Commonalities of Motor Performance Metrics are Revealed by

Predictive Oscillatory EEG Components

M. Tangermann

, J. Reis

and A. Meinel

Brain State Decoding Lab, Cluster of Excellence BrainLinks-BrainTools,

Dept. of Computer Science, Albert-Ludwigs-University, Freiburg, Germany

Dept. of Neurology, University Medical Center Freiburg, Germany

Keywords:

EEG, Oscillatory Components, Single-Trial Analysis, Performance Prediction, Machine Learning, Spatial

Filters, Subspace Decomposition, Hand Motor Task, Isometric Force, Performance Metrics, Reaction Time,

Jerk, Motor Rehabilitation.

Abstract:

The power of oscillatory components of the electroencephalogram (EEG) can be predictive for the single-trial

performance score of an upcoming task. State-of-the-art machine learning methods allow to extract such pre-

dictive subspace components even from noisy multichannel EEG recordings. In the context of an isometric

hand motor rehabilitation task, we analyse EEG data of n=20 normally aged subjects. Predictive oscillatory

EEG subspaces were derived with a spatial ﬁltering method (source power comodulation, SPoC), and the

transfer of these subspaces between ﬁve performance metrics but within data of single subjects was investi-

gated. Findings suggest, that on the grand average of 20 subjects, informative SPoC subspace components

were extracted, which could be shared between a set of three metrics describing the duration of subtasks and

jerk characteristics of the force trajectories. Transfer to any other of the remaining four metrics was not possi-

ble above chance level for a metric describing the reaction time and a metric assessing the length of the force

trajectory. Furthermore we show, that these transfer results are in line with the structure of cross-correlations

between the performance metrics.

1 INTRODUCTION

Motor tasks are performed in rehabilitation scenarios

or in basic research of the human motor system. The

execution quality of repeated trials of the same task

varies and can be assessed on a trial-by-trial basis by

a large number of performance metrics. Examples

are the reaction time, the smoothness of the trajectory

produced, its length, or the duration of trial/repetition.

Each of these metrics focus on different aspects of the

motor task.

Trial-to-trial variations of the motor performance

can have many different causes ranging from an un-

stable experimental setup over varying starting posi-

tions of the user or ﬂuctuating muscle tone to alter-

ations of the mental state of the user. The latter can

be expressed e.g. by varying attention levels during

the presentation of cues or the predisposition of brain

regions involved in the motor execution, and is of in-

terest especially in the context of motor rehabilitation

after stroke. Analysing electroencephalogram (EEG)

data recorded immediately before the execution of

each single motor execution trial with machine learn-

ing methods (M

uller et al., 2008; Parra et al., 2005;

Delorme et al., 2011) can reveal oscillatory activity

of the EEG, which is informative about the perfor-

mance metric of the upcoming trial and may be the

basis for brain-state dependent training paradigms.

For the SVIPT hand motor task used in stroke re-

habilitation (Meinel et al., 2015; Casta

no-Candamil

et al., 2015b) it was proposed to use the supervised

spatial ﬁltering method source power comodulation

(SPoC, (D

ahne et al., 2014)). SPoC ﬁnds spatial ﬁl-

ters, which extract oscillatory subspace components

of the EEG within a narrow frequency band. The al-

gorithm optimizes these ﬁlters in such a way, that the

resulting subspace components comodulate in power

to a variable which is accessible for every trial. In

the case of SVIPT training, the values of a trial-wise

performance metric can be used as labels to guide the

SPoC algorithm. For the SVIPT task ﬁve different

trial-wise performance metrics have been extracted to

describe the quality of the force control.

Concerning the search for comodulating oscilla-

Tangermann, M., Reis, J. and Meinel, A..

Commonalities of Motor Performance Metrics are Revealed by Predictive Oscillatory EEG Components.

In Proceedings of the 3rd International Congress on Neurotechnology, Electronics and Informatics (NEUROTECHNIX 2015), pages 32-38

ISBN: 978-989-758-161-8

tory components of the EEG, it must be expected that

the exact type of performance metric used (the la-

bels for SPoC) will inﬂuence both, the optimal fre-

quency band for the extraction of SPoC components,

and the actual spatial ﬁlters/components revealed by

SPoC. While the cross-correlations between the ﬁve

metrics can easily be computed, it is an open ques-

tion, if informative oscillatory subspace components

can be transferred successfully between metrics.

