Dynamics of a Stimulation-evoked ECoG Potential During Stroke

Rehabilitation

A Case Study

Armin Walter

, Georgios Naros

2,3

, Martin Sp¨uler

, Wolfgang Rosenstiel

, Alireza Gharabaghi

2,3

and Martin Bogdan

1,4

Department of Computer Engineering, Wilhelm Schickard Institute, University of T¨ubingen, T¨ubingen, Germany

Werner Reichardt Centre for Integrative Neuroscience, University of T¨ubingen, T¨ubingen, Germany

Department of Neurosurgery, University Hospital, University of T¨ubingen, T¨ubingen, Germany

Department of Computer Engineering, University of Leipzig, Leipzig, Germany

Keywords:

Cortical Stimulation, Stroke, Electrocorticography, Evoked Activity, CCEP, Latency.

Abstract:

Cortical stimulation is being investigated as a possible tool to support stroke rehabilitation. In particular the

analysis of stimulation-evoked neural activity during the rehabilitation process might be helpful to gain a better

understanding of the brain reorganization associated with functional recovery after stroke. In this paper, the

stimulation-evoked brain activity from a patient with implanted epidural electrodes undergoing an interven-

tion using of brain-computer interfaces combined with cortical stimulation for stroke rehabilitation has been

analyzed. We identiﬁed a component of the evoked cortical activity that exhibited several characteristics that

have not been described before: A signiﬁcant latency decrease over the course of the rehabilitation training, a

signiﬁcantly smaller latency if the patient attempted to move his paralyzed hand compared to rest and a sig-

niﬁcant correlation of the latency with the spectral power of the ECoG signal. In addition to the latency, other

parameters such as the peak amplitude of the evoked activity were tested as well, but showed a smaller effect

size. We hypothesize that such “dynamic” components of the evoked activity that appear to be correlated with

the rehabilitation process and the ongoing brain signal could be a target for future closed-loop stimulation

systems.

1 INTRODUCTION

Brain stimulation is a powerful tool for clinical

practice and research for several reasons: First,

stimulation is thought to modulate the activity of

the stimulated brain area. This makes it useful for

example for the treatment of chronic pain (Tsub-

okawa et al., 1991) and it is also investigated for

stroke rehabilitation (Hummel and Cohen, 2006).

Secondly, if one measures the cortical responses

(cortico-cortical evoked potentials, CCEPs) to short

stimulation pulses, one can derive information about

the functional and effective neural connectivity

(Matsumoto et al., 2004) within the brain.

It is important to note that these evoked potentials

provide a stable measure: At least for transcranial

magnetic stimulation (TMS) and EEG, it has been

shown that the evoked cortical response is repro-

ducible from session to session (Lioumis et al., 2009;

Casarotto et al., 2010). Thus, observed changes

in the evoked activity between sessions give an

indication that general changes in neural connectivity

might have happened over time. However, there is

considerable variability in the shape of the response

within the same session, even if constant stimulation

parameters are used. Some of the variance of the

neural responses to a certain set of stimulation pa-

rameters can be explained by the concept of cortical

excitability: How easily a brain area is activated

by stimulation varies for example depending on the

task of the stimulated person (Nikulin et al., 2003;

Morishima et al., 2009) or the state of consciousness

(Massimini et al., 2005). Task-dependent differences

in the evoked activity within a session might therefore

help to illuminate the role the stimulated brain area

and the area where the answer is recorded play in the

processing of the task.

These points make the analysis of such

241

Walter A., Naros G., Spüler M., Rosenstiel W., Gharabaghi A. and Bogdan M..

Dynamics of a Stimulation-evoked ECoG Potential During Stroke Rehabilitation - A Case Study.

DOI: 10.5220/0004644302410248

In Proceedings of the International Congress on Neurotechnology, Electronics and Informatics (BrainRehab-2013), pages 241-248

ISBN: 978-989-8565-80-8

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

stimulation-evoked potentials very interesting in

the context of motor recovery after stroke: Rehabil-

itation treatments can induce cortical reorganization

(Liepert et al., 2000) which should be accompanied

by observable changes in the evoked neural activity,

motivating the use of these potentials to monitor the

reorganization process. More information about the

processes behind such “dynamic” components of

the evoked activity can be gained, if the stimulation

is then applied while the patient performs different

tasks. The task-dependent behavior could then help

to identify, which processing steps within the brain

have been inﬂuenced by the treatment.

