# Discrimination of Motor Imagery-Induced EEG Patterns in Patients with Complete Spinal Cord Injury

Authors: G. Pfurtscheller; P. Linortner; R. Winkler; G. Korisek; G. Müller-Putz
Journal: Computational Intelligence and Neuroscience
 (2009)

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## Abstract

EEG-based discrimination between different motor imagery states has been subject of a number of studies in healthy subjects. We investigated the EEG of 15 patients with complete spinal cord injury during imagined right hand, left hand, and feet movements. In detail we studied pair-wise discrimination functions between the 3 types of motor imagery. The following classification accuracies (mean± SD) were obtained: left versus right hand 65.03%±8.52, left hand versus feet 68.19%±11.08, and right hand versus feet 65.05%±9.25. In 5 out of 8 paralegic patients, the discrimination accuracy was greater than 70% but in only 1 out of 7 tetraplagic patients. The present findings provide evidence that in the majority of paraplegic patients an EEG-based BCI could achieve satisfied results. In tetraplegic patients, however, it is expected that extensive training-sessions are necessary to achieve a good BCI performance at least in some subjects.


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## Body

## 1. Introduction

Functional
magnetic resonance imaging (fMRI) and EEG studies have shown that executed and
imagined movement activates overlapping and/or similar neural networks in
primary motor and related areas [1–3]. This equivalence of motor execution
and motor imagery in relation to cortical activation is one prerequisite for
the restoration of motor  functions in
para- and/or tetraplegic patients using a brain-computer interface (BCI; [4, 5]). Whether patients with complete spinal cord injury (SCI) are able to control
their brain oscillations reliable and safe through imagined limb movements and
operate herewith a BCI is however still an open question.Sensorimotor
rhythms such as mu and central beta oscillations can be modified by executed
and imagined movement [6–10]. By using multichannel EEG recordings
and applying pair-wise discrimination functions to the EEG signals it is
possible to discriminate between 3 different types of motor imagery (right or
left hand or foot) [11, 12]. In this study we addressed the following questions:
(i) is it possible to discriminate pair-wise between 3 motor imagery-related
EEG patterns (right hand, left hand, and feet) in patients with complete spinal cord injury and (ii) is  this discrimination different for
paraplegic and tetraplegic patients. When a reliable detection of
imagery-related brain states in ongoing EEG is possible the BCI output signal
can be used to control, for example, a neuroprosthesis [5].


## 2. Methods

### 2.1. Subjects and Experimental Task

The
patient group consisted of 15 patients (four females and eleven males) aged from
16 to 64 years (M =  41 years, SD = 14.50). All patients suffered from
a complete sensor and motor paralysis at ASIA level C5 to T12 after a traumatic
SCI between 1.6 months and 32.9 years prior to the measurements. Seven patients
were tetraplegic, and eight patients were paraplegic. Information on the
patients is summarized in Table1.Table 1
Patients characteristics.
PatientDate
of birth
(year)Date
injury (year)Duration
(months)ASIA
levelNumber
of trials
(artifact-free/total)P01198720073.9C5
(Tetra)207/240P02198120075.3C6
(Tetra)220/240P0319571991192Th12
(Para)222/240P0419721989226.5C6
(Tetra)163/240P05195620072.5C5
(Tetra)186/210P061960198238.9Th5
(Para)223/240P07194920074.2C6
(Tetra)155/180P0819591979341.7Th11
(Para)199/240P091943200711.9Th6
(Para)149/210P01019491975394.6Th8
(Para)113/210P011196620075.5Th4
(Para)161/180P012199220081.6Th12
(Para)165/210P0131963200533.2C7
(Tetra)157/210P014196520076.8C6
(Tetra)159/240P0151984200622.1L1
(Para)190/210Mean1965.531998.9386.05Median1963200711.9SD14.8312.08134.42Measurements
were carried out at the Rehabilitation Clinic Tobelbad (Austria). The
experiment was divided into 6–8 runs (depending on the physical condition of
the patient), each consisting of 30 trials of three different motor imagery
tasks (10 trials each). Between those runs participants could (and were
encouraged to) take short breaks for recovery and in order to avoid fatigue.Each
trial began with the presentation of a fixation cross at the centre of the
monitor, followed by a short warning tone at second 2. At second 3, an arrow
pointing randomly left, right, or down, representing one of three different
motor imagery tasks (left hand (L), right hand (R) and both feet (F),
resp.), appeared on the screen for 1.25 seconds, additionally to the
fixation cross. The fixation cross remained displayed on the screen until the
end of the trial at second 8, indicating that the imagination still had to be
performed. This implies a motor imagery lasting for 5 seconds was required.
After that, a blank screen was presented until the beginning of the next trial.
This intertrial period varied randomly between 0.5 and 2.5 seconds.Timing
and experimental paradigm are displayed in Figure1(b).(a) Electrode positions. (b) Timing and experimental paradigm.
(a)(b)


### 2.2. EEG Recording

Continuous EEG signals were recorded
from a grid of fifteen sintered Ag/AgCl ring electrodes (Easycap, Germany) that
were mounted orthogonally in both, horizontal and vertical directions, over the
electrode positions C3, Cz, and C4 (according to the international 10–20 electrode
system, cf. Figure1(a)). The closely spaced interelectrode distance was 2.5 cm. All
electrodes were referenced to the left mastoid. The ground electrode was
mounted at the right mastoid. Impedances were kept below 5 kOhm. For monopolar
EEG derivation a portable amplifier (g.tec, Graz, Austria) was used. Signals
were digitized at 256 Hz and bandpass filtered between 0.5 and 100 Hz.
Sensitivity was set to 100 μV and a notch filter at 50 Hz was used.