2 METHODS

2.1 Hand Motor Task

Figure 1: EEG-tracked SVIPT scheme. By force modula-

tion, the horizontal cursor position can be controlled.

The sequential visual isometric pinch task

(SVIPT) (Reis et al., 2009) requires isometric

force control of the thumb and the index ﬁnger. Since

SVIPT training improves motor performance upon a

range of hand-motor tasks, the paradigm is applied

in motor rehabilitation after stroke. During a single

trial, the subject is required to control a horizontally

moving cursor by generating an isometric force

between thumb and index ﬁnger. All ﬁelds (T0, T1,

and T3) are visible during the full duration of a trial.

Field T0 corresponds to zero force and is used as the

starting point of the cursor. Reaching the ﬁeld T2

requires the highest force application (see Fig. 2).

Each trial comprised the following stages: the vi-

sual presentation of a light blue (still inactive) cursor

indicated the get-ready phase. This interval randomly

varied between 2 s and 3 s of duration and was termi-

nated by the go-cue. At this time point, the cursor

turned dark blue and its horizontal position could be

controlled by the subject. During this running stage

of the trial, the subject was asked to manoeuvre the

cursor through a sequence of the three narrow tar-

get ﬁelds as quickly and as accurately as possible.

The two sequence types requested to produce were

either T0-T1-T0-T2-T0 (see the example force pro-

ﬁle in Fig. 2) or T0-T2-T0-T1-T0. During a sequence,

the next target ﬁeld to be hit was visually highlighted

in slight green color. In order to hit a target ﬁeld, a

dwell time of 200 ms had to be fulﬁlled. Skipping a

target ﬁeld and moving on the the next element of the

sequence was not accepted.

Trials with high level of motor control are charac-

terized by a rapid initial force ramp-up and the avoid-

ance of overshoots beyond target ﬁelds. As intro-

duced in (Meinel et al., 2015), we modiﬁed the orig-

inal SVIPT setup by the additional recording of EEG

activity throughout all stages of the experiment. To

design improved rehabilitation training scenarios, it

would be of interest to identify EEG correlates within

an interval prior to the go-cue, which are predictive

wrt. the trial-wise performance metric.

Force proﬁle F(t)

hit

T0,exit

time t

T0 T1 T2

Figure 2: Force proﬁle F(t) for single SVIPT trial. The trial

start at time point t

is followed by leaving the target ﬁeld

T0 with the blue cursor at time point t

TO,exit

. The successful

hit of the ﬁrst target ﬁeld is marked at time point t

hit

2.2 Subjects, Data Acquisition and

Preprocessing

In total, 20 single sessions from the same number of

right-handed subjects with an average age of 53 years

(std: 6 years) were recorded. Each subject controlled

the cursor with the left hand during 400 trials. Dur-

ing the SVIPT task, EEG signals from 64 passive

Ag/AgCl EasyCap electrodes placed according to the

extended 10–20 system and referenced against the

nose were registered by BrainAmp DC ampliﬁers.

The data was sampled at 1 kHz. During ofﬂine pre-

processing, signals were band-ﬁltered between 0.7 Hz

and 25 Hz and subsampled to 500 Hz. An amplitude

and variance rejection criterion was applied per epoch

and per channel in order to diminish the impact of

noise, eye movements or muscular artifacts. Rejected

epochs were not compensated for, and rejected chan-

nels were not replaced.

Commonalities of Motor Performance Metrics are Revealed by Predictive Oscillatory EEG Components

2.3 SVIPT Performance Metrics

Since there is no unique measure to validate the qual-

ity of a single repetition of the SVIPT task, we deﬁne

a subset of performance metrics and contrast them

among each other. In the following, ﬁve trial-wise

performance scores

are motivated. Each of them

is derived from the force proﬁle F

(

t) as described in

Fig. 2:

• Reaction Time / RT: A quick reaction upon the

go-cue is the basis for a successful trial. Reaction

time is deﬁned as the interval between the go-cue

at time t

and the time point t

T 0,exit

which indi-

cates the cursor leaving the starting ﬁeld T0.