Such an improved understanding of the interac-

tion between stimulation and brain reorganization

might lead to novel treatment options for the patients.

For example, it has been suggested that closed-loop

stimulation for stroke rehabilitation might be more

effective than the current open-loop paradigms (Plow

et al., 2009). One could envision a closed-loop

paradigm where stimulation parameters are adapted

to the ongoing brain activity in order to closely

control evoked potentials connected to the treatment,

an approach which has been shown to be feasible in

an animal model (Brugger et al., 2011). A necessary

prerequisite in order to realize such a system for

human patients is that there has to be an inﬂuence

of the measurable prestimulus neural activity on

the stimulation-evoked potential. For motor-evoked

potentials (MEPs), conﬂicting results are reported

in the literature, with some studies concluding that

the prestimulus spectral power or coherence of the

EEG inﬂuences the MEP amplitude (e.g. (Schulz

et al., 2013)), while other studies fail to ﬁnd such

a relationship (Mitchell et al., 2007; van Elswijk

et al., 2010). In the case of evoked cortical activity,

a relationship between the amplitude of slow oscil-

lations during sleep and the amplitude of the evoked

activity has been described (Bergmann et al., 2012),

but during wakefulness, where the cortical responses

to stimulation vastly differs from those during sleep

(Massimini et al., 2005), no similar inﬂuence has

been reported.

In our work with hemiparetic stroke patients who

underwent implantation of epidural electrodes for the

investigation of the combination of brain-computer

interfaces and cortical stimulation for stroke re-

habilitation (Walter et al., 2009), we analyzed the

CCEPs in several experiments over the course of the

intervention. In this paper we present results from

one patient with a paralyzed hand where we found

a component of the evoked cortical activity after

epidural electrical stimulation which exhibited an

interesting behavior: The latency of this component

decreased over the course of the treatment and

was signiﬁcantly depending on whether the patient

attempted to move the paralyzed hand or not. We also

show that there was a signiﬁcant correlation between

the latency of the component and the spectral power

of the ECoG signal before the stimulus. This study

provides novel insights into stimulation-evoked

potentials, because it is the ﬁrst time that such an

analysis is conducted with (i) a stroke patient who (ii)

participated in the same experiment repeatedly over

several weeks while (iii) undergoing an intervention

attempting to induce neural reorganization using (iv)

implanted electrodes for recording and stimulation.

Almost all other studies on this topic are conducted

with healthy participants and thus restricted to the

use of noninvasive methods such as combined EEG

and TMS.

2 MATERIALS AND METHODS

2.1 Patient

Patient P1 (male, 56 years old) had suffered a stroke

in the right hemisphere 80 months prior to the

study, leading to paralysis of the left hand. He was

implanted with 16 epidural electrodes (Resume II,

Medtronic, Fridley, USA) on 4 strips, arranged in a

4x4 grid covering parts of the primary somatonsen-

sory (S1), primary motor (M1) and premotor cortex

(PMC). The grid was centered over the MEP hotspot

for the extensor digitorum communis muscle as deter-

mined by a TMS mapping (Wassermann et al., 2008)

before the surgery. More details on the patient and the

electrodes can be found in (Walter et al., 2012). The

electrode layout is shown in ﬁgure 1 (left).

The external connections to the electrodes were

present for four weeks and then internalized in a sec-

ond surgery. During these 4 weeks, the patient re-

ceived daily rehabilitation sessions and participated

also in the experiment reported in section 2.4.

2.2 Electrophysiological Recording

ECoG was recorded with a monopolar ampliﬁer

(BrainAmp DC, BrainProducts, Munich, Germany)

with a sampling rate of 1000 Hz and a high-pass ﬁl-

ter with a cutoff frequency of 0.16 Hz. ECoG was

recorded from all epidural electrodes, with the ex-

ception of the stimulation electrode and one electrode

over the somatosensory cortex which was used as the

reference. In the ﬁrst session, channel 1 in the setup of

ﬁgure 1 was used; in all following sessions channel 4.

