection of

ERPs called LRP and the earlier evoked BP (Kornhu-

ber and Deecke, 1965; Balconi, 2009).

In comparison to the detection of changes in the

frequency range of the EEG (Leeb et al., 2006), the

detection of ERPs is possibly faster, since ERPs are

temporally clearly differentiated brief events and es-

pecially in the case of the BP much earlier to de-

tect. The BP, also called readiness potential (RP),

can be detected even seconds before the start of the

movement though a differentiation between the side

of movement e.g., right or left, and the part of the

body, e.g. arm or leg, can only be predicted by the

later evoked LRP which is the lateralized part of the

RP. The LRP can be measured above certain parts of

somatotopically organized brain areas to differentiate

between periods of movement and rest of correspon-

dent body areas.

Our approach therefore allows in principle not

only to detect movement onset very early by detect-

ing the BP but also to differentiate between the side of

movement and certain parts of the body by detecting

the LRP. We also believe that the detection of ERP

components is less prone to artifacts possibly origi-

nating from muscle activity or noise induced by the

exoskeleton, e.g., by hydraulic pumps or electroni-

cally controlled switches. Especially muscle artifacts

are a major problem since the subject is not seated in

a chair but is standing and therefore constantly mov-

ing slightly while wearing and using the exoskele-

ton. Since the chosen data processing for ERP detec-

tion needs only information in the very low frequency

range (below 4 Hz), rigorous ﬁltering can be applied

that eliminates or at least strongly reduces the before

mentioned artifacts.

The challenge of our approach is the detection

of a rather weak signal of short duration at an un-

known time point. Both, BP and LRP are about 10 to

100 times smaller in amplitude than the superimposed

brain activity and can normally only be detected in av-

erage analysis (enhancing time-triggered ERP activity

and reducing so-called activity-unrelated noise). For

our approach, single trial EEG analysis in real time is

needed that has to be fast to allow multiple testing for

the occurrence of ERPs before the movement.

4.1 Framework

In this section, we present the architecture of our

framework for the aBRI that is used to make a predic-

tion whether an operator prepares to perform a move-

ment or whether he does not. This prediction is inte-

grated into the bio-inspired control architectures that

controls the stiffness of the exoskeleton.

The framework is designed to classify single trial

EEG epochs, which is a major requirement for vari-

ous different application scenarios for adaptive Brain

Reading (aBR) (Kirchner et al., 2009). Since a

machine-learning based approach is used for single

trial classiﬁcation, the framework has to support vari-

ous data preprocessing methods and training sessions

of the underlying classiﬁer.

Major requirements for the aBRI are, among oth-

A MULTIMODAL BRAIN-ARM INTERFACE FOR OPERATION OF COMPLEX ROBOTIC SYSTEMS AND UPPER

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155

ers, a high classiﬁcation accuracy, modular design to

facilitate the development process by rapid prototyp-

ing, different instances of possible data processing de-

signs and evaluation of the used algorithms, as well as

high performance for online data processing.

To fulﬁll these requirements, the data processing

system is based on the Modular toolkit for Data Pro-

cessing (Zito et al., 2008), which in turn is based on

NumPy and SciPy (Jones et al., 2001) to perform the

computations. These frameworks consist of C++ im-

plementations of the mentioned algorithms with bind-

ings to Python. Since the actual computationally in-

tensive algorithms are realized as binary object code,

the performance impairment of a scripting language

does not account here signiﬁcantly.

The framework is realized as a set of independent

modules which are called nodes. Several nodes can

be assembled to create ﬂows, where the data is trans-

ferred from one node to the next one. Different es-

pecially time-critical nodes are realized in C++ and a

channel-wise parallelization by using OpenMP.

The overall processing system is structured as fol-

lows (Figure 5 for an overview):

Figure 5: Data processing scheme.

EEG Acquisition. This module acquires the data

from the EEG hardware and transfers it to the succes-

sive data processing modules. In this step, the data is a

continous stream of the raw signal data. An important

point is the capability to automatically add markers to

the datastream to label special events for training ses-

sions, e.g. when the operator starts to move or enters

a speciﬁc position. A streaming of ﬁles which contain

previously acquired EEG data is also possible.