• Duration / DUR: In a similar manner as RT, the

duration from the go-cue at time t

until the hit

of the ﬁrst target ﬁeld at time point t

hit

needs to be

short for a successful trial:

DUR ≡ t

hit

−t

(1)

• Cursor Path Length / CPL: The total path length

the cursor is moved from the go-cue to the hit of

the ﬁrst target ﬁeld can be described by:

CPL ≡

hit

F(t)|dt

(2)

• Integrated Squared Jerk / ISJ: Changes in

smoothness of the force proﬁle characterize the

level of ﬁne-granular motor control. Therefore,

the jerk - deﬁned as the third derivative of the

force proﬁle - is contained in the ISJ, which is de-

ﬁned as:

ISJ ≡

hit

F(t)

(3)

• Normalized Jerk / NJ: Related to ISJ, the NJ is

a unit-free and proportional measure of the jerk:

NJ ≡

ISJ · DUR

2 ·CPL

(4)

However, the introduced scores CPL, DUR, ISJ and

NJ strongly depend on the selection of an upper time

limit. A good choice of this parameter requires a

trade-off between staying close to the go-cue and the

analysis window (which ends just before the go-cue)

on the one hand, and processing the richer informa-

tion of a full target sequence on the other hand. As a

compromise between both requirements, we decided

to extract these four metrics up to the hit of the ﬁrst

target ﬁeld.

Except for metric RT, a standardization of the distribu-

tion of performance scores had to be performed before the

measurements of the two sequence types (either T0-T1-. . .

or T0-T2-. . . ) could be joined.

2.4 Source Power Comodulation

The trial-wise SVIPT performance metric can be used

as label information to guide a data-driven machine

learning approach to determine EEG subspaces which

comodulate in band power with a given continuous

performance metric. Source power comodulation

(SPoC) (D

ahne et al., 2014) is a linear spatial ﬁlter-

ing method, which maximizes the correlation of an

epoch-wise deﬁned bandpower Φ

(e) = Var[ ˆs](e) of

the subspace signal ˆs = w

x(t) with a given epoch-

wise metric z(e). The spatial ﬁlter w is calculated

by solving argmax

{corr[Φ

,z]

}. The multivariate

variable x(t) ∈ R

describes the EEG signal with N

sensors.

As the alpha band activity of the EEG has been

correlated with visual attention processes (Thut et al.,

2006; Romei et al., 2008) as well as with the

state of the motor system (Pfurtscheller et al., 1996;

Pfurtscheller and Da Silva, 1999), we chose to focus

the analysis to the frequency band of 8 Hz to 13 Hz.

Extracting EEG from a prediction window located

within the get-ready phase and prior to the go-cue

and utilizing performance metrics as described in 2.3,

one can use the resulting spatial ﬁlters as predictors

for estimating the motor performance of the upcom-

ing trial. As the EEG signals need to be band-ﬁltered

before entering the approach, the frequency band of

interest and the exact time interval of the prediction

window are hyperparameters which inﬂuence the re-

sulting SPoC performance. To optimize these, we

performed a grid search upon different SPoC parame-

ter conﬁgurations. The frequency bands [ f

, f

+ ∆ f ]

were linearly increased within the alpha band range

from f

= 8 − 13 Hz with a step size of 0.5 Hz. The

band width was kept ﬁxed at ∆ f = 1Hz. Three differ-

ent time intervals [−∆t,−50ms] relative to the go-cue

with ∆t = {600, 800,1000}ms were investigated for

the prediction window.

The parameter sweep was calculated for each of

the subjects and for all ﬁve performance metrics.

For further analysis, the best conﬁguration per sub-

ject and performance metric was chosen according

to their correlation values and pattern similarities.

A detailed methodology to extract informative com-

ponents can be found in (Casta

no-Candamil et al.,

2015b), and (Haufe et al., 2014) motivates the in-

terpretation of subspace components via the resulting

patterns.

NEUROTECHNIX 2015 - International Congress on Neurotechnology, Electronics and Informatics

2.5 SPoC Component Transfer Across

Performance Metrics

The eigenvalues of SPoC ﬁlters correspond to corre-

lations of these components with the underlying per-

formance metric. In Fig. 3 the averaged spectrum

across all 20 subjects and all ﬁve performance metrics

is shown. As the correlation values drop rapidly from

component to component, the ﬁrst e.g. ﬁve SPoC

components typically are sufﬁcient to provide an in-

formative subspace.

0 10 20 30 40 50 60

−0.4

−0.3

−0.2

−0.1

0.1

0.2

0.3

0.4

0.5

component no.

eigenvalue

Figure 3: Eigenvalue spectrum of SPoC components aver-

aged over 20 subjects and ﬁve performance metrics.