Apart from the ECoG, EMG was recorded as well on

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200 400 600

−50

msec

µV

200 400 600

msec

200 400 600

msec

200 400 600

msec

−50

µV

6 8

−50

µV

10 11 12

−50

µV

15 16

100 200 300 400 500 600

−50

msec

µV

movement

rest

significant

Figure 1: Left: Overlay of the epidural electrode positions with the MRI of P1. White circles: recording electrodes, black

circle: stimulation electrode. Channel 1 is in the lower left corner, channel 4 in the lower right corner, channel 16 in the upper

right corner and so on. White arrow: Position of channel 8. Center: Average CCEP for stimuli in the movement (blue) and

rest (red) phase for all channels, 50 - 600 msec after the pulse. Black dots mark samples with a signiﬁcant difference in the

amplitude between conditions. Channel 13 on the upper left corner was the reference, therefore it is missing. Data stems for

the third session with P1. Right: Zoom on the averaged CCEPs per condition for channel 8.

the left hand and arm, but it did not play a role for this

analysis. A 50x90 mm adhesive electrode placed un-

der the left clavicle of the patient served as the ground

electrode for the recording. Prior to further analysis,

the ECoG data was re-referenced to channel 13 on the

frontomedial corner of the electrode grid.

2.3 Cortical Stimulation

For epidural electrical stimulation we used an STG

4008 stimulus generator (MultiChannel Systems,

Reutlingen, Germany), capable of a maximum output

current of 16 mA. Stimulation was applied with single

anodal biphasic rectangular symmetric pulses with a

length of 500 µsec and an intensity of 7 mA. The ad-

hesive electrode on the left clavicle of the patient also

served as the cathode for stimulation. The intensity of

7 mA was selected because a single pulse with this in-

tensity consistently evokedsmall MEPs on the left ex-

tensor digitorum communis (EDC) muscle of the pa-

tient. The stimulation pulses were given continuously

throughout the experiment with a ﬁxed inter-stimulus

interval of 2 seconds and a small jitter of ± 40 msec.

2.4 Experiment

The data analyzed here stems from the open-loop

stimulation experiment described in (Walter et al.,

2012). In short, the patient was sitting in a chair with

his left arm and hand ﬁxed to a commercially avail-

able rehabilitation device (Tyromotion Amadeo HTS,

Graz, Austria). This orthosis was capable of opening

and closing the paralyzed hand of the patient.

The task of the patient was to attempt to open his

paralyzed hand on cue. Each trial consisted of three

phases: preparation(2 sec), movement (6 sec) and rest

(8 sec). During preparation, the participant received

an auditory cue but was instructed to wait with the

execution until the ”Go!” command was given at the

start of the movement phase. During the movement

phase, starting with a closed position of the left hand,

the participant had to try to open the left hand until

the end of the movement phase. At that point, another

auditory cue (”Relax!”) was given. During the rest

period, the hand of the participant was returned to its

original closed position which took about 2-3 seconds

and the participant was instructed to relax. This task

design was adapted from (Ramos-Murguialday et al.,

2013) who used it in a noninvasive BCI-guided reha-

bilitation study with stroke patients, but without stim-

ulation.

During the movement phase, the spectral power of

the ECoG recorded on channels over S1 and M1 be-

tween 16 and 22 Hz was extracted and used as input

for an adaptive linear classiﬁer to detect online when

the patient is trying to move the paralyzed hand. This

makes use of the well-known event-related desyn-

chronization (Pfurtscheller and Lopes da Silva, 1999)

of sensorimotor and β rhythms during movements. If

such an intention was found, the orthosis continued

to open the hand, otherwise it was stopped. For the

computation of the spectral power in the presence of

stimulation artifacts, the methods from Walter et al.

(2012) were used.

Over the course of 4 weeks, the experiment was

repeated with the patient weekly, 4 times in total. Per

session, between 42 and 48 trials were conducted.

Regarding stimulation, between 130 and 143 stimuli

were applied during the movement phases and be-

tween 174 and 200 stimuli within the rest phases.

There are more stimuli in the rest phase due to the

greater length of this phase compared to the move-

DynamicsofaStimulation-evokedECoGPotentialDuringStrokeRehabilitation-ACaseStudy

243

Table 1: CCEP parameter for measured signal s(t).

Parameter Computation

Latency τ Woody’s method

Positive peak max{s(τ+ t), −200 ≤ t ≤ 200}

Negative peak min{s(τ+t), −200 ≤ t ≤ 200}

Area

∑

τ+200

t=τ−200

s(t)

Absolute area

∑

τ+200

t=τ−200

|s(t)|

Table 2: Number of stimuli in the analysis.