Windowing. In this step, the raw EEG data stream

is segmented into windows of the same shape (length

of the signal frame) to extract instances which can be

processed and classiﬁed independently of each other.

This simpliﬁes the successive computations since it

allows to work always on instances of the same shape.

The process of extracting the time windows is

called windowing. The framework is capable of us-

ing different methods for window generation, e.g. to

generate training samples for classiﬁer training and

testing by using speciﬁed markers, or for online data

processing in the real application scenario.

In the case of BP and LRP detection later on EEG

data is segmented into windows of length of 1000 ms.

Instances containing movement preparation were cut

out with respect to a movement marker and instances

containing no movement preparation were extracted

during a rest period (Section 4.2).

Preprocessing and Feature Generation. Prepro-

cessing refers to operations aimed at increasing the

signal-to-noise ratio. It can be performed in several

steps. In Figure 5, an example ﬂow with several dif-

ferent data processing methods is shown.

Speciﬁcally in the ﬂow which is used later on for

the movement prediction, each window is standard-

ized channel-wise (subtraction of the corresponding

mean and division by the standard deviation). A dec-

imation is applied afterwards with an anti-aliasing

ﬁlter to reduce the sampling rate of the data from

5000 Hz to 20 Hz.

After that, the data is scaled with the function

speciﬁed by Equation 1 from Section 1. The next

processing step is another band pass ﬁlter with a nar-

row frequency band, which is applied to remove un-

wanted frequencies while retaining the sample rate. In

the end, the last 4 values per channel of each window

are used as the extracted features for the classiﬁer. In

the current scenario, all features are normalized be-

fore they are used for classiﬁcation.

Classiﬁcation. Any kind of classiﬁcation algorithm

suited for binary decision tasks can be used here. We

applied support vector machines with a linear kernel.

4.2 Experimental Setup

In this section the experimental setup which is used

for the acquisition of both training and test data is pre-

sented.

BIODEVICES 2011 - International Conference on Biomedical Electronics and Devices

156

C4 / C3

-3

-1000

-400 0-600 -200 200 400

movement marker

-6

-800 600 800

ipsilateral (C4; n = 253)

contrallateral (C3; n = 253)

Ball

presented

Rest

Position

left

Recording

Runs

Presentation

of Target Ball

Run 1

Start End

Rest

Run 3

Movement

Ball

entered

Start End Start End

ITI

Ball

presented

Rest

Window 2

Movement

Rest

Position

left

Ball

entered

Window 1

Trial 1 Trial 2

Run 2

Break

...

Figure 6: Experimental setup: a) Subject performs goal-oriented movements triggered by target balls; c) experimental design;

b) averaged EEG activity before and after the movement measured above the primary motor cortex areas, ipsilateral (electrode

position C4) and contralateral (electrode position C3) to the side of movement.

Subjects. Two subjects (two males, 30 and 27 years

old) participated in this experiment. The participants

were right-handed and had normal or corrected-to-

normal vision. For each participant, three runs con-

sisting of 60 trials were conducted on the same day,

two of which were used for parameter optimization

and one for validation.

Design and Procedure. Participants wore a head-

mounted display (HMD) and stood in a dimly lit room

while performing a task in a virtual environment. The

task was to move their right arm from a rest position

in order to reach virtual target balls, which were pre-

sented in the HMD to allow free movements indepen-

dent of head position (Figure 6 a).

Leaving the rest position was detected by a hand-

tracking system. Whenever the subjects moved their

arm 5 cm away from the rest position, a marker for

movement was sent and stored together with the EEG

(Figure 6 b). Based on the movement marker, the

EEG stream was segmented into windows of length

of 1000 ms (Section 4.1).