In order to investigate common characteristics of

motor performance metrics within subjects, we now

transfer SPoC ﬁlters and calculate their correlation

with another performance metric, which had not been

utilized for calculating the spatial ﬁlter. For a given

metric i, the epoch-wise band power feature Φ

x,i

us-

ing only the ﬁrst component is extracted. In a second

step, we report on the correlations of this feature with

all other performance metrics j in a transfer matrix

(i, j) = corr[Φ

x,i

] for each subject k. Since the

EEG preprocessing delivers different numbers of re-

maining trials across subjects, we randomly select a

subset of 200 epochs, which ensures the comparabil-

ity of correlation values across subjects. As each ma-

trix T

is computed on the basis of a single subject, the

grand-average transfer matrix T

= 1/N

∑

(i, j)

across N

= 20 subjects must be derived by averag-

ing. To evaluate the signiﬁcance level of correlation

values, we computed SPoC repeatedly based on 1000

randomly shufﬂed label values. As reported in Fig. 4,

the exemplary shufﬂing of the ISJ labels evokes the

95 % threshold at a correlation value of 0.22. Thus,

the entries of the transfer matrix T (i, j) need to exceed

this threshold in order to report a successful compo-

nent transfer.

−0.4 −0.2 0 0.2 0.4

0.02

0.04

0.06

0.08

correlation value

probability distribution (a.u.)

Figure 4: Bootstrapping result for metric ISJ. The 95%

threshold at a correlation value of 0.22 is indicated by a

blue arrow.

3 RESULTS

3.1 Correlations Across Performance

Metrics

The performance metrics described in Sec. 2.3 were

computed across all 20 subjects on each of the 400

trials. Their scatter plots in Fig. 5 depict the grand

average correlations between ﬁve motor performance

metrics. For visualization purposes, all values were z-

scored, and values exceeding four standard deviations

were omitted from the scatter plots. The strongest

correlation is obtained between the metrics ISJ and

DUR, non-linear interactions are observed between

DUR and NJ. Metric RT is rather uncorrelated to

the four other metrics, which is (to a lesser extend)

also observed for the metric CPL. The histograms of

the individual metrics reveal that there are symmetric

distributions contained (DUR) as well as asymmetric

ones (NJ, CPL).

Figure 5: Scatter plots visualize the grand average corre-

lations between ﬁve motor performance metrics for 8000

SVIPT trials, derived from 20 subjects and 400 trials per

subject.

Commonalities of Motor Performance Metrics are Revealed by Predictive Oscillatory EEG Components

Table 1: For each performance metric, the table lists the op-

timal SPoC parameters derived: the frequency band speci-

ﬁed in Hz, the time interval relative to the go-cue, the num-

ber of ﬁrst-ranked components contributing to a maximal

correlation value and the correlation value itself.

Metric FQ band Ival [ms] # cp corr

RT 9.0-10.0 -1000,-50 2 0.508

ISJ 11.5-12.5 -1000,-50 1 0.242

CPL 11.5-12.5 -1000,-50 1 0.344

DUR 9.5-10.5 -800,-50 1 0.178

NJ 8.0-9.0 -1000,-50 1 0.214

3.2 SPoC Component Transfer Across

Performance Metrics

RT ISJ CPL DUR NJ

ISJ

CPL

DUR

0.1

0.2

0.3

0.4

0.5

Pattern

Filter

Correlation

Figure 6: (Best single subject) Left: First ﬁve SPoC activity

patterns for each performance metric according to the best

parameter set are plotted. The corresponding ﬁrst SPoC

ﬁlter is shown in addition. Right: Transfer matrix T (i, j).

(Please see Sec. 3.2 for details).

The SPoC parameters (frequency band and inter-

val interval) have been optimized separately for each

metric, and Table 1 shows the values for the exam-

ple of the best single subject. Please observe, that the

derived best frequency bands vary between the perfor-

mance metrics even within data of this single subject.

The transfer approach is illustrated on data of the

same subject in Fig. 6. The 25 scalp maps on the

left depict the patterns of the ﬁrst ﬁve SPoC compo-

nents of this subject (organized in columns), as de-

rived for the ﬁve performance metrics (rows). For the

ﬁrst-ranked component, the corresponding spatial ﬁl-

ter weights are depicted in an additional scalp map.

Please remind, that only the ﬁrst-ranked component

has been used to evaluate the correlation for the com-

ponent transfer across metrics. However, as the com-

ponents of the ”receiving” metric may show similar

patterns even in lower ranks, the full ﬁve ﬁrst-ranked

scalp patterns are provided.