Session Movement Rest

1 141 166

2 133 159

3 128 169

4 140 196

ment (8 sec vs. 6 sec).

For each of these stimuli, the spectral power of the

prestimulus data and several parameters for the CCEP

were extracted.

2.5 Parametrization of CCEPs

After visual inspection, the late CCEP was most pro-

nounced on channel 8, thus this channel was used

for further analysis (see ﬁgure 1). The latency of

the CCEP component was estimated with Woody’s

method (Woody, 1967): A template was constructed

by averaging the evoked potentials from stimuli in the

movement phase for the time window between 100

and 600 msec after the stimulation pulse. The cross-

correlation of this template with the evoked wave-

forms then yields the latency of the component for

each individual stimulus.

Other extracted parameters are the amplitude of

the strongest positive and strongest negative peak, the

sum of all amplitudes and the sum of the absolute am-

plitudes (table 1). Visual inspection revealed that the

CCEP was contained within an area of ± 200 msec

around the strongest negative peak. Thus, the peak

and area parameters were computed in this range. 11

stimuli of the rest phase of session 1 and 13 of the

rest phase of session 2 were removed from further

analysis, because no proper ﬁt of the template could

be obtained for them, leading to non-meaningful lag

estimations. Furthermore, if no positive or negative

peak could be found, the stimulus was removed as

well from further analysis. Due to this condition, 23

more stimuli were removed. The number of stimuli

per condition and session that entered the analysis are

given in table 2.

2.6 Spectral Analysis

We used autoregressive (AR) models to estimate the

spectral power before each stimulus. To this end, one

second of the signal directly before the stimulus was

extracted and an AR model of order 50 was ﬁtted to

the data and evaluated at frequencies between 5 and

100 Hz in steps of 1 Hz.

2.7 Statistical Analysis

The dependency of the CCEP parameters on the fac-

tors session and movement condition was assessed

with a two-way ANOVA because we found no strong

deviations from normality in the residuals. Post hoc

tests were conducted with unpaired t tests.

We performed permutation tests to investigate

whether there is a signiﬁcant correlation between

the prestimulus spectral power and parameters of

the evoked component and which time points of the

poststimulus signal have signiﬁcantly differing ampli-

tudes for movement and rest.

The Spearman correlation coefﬁcient ρ was used

as a nonparametricmeasure of a monotonousrelation-

ship between the spectral power and the CCEP param-

eters. The signiﬁcance of the correlation was assessed

with a permutation test, repeated 3000 times. In each

repetition k, ρ

between the spectral power values

at each frequency f

∈ 5, . . . , 100 Hz and a random

permutation of the CCEP parameter values was com-

puted and m

= max(|ρ

|) was extracted and aggre-

gated in the set M = {m

}

k=1,...,3000

. The signiﬁcance

threshold for |ρ| at the α = 0.05 level was set as the

value of M corresponding to the one-tailed (1-α)100

percentile of M.

Signiﬁcant differences in the evoked activity be-

tween the movement and the rest phase of the trial

(shown for an example in ﬁgure 1) were assessed in a

similar way: For each time point, Cohen’s d for un-

equal sample sizes was used as a measure for the dif-

ference between the stimuli in each condition. The

condition labels were permuted 3000 times and d

was computed for each repetition k and poststimulus

time point t

. The maximum m

′

= max(d

) of each

repetition was stored in a set M

′

= {m

′

}

k=1,...,3000

yielding the signiﬁcance threshold for d. However,

in contrast to above, because this procedure was per-

formed for all recording channels, the threshold was

taken at the α =

0.05

level, where C is the number of

recording channels. In the case displayed in ﬁgure 1:

C=14.

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250 300 350 400 450 500 550

rest

move

rest

move

rest

move

rest

move

Week 4

Week 3

Week 2

Week 1

Latency [msec]

Latency

Figure 2: Single-trial latency of the evoked component,

grouped by condition and session.

3 RESULTS

3.1 Inﬂuence of Time and Movement

Condition

The patient participated in the experiment 4 times

with a time difference of 1 week between sessions.