After entering the target ball, the subjects returned

to the rest position. At the same time the next target

ball appeared. Time needed to return to the rest po-

sition and for the next target ball to appear depended

on the distance between the entered target ball and

the rest position. Therefore, this time, i.e. inter-trials-

interval (ITI, Figure 6 c), varied according to the dif-

ferent distances between target balls and rest position.

To support the rest state of the arm, an armrest

that was designed as part of our test bed was used.

This arm rest was integrated into the setup to imitate

a strong support of the arm by the exoskeleton. This

is for example useful whenever a subject is holding

something still in his hand or is manipulating a de-

vice with outstretched arms. In both cases, a strong

force would rest on the arm which the exoskeleton

would support. Participants had to stay in the rest po-

sition for at least 5 seconds. In case that a subject

would leave the rest position earlier, the next target

ball would disappear. This was done to avoid too fast

changes between movement and rest which was very

important to assure long enough non-movement peri-

ods for training.

Data Acquisition. The EEG was continuously

recorded from 128 electrodes (extended 10-20 sys-

tem, actiCap, Brain Products GmbH, Munich, Ger-

many), referenced to FCz. EEG signals were ampli-

ﬁed using four 32-channel BrainAmp DC ampliﬁers

(Brain Products GmbH, Munich, Germany) and ﬁl-

tered with a low cutoff of 0.1 Hz and high cutoff of

1000 Hz. Signals were digitized with a sampling rate

of 5000 Hz. Impedance was kept below 5 kΩ.

5 RESULTS

In this section ﬁrst results about performances in an-

ticipating movements via the aBRI interface, and the

suitability of the proposed control approach to modu-

late the exoskeleton joints impedance, are presented.

It is worth to mention that tests of the two interfaces,

A MULTIMODAL BRAIN-ARM INTERFACE FOR OPERATION OF COMPLEX ROBOTIC SYSTEMS AND UPPER

LIMB MOTOR RECOVERY

157

in this case, were conducted separately. Nevertheless

this is sufﬁcient to demonstrate the feasibility of our

approach and gives solid basis to deﬁne future com-

bined experiments.

5.1 Active Exoskeleton - Results

This section provides results for position and admit-

tance control of hydraulic actuators using the schemes

described in Section 3.2. Therefore, a single joint

testbed was equipped with a hydraulic actuator driv-

ing a lever with a force sensor implemented at the

tip. Actuator positions are acquired with an absolute

optical encoder series HEDS 9040 from Agilent pro-

viding a resolution of 2880 ticks per revolution, i.e.

∆q =

0.125

◦

tick

Position Control Results. For evaluating the posi-

tion control performance of the testbed, a sequence

of different desired positions is given within the

workspace of −84

◦

≤ q

≤ 53

◦

. For a better tran-

sient behavior, changes in reference are propagated to

the control system by a PT

trajectory generator. See

Figure 7 for results.

Figure 7: Single joint position results; solid: reference tra-

jectory; dashed: actual joint position; grey: controller error.

The control loop shows a good performance in the

overall working range. Desired positions are tracked

with a steady state error of e

= 0.25

◦

which is equal

to two encoder ticks, while transients are followed by

only a small phase shift. The precision of the control

loop is limited to a minimum of one encoder tick.

The maximum angular speed of the hydraulic actuator

can be extracted to

◦

The position controller offers a good positioning

accuracy as well as a proper transient behavior and

thus is a suitable basis for the tracking of a given tra-

jectory.

Admittance Control Results. Here, the reaction of

the admittance control system to a given input from

an operator is demonstrated. Furthermore, the inﬂu-

ence of the stiffness and damping parameters K and D

from Equation 2 on the behavior of the control loop

is illustrated. For the ﬁrst set of experiments only an

adaption of K is performed, while damping is kept at

a constant value. Afterwards, the damping is varied

for a ﬁxed stiffness. During all test runs, a human

is trying to move the single joint along an identical

trajectory from a start position of q

= −10

◦

(hori-

zontal orientation of the lever) to an end position of

= −30

◦

by exerting a force to the sensor located at

the tip of the device.