The matrix on the right half of Fig. 6 visualizes the

transfer matrix T (i, j) as described in Section 2.5. It

was derived for the same subject. Entry T (i, j) color-

codes the correlation gained by transferring the ﬁrst-

ranked component of the metric in row i to the met-

ric of column j. Correlation values, which have not

passed the bootstrapping test, have been marked by

white entries. The entries T (4,5) and T (5,4) show

the highest correlation values. It can be observed, that

the involved metrics DUR and NJ share very similar

patterns among their ﬁrst-ranked components.

RT ISJ CPL DUR NJ

ISJ

CPL

DUR

0.1

0.2

0.3

0.4

0.5

Correlation

Transfer from:

Transfer to:

Figure 7: Grand average transfer matrix T

across all 20

subjects. Each entry (i,j) color-codes the correlation gained

by applying the ﬁlter of the ﬁrst ranked SPoC component

of the metric in row i to the EEG data and correlating the

power of the resulting oscillatory signal to the metric of col-

umn j.

The corresponding grand average results over all

20 subjects are depicted in Fig. 7. The matrix is close

to symmetric, and the transfer of ﬁrst-ranked compo-

nents seems to work reasonably well within a set of

three metrics (ISJ, DUR and NJ), while the metrics

RT and CPL produce subspace components, which

are not sufﬁciently informative for other metrics.

4 DISCUSSION AND

CONCLUSIONS

Comparable to experiments close to the visual per-

ception threshold (Schubert et al., 2009; van Dijk

et al., 2008), where characteristics of occipital alpha

oscillations of the EEG have been found informative

about the probability to perceive a stimulus, oscilla-

tory components can also contain information about

the performance quality of a motor task, and inﬂuenc-

ing relevant oscillatory activity by user training can

improve reaction time (Boulay et al., 2011). Spatial

ﬁltering with SPoC offers one possibility to access

such components of the EEG.

In the scatter plots of ﬁve motor performance met-

rics, it was observed that the metric ISJ is correlated

NEUROTECHNIX 2015 - International Congress on Neurotechnology, Electronics and Informatics

with the metric DUR, and DUR with NJ, while the

metric RT is rather uncorrelated to the other metrics.

At ﬁrst glance, this is surprising, as for example the

metrics RT and DUR both are temporal metrics and

nevertheless are only weakly correlated. A possible

explanation is that RT reﬂects a very early phase of

the trial, while DUR includes information also from a

slightly later trial stage.

Analyses based on SPoC showed, that the over-

all structure of those cross-correlations between the

ﬁve metrics can be reproduced well by an transfer ap-

proach: ﬁrst, the best oscillatory EEG component on

one metric was estimated, and subsequently its infor-

mative content (in terms of power comodulation) was

tested against other metrics. The metric RT, for ex-

ample, does not show high correlations with the four

other metrics — correspondingly, the power of the

SPoC component derived by RT also does not cor-

relate well with the four other metrics. The opposite

can be observed for the metrics ISJ, DUR and NJ.

These results are astonishing, as the subspace

transfer approach bears a number of potential pitfalls

— the optimal frequency parameters vary between

the ﬁve metrics, and the eigenvalue ranking of SPoC

components reveals permutations already due to small

changes of the data set, e.g. caused by label noise

or overly small training data sets (Casta

no-Candamil

et al., 2015a). Nevertheless on the grand average, the

transfer results reproduce the cross-correlation struc-

ture of the metrics.

The transfer results may have practical implica-

tions for the prediction of trial outcomes in a rehabil-

itation training: in cases, where no informative sub-

space can be derived for one metric, a transfer of a

subspace derived from another metric may contain in-

formation if cross-applied. In case of patients, where

lower SNR and a small number of available cali-

bration trials are common problems, the transfer ap-

proach may be key to success. But even under higher

SNR conditions, the trial outcome may be predicted

with an increased reliability, if informative subspaces

can be combined, which have been derived from dif-

ferent metrics.

ACKNOWLEDGEMENTS

The authors appreciate support by the German Re-

search Foundation (DFG, grant EXC1086) for the

cluster of excellence BrainLinks-BrainTools. Part of

this work was performed on the computational re-

source bwUniCluster funded by the Ministry of Sci-

ence, Research and the Arts Baden-W

urttemberg and

the Universities of the State of Baden-W

urttemberg,

Germany, within the framework program bwHPC.

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