We found a signiﬁcant, consistent reduction in the

latency over time and a strong difference between

conditions (ﬁgure 2). This was conﬁrmed by a

two-way ANOVA with factors condition and session,

where a signiﬁcant effect was found for the condi-

tion (F(1,1218) = 2244.6, p < 0.001), the session

(F(3,1218) = 518.5, p < 0.001) and the interaction be-

tween the two (F(3,1218) = 41.5, p < 0.001). Within

each session, the latency during movements was con-

sistently smaller than during rest (one-sided t tests, all

p < 0.001). For the other parameters, the factor ses-

sion was always signiﬁcant and the condition for the

area and the negative peak amplitude. The interac-

tion between both factors was signiﬁcant for the area

and the absolute area, but not the peak measures. The

detailed ANOVA tables and graphs for these parame-

ters are found in appendix 4. The comparison of the

graphs in ﬁgure 4 with ﬁgure 2 make it clear that the

effect size for the latency is much greater than for the

other parameters.

3.2 Inﬂuence of Prestimulus Spectral

Power

The left part of ﬁgure 3 shows that there is a signif-

icant correlation between the spectral power before

the stimulus and the extracted CCEP parameters, es-

pecially in the range of 10-40 Hz, roughly encom-

passing parts of the α band and the β band. Across

sessions, this correlation is best preserved for the la-

tency and to alesser extent for the area. However, one

might argue that this correlation is simply an effect

of a switch in the ”brain state” between the move-

ment and the rest phase. It is well known that the

spectral power of the brain rhythms differs between

these tasks (Pfurtscheller and Lopes da Silva, 1999)

and also that the excitability of the motor cortex is

higher during movements than during rest (Fadiga

et al., 1999). Thus, when taking all stimuli into ac-

count, this correlation might simply be an epiphe-

nomenon of the changing brain state.

For this reason, we also looked at the CCEPs when

only stimuli within the movement phase are taken

into account (ﬁgure 3, right). We found that in this

case, there is still a signiﬁcant reproducible correla-

tion present for the latency, again most pronounced

for the β band, but that it vanishes for the other param-

eters. Because no direct change of the brain state is

expected here, the correlation could indicate a direct

inﬂuence of the spectral power of the ongoing brain

activity on the component latency.

4 DISCUSSION

In patient P1, we identiﬁed a component of the

stimulation-evoked cortical activity which exhibited

an interesting behavior. First of all, in the ﬁrst exper-

imental session, the peak of this component occurred

with a latency of 447.84 ± 43.66 msec (mean ± std).

Although the literature characterizing CCEPs from

epidural stimulation is scarce, TMS-evoked EEG re-

sponses in healthy persons last only for up to 300

msec after the pulse (Ferreri et al., 2011). If we ex-

pect a more or less similar behavior for epidural stim-

ulation, such a late component is certainly an odd-

ity. Secondly, we found that there was a clear dif-

ference in the shape and latency of this component

depending on whether the patient was attempting to

move the paralyzed hand (movement phase) or rest-

ing (rest phase). Thirdly, because the experiment was

repeated weekly over the course of 4 weeks, we were

able to observe the evolution of this potential while

the patient performed the rehabilitation training. We

found a coherent reduction of the latency across ses-

sions. Taken together, this means we have found an

evoked potential which had a possible relationship to

the motor system of the paralyzed limb (difference

between movement and rest), had very atypical char-

acteristics at ﬁrst (high latency) which became less

atypical over the course of the rehabilitation train-

ing (reduction in latency across time). Thus, this po-

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245

latency

Frequency [Hz]

1 2 3 4

100

pos peak

1 2 3 4

neg peak

Session number

1 2 3 4

area

1 2 3 4

abs area

1 2 3 4

−0.5

0.5

latency

Frequency [Hz]

1 2 3 4

100

pos peak

1 2 3 4

neg peak

Session number

1 2 3 4

area

1 2 3 4

abs area

1 2 3 4

−0.5

0.5

Figure 3: Signiﬁcant correlations between parameters of the CCEP and the prestimulus spectral power. Left: all stimuli.

Right: Only stimuli during the movement phase.

tential might serve as a correlate for rehabilitation.

Furthermore, the analysis showed a signiﬁcant rela-

tionship between the prestimulus spectral power and

the latency of the component. The positive correla-

tion between the latency and the spectral power was

most prominent in the β-band, meaning that higher

β-power is associated with a longer latency. This is

consistent with studies on the relationship between

EEG and TMS-evoked MEPs that implicated β-band

power as an inhibitory mechanism in the motor sys-

tem (Schulz et al., 2013) and also with the event-

related desynchronization of β oscillations during at-

tempted movements. On the level of CCEPs, how-

ever, such a relationship has not been reported, yet.