Figure 8 depicts the results of piloting the joint

in admittance control mode at different stiffness val-

ues and a constant damping of D = 0.35. Figure 8(a)

shows the movement of the actuated joint, while Fig-

ure 8(b) presents the simultaneously measured con-

tact forces. Note that the position trajectories are not

aligned because they are performed manually by a hu-

man, trying to repeat the same movement in each run.

(a)

(b)

Figure 8: a) trajectories of hydraulic joint for different stiff-

ness coefﬁcients of admittance ﬁlter and constant damping

of D=0.35; b) measured contact force between human and

hydraulic actuator during the movements

It is obvious that an increase of the stiffness coef-

ﬁcient in the range of K ∈ [0.5, 2] results in a higher

contact force between operator and device during the

whole movement, i.e. more force has to be generated

by the user to transfer the system to the wanted posi-

tion. Moreover, it can be determined that the steady

state value for the contact force is directly propor-

tional to K. Utilizing these results in the overall sce-

BIODEVICES 2011 - International Conference on Biomedical Electronics and Devices

158

nario, the idea of lowering the stiffness according to

a measured LRP will result in a smaller impedance of

the device against performed motions, reduce the im-

pact force at the beginning of the movement, and thus

makes the device more transparent to the user.

In the next step, the inﬂuence of the damping co-

efﬁcient D to the control loop is investigated, keeping

the stiffness at a constant value of K = 1. Results of

this experiment are depicted in Figure 9, while Figure

9(a) again shows the movement of the device, Figure

9(b) is containing the related force curves.

(a)

(b)

Figure 9: a) trajectories of hydraulic joint for different

damping coefﬁcients of admittance ﬁlter and constant stiff-

ness of K=1; b) measured contact force between human and

hydraulic actuator during the movements

By changing the damping ratio only the transient

behavior is inﬂuenced, while the steady state values

end up in the same interaction force governed by the

stiffness coefﬁcient. Mechanically damping can be

described as a sink which dissipates more energy

the higher it is. Experimental results verify this

point of view by showing that the user has to apply

more force for higher than for lower ratios, to get the

device from the start to the end point in the same time.

5.2 aBRI - Movement Prediction

Results

Three different analyses are presented below to out-

line our approach and to show current work. The

aim of aBRI is to distinguish between the two classes:

“no movement” and “movement”. In contrast to var-

ious BCI-groups dealing with ERP research (Section

1), which often use accuracy as a performance mea-

sure of their systems, here Area Under Roc is ap-

plied. There are two reasons: ﬁrst, the datasets are

unbalanced (about four times more “no movement”

instances) and second, even this class ratio might not

be constant, e.g., in the online case. So a performance

measure beeing robust against a varying class ratio

has been chosen (Fawcett, 2006).

Data Processing Optimization Results. For the

capability of the system to detect and respond to

a movement preparation before its actual execution,

both the classiﬁcation performance and the running

time of the data processing are important factors.

To get an impression which algorithms are appli-

cable for online detection, the running time and the

classiﬁcation performance of several algorithms were

measured and compared. A major bottleneck of the

data processing are all operations that are applied be-

fore the decimation (which is the reduction of the

sample rate with preceding ﬁltering) since these have

to deal with a high amount of data. In the used ﬂow

(Figure 5), these are the standardization and the anti-

alias ﬁltering step of the decimation itself.

To evaluate which algorithm is suitable for the

ﬁltering step decimation procedure, different anti-

aliasing ﬁlters (AAF) were compared. All ﬁltering

steps of the AAF were applied in two consecutive

steps (with decimation factors of 25 and 10, respec-

tively). The inspected types were: the use of no ﬁlter

at all (called downsampling in the following), a ﬁnite

impulse response ﬁlter (FIR, applied as a direct form

time domain convolution with a ﬁlter kernel having a

length of 31 taps designed using the window design

method and using a hamming window, omitting the

time series values that are irrelevant due to the subse-

quent downsampling), and an elliptic inﬁnite impulse

response ﬁlter (IIR, with 60dB stopband attenuation

with an order of 10, realized in direct form). Of the

successive operations, the application of the band pass

ﬁlter (BPF) was another time-consuming step, so that

different types of BPF were examined (again with the

described FIR and IIR ﬁlters, and simply using an

FFT to set all unwanted frequencies to 0). For the

evaluation of the classiﬁcation performance, several

high and low cutoffs of the BPF and several complex-

ities (SVM parameter) were tested additionally.

Repeated measures ANOVA with four factors was

performed: AAF (IIR, FIR, Downsampling), BPF

(FFT, FIR, IIR), Low/High cutoff (0.4/4.0, 0.1/4.0,

0.4/2.0, 0.1/2.0) and SVM complexity (5.0, 1.0, 0.1,

0.01). Subsequently, repeated measures ANOVA with

two factors was performed: AAF (IIR, FIR, Down-

A MULTIMODAL BRAIN-ARM INTERFACE FOR OPERATION OF COMPLEX ROBOTIC SYSTEMS AND UPPER

LIMB MOTOR RECOVERY

159

sampling) and BPF (FFT, FIR, IIR, No). If needed,

Greenhouse-Geisser correction was applied and the

corrected p-value is reported. For pairwise compar-

isons, Bonferroni correction was applied.

A main effect of AAF [F(2, 94) = 8.07, p <

0.005; pairwise comparisons: IIR > Downsampling,

IIR > FIR, Downsampling vs. FIR: n.s.] and a main

effect of BPF [F(2, 94) = 14.99, p < 0.001; pairwise

comparisons: FFT > FIR, IIR > FIR, FFT vs. IIR:

n.s.] were found. There was an interaction between

two factors [BPF x AAF: F(4, 188) = 79.05, p <

0.001]. Pairwise comparisons showed that the best

combination was ”IIR AAF-FFT BPF-0.1/4.0 Hz-0.1

complexity”. Subsequent analysis of control condi-

tion (No BPF with three types of AAF) revealed that

the best combination was signiﬁcantly better than the

control condition [p < 0.002].

Beside the classiﬁcation performance, the com-

puting time was evaluated. Even if the corresponding

computations are performed on a high performance

computing system in parallel, the used algorithms

play an important role.

The running times are the averages of the wall-

clock processing times of windows of all datasets that

were used in the optimization procedure. We used an

Apple Mac Pro with two Intel Quad-Core Xeon pro-

cessors (resulting in 16 virtual cores due to Hyper-

Threading) at 2.66 G Hz and 32 GB memory for the

time measurements. A channel-wise parallelization

of the used ﬁltering procedures and the standardiza-

tion was performed using OpenMP. The average and

maximum running times are shown in Table 1.

Table 1: Average (and maximum computation times in

brackets) of different ﬁltering algorithms in ms.

Anti alias

ﬁlter type

Band pass ﬁlter type

FIR IIR FFT No

FIR 26.4

(64.3)

25.4

(30.6)

28.0

(34.5)

20.3

(24.5)

IIR 25.4

(28.4)

24.9

(28.0)

27.6

(31.6)

20.2

(27.1)

No 20.7

(25.0)

19.8

(26.6)

22.4

(29.9)

14.7

(21.5)

Time-Dependent Classiﬁcation Performance.

Apparently, the longer the distance in time between

classiﬁcation and actual movement onset, the more

difﬁcult it is for the algorithm to detect the BP/LRP.

In addition neurobiological analysis about the be-

ginning of movement preparation from averaging

lots of trials for the channels C3 and C4 (Figure

6 c) does not allow to draw conclusions for the

multi-channel single trial case. To obtain knowledge

about how performance increases with sliding in

time closer to the movement, a 5×2 cross-validation

with the optimized parameters on one dataset of each

subject was performed. These datasets were not

used for the optimization procedure to ensure reliable

performance measurements.

Figure 10: Boxplots of the time-dependent classiﬁcation

performance for two subjects: NP (red) & CJ (blue).