An earlier study with combined EEG and TMS had

identiﬁed movement-related changes in the evoked

brain activity for an N100 component (Nikulin et al.,

2003). They demonstrated a decrease in the ampli-

tude and an increase in latency for the N100 during

movement compared with rest and hypothesized that

the N100 is an inhibitory response that is suppressed

during movements. The evoked potential described

here exhibits the opposite behavior at least for the la-

tency, thus we can speculate that it might represent an

excitatory response.

From this, a closed-loop system could be feasible

which uses online spectral analysis to predict the la-

tency of the evoked activity, stimulating only if this

prediction is within a predeﬁned range in order to re-

duce the variance of the stimulation effect. It would

be very interesting to see whether such an optimized

closed-loop stimulation protocol had an impact on the

recovery of motor function.

Unfortunately, the external connection with the

implanted electrodes of the patient had been removed

in a second surgery before further experiments on this

issue with the patient could take place. Although the

experiment was repeated in two more chronic stroke

patients, no comparable CCEP component could be

identiﬁed. It is unclear whether the occurrence of the

component analyzed here was just due to the speciﬁc

pathophysiology of patient P1 or if it might be repro-

ducible in more patients. For this reason, it would be

very interesting to perform this or a similar experi-

ment with other stroke patients undergoing rehabili-

tative training. It might not be necessary to use im-

planted electrodes for these experiments as combined

EEG and TMS might sufﬁce. One great advantage

of the implanted electrdoes is, however, that these are

ﬁxed in place, eliminating the possibility that the sen-

sitivity of evoked potentials to the stimulation posi-

tion (Casarotto et al., 2010) inﬂuences the analysis.

Similarly, instead of an online analysis of the brain

activity, it might be enough to have the patient per-

form cued attempts to move the paralyzed limb, as

long as one retains the concept of applying single

suprathreshold stimulation pulses over the hotspot of

MEP generation on the paralyzed limb in the lesioned

hemisphere. If the hypothesis is correct that such a

late potential is a correlate of the pathologicalchanges

after stroke, one should be able to observe a potential

with the following characteristics at least for some pa-

tients:

• Location over sensorimotor cortex on the stimu-

lated hemisphere

• High latency at the start of the rehabilitation train-

ing

• Latency reduces over the course of the training

• Latency and other parameters differbetween stim-

uli during the movement and stimuli during the

rest phase.

• Correlation between the prestimulus spectral

power and some parameters of the CCEP

If this is conﬁrmed, it might be worthwhile to at-

tempt a closed-loop stimulation experiment as pro-

posed above to control the evoked component.

ACKNOWLEDGEMENTS

This work was supported by ERC grant 227632.

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246

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−1000 0 1000 2000 3000

rest

move

rest

move

rest

move

rest

move

Week 4

Week 3

Week 2

Week 1

Area [µV]

Area

2000 4000 6000 8000 10000 12000 14000

rest

move

rest

move

rest

move

rest

move

Week 4

Week 3

Week 2

Week 1

Area [µV]

Absolute area

20 40 60 80

rest

move

rest

move

rest

move

rest

move

Week 4

Week 3

Week 2

Week 1

Amplitude [µV]

Positive peak

−120 −100 −80 −60 −40 −20

rest

move

rest

move

rest

move

rest

move

Week 4

Week 3

Week 2

Week 1

Amplitude [µV]

Negative peak

Figure 4: Single-trial parameters of the evoked component, grouped by condition and session.

Table 3: Results of two-way ANOVAs for the CCEP parameters for factors session (S), condition (C) and the interaction

between these (S*C).

Parameter Factor df F(df,1218) p

Latency

S 3 518.5 < 0.001

C 1 2244.6 < 0.001

S*C 3 41.5 < 0.001

Positive peak

S 3 150.93 < 0.001

C 1 2.16 0.14

S*C 3 2.18 0.09

Negative peak

S 3 127.64 < 0.001

C 1 21.26 < 0.001

S*C 3 1.93 0.12

Area

S 3 21.7 < 0.001

C 1 119.7 < 0.001

S*C 3 11.38 < 0.001

Absolute area

S 3 210.29 < 0.001

C 1 1.40 0.24

S*C 3 3.15 0.024

APPENDIX

NEUROTECHNIX2013-InternationalCongressonNeurotechnology,ElectronicsandInformatics

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