Figure 10 shows the evolvement of performance

over time, whereas the time points denote the end of

each extracted time window. I.e., one box plot de-

picts the result for a classiﬁer trained on windows

up to a speciﬁc time point and tested on other trials

windowed with the same procedure. Note that the

movement marker (time point zero) does not indicate

movement onset, since it is stored when the subject

had already moved 5 cm. That means, the interest-

ing range for the brain-arm interface would be quite

before that point (at least around -150 to -250 ms).

Although, in the online case, there is no time-

dependent classiﬁcation possible and also the subject-

speciﬁc differences need further investigations, these

results demonstrate that the approach in principle al-

lows to detect movement preparation.

Online Simulation Results. Since, in the real ap-

plicable system, there will be a continuous movement

prediction, another test was conducted on the same

datasets as for the time-dependent test, but this time

in an pseudo online manner. For the test, a classiﬁer

with optimized parameters was trained on two differ-

ent windows of each trial, i.e., cut out up to -250 and

-150 ms. This was done to make the classiﬁer some-

how robust (Blankertz et al., 2006) against the “mov-

ing” LRP in the windows. The classiﬁer is then tested

on sliding windows extracted every 50 ms.

For the “Force Control” module of the brain-arm

interface, a continuous value S

LRP

from 0 to 1 is ex-

pected. To achieve this, the SVM score can be used

and scaled in the demanded range. In Figure 11, the

classiﬁer output against the time point of classiﬁca-

tion is shown. It is below zero if an instance is classi-

ﬁed to be “no movement” and above zero otherwise.

BIODEVICES 2011 - International Conference on Biomedical Electronics and Devices

160

Figure 11: Mean (bars) with standard deviation (errorbars)

of the SVM prediction values for sliding windows of the

test datasets of two subjects: NP (red) & CJ (blue).

Scaling can be done for example by

LRP











svm

min

if svm

< svm

min

svm

2·svm

max

+ 0.5 if svm

∈ [svm

min

, svm

max

]

svm

max

if svm

> svm

max

where svm

is the SVM score for the actual classi-

ﬁed time window in the online test case and svm

min

& svm

max

are the minimum and maximum values for

the SVM score obtained in the training phase of the

classiﬁer.

6 CONCLUSIONS AND FUTURE

WORK

This paper proposes the architecture of a multimodal

haptic interface that has two main ﬁelds of appli-

cation: as a control device to operate complex and

heterogeneous robotic systems in an intuitive man-

ner, and as rehabilitation tool in order to facilitate

and speedup the recovery procedure of upper limbs.

In particular a control scheme that combines bio-

inspired with classical control techniques, and in-

tegrates information about movements preparation,

is presented. In comparison to classical BCI ap-

proaches, the proposed aBRI interface does not use

brain signal, to directly control the movements of the

haptic device, on the contrary the information is used

to modulate its impedance and therefore to facilitates

the prepared user movements. This improves the nat-

ural feeling in wearing the interface, enhancing the

operation of the robotics system, and increasing the

quality of the therapy.

Results show that modulating the stiffness and

damping of the force controller can be used on one

hand to ensure a safe interaction in case of low LRP

detection scores, by avoiding a too sensitive reaction

to motion inputs from the operator. On the other hand,

this feature can be used to facilitate the ability of the

system to work as a rehabilitation device, resisting or

supporting the movements of a user.

For future work we plan to improve performance

of the movement prediction by e.g., investigating sev-

eral other classiﬁcation and data processing algo-

rithms, perform a thorough evaluation of ﬁlter al-

gorithms and further smoothing methods, as well as

combining the detection of ERPs and changes in the

frequency range. Further on, we will improve adapt-

ability to different users and changes in brain activity

during the usage by the development of self-adapting

methods for aBRI.

Finally experiments that combine both interfaces

(Exoskeleton, aBRI) have to be conducted, this in or-

der to demonstrate the feasibility of the proposed con-

trol approach in a real time scenario. Questions rel-

atively the user comfort and the beneﬁts of the on-

line joint stiffness modulation have to be addressed

via testing the system on different users.

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