### 2.3. Data Analysis

The
method of Common Spatial Patterns (CSP) and Fischer’s linear discriminant
analysis (LDA) classifier were used to discriminate between any 2 classes. The CSP-method
projects multichannel EEG data into a low-dimensional spatial subspace in such
a way that the variances of the filtered time series are optimal for
discrimination. The projection matrix, consisting of the weights of the EEG
channels, is sorted in descending order of the eigenvalues. Before applying CSP
and LDA, a fully automated method for reducing EOG artifacts was applied on the
data. Then, the EEG recordings were visually inspected for remaining EOG and
EMG artifacts and filtered between 8–30 Hz. To get a good generalization
of the classifier a10×10 cross-validation procedure was adopted. The EEG data
from each trial was divided into time segments of 1 s overlapping by half of
their length. For further details see [11, 14].


### 2.4. Calculation of Time-Frequency Maps

To enhance
local oscillations, orthogonal source derivations (Laplacian) were calculated
[15]. After triggering the data, trials of 10 s duration were obtained
including 3 seconds before the cue. The quantification of ERD/ERS was carried
out in four steps: band pass filtering of each trial, squaring of samples (with
smoothing) and subsequent averaging over trials and over sample points. The
ERD/ERS is defined as the percentage power decrease (ERD) or power increase
(ERS) in relation to a one-second reference interval (0.5–1.5 seconds ) before
the warning tone [3]. ERD/ERS values corresponding to 2-Hz frequency bands
ranging from 6–18 Hz (with an overlap of 1 Hz) and 4-Hz frequency
bands ranging from 18–38 Hz (with an overlap of 2 Hz) were calculated. All
values for one EEG channel were subsequently used to construct time-frequency
maps (ERD/ERS maps). The statistical significance of the ERD/ERS values was
verified by applying a t-percentile bootstrap statistic to calculate
confidence intervals with
α=0.05.


### 2.5. Statistical Analysis

An ANOVA was computed in order to
examine whether paraplegic versus tetraplegic patients differ regarding reached
classification accuracy. This ANOVA consisted of the between-subject variable
SCI (2 levels: paraplegics and tetraplegics) and the within-subject variable
ACCURACY (3 levels: left hand versus right hand, left hand versus feet and right hand
versus feet).


## 2.1. Subjects and Experimental Task

The
patient group consisted of 15 patients (four females and eleven males) aged from
16 to 64 years (M =  41 years, SD = 14.50). All patients suffered from
a complete sensor and motor paralysis at ASIA level C5 to T12 after a traumatic
SCI between 1.6 months and 32.9 years prior to the measurements. Seven patients
were tetraplegic, and eight patients were paraplegic. Information on the
patients is summarized in Table1.Table 1
Patients characteristics.
PatientDate
of birth
(year)Date
injury (year)Duration
(months)ASIA
levelNumber
of trials
(artifact-free/total)P01198720073.9C5
(Tetra)207/240P02198120075.3C6
(Tetra)220/240P0319571991192Th12
(Para)222/240P0419721989226.5C6
(Tetra)163/240P05195620072.5C5
(Tetra)186/210P061960198238.9Th5
(Para)223/240P07194920074.2C6
(Tetra)155/180P0819591979341.7Th11
(Para)199/240P091943200711.9Th6
(Para)149/210P01019491975394.6Th8
(Para)113/210P011196620075.5Th4
(Para)161/180P012199220081.6Th12
(Para)165/210P0131963200533.2C7
(Tetra)157/210P014196520076.8C6
(Tetra)159/240P0151984200622.1L1
(Para)190/210Mean1965.531998.9386.05Median1963200711.9SD14.8312.08134.42Measurements
were carried out at the Rehabilitation Clinic Tobelbad (Austria). The
experiment was divided into 6–8 runs (depending on the physical condition of
the patient), each consisting of 30 trials of three different motor imagery
tasks (10 trials each). Between those runs participants could (and were
encouraged to) take short breaks for recovery and in order to avoid fatigue.Each
trial began with the presentation of a fixation cross at the centre of the
monitor, followed by a short warning tone at second 2. At second 3, an arrow
pointing randomly left, right, or down, representing one of three different
motor imagery tasks (left hand (L), right hand (R) and both feet (F),
resp.), appeared on the screen for 1.25 seconds, additionally to the
fixation cross. The fixation cross remained displayed on the screen until the
end of the trial at second 8, indicating that the imagination still had to be
performed. This implies a motor imagery lasting for 5 seconds was required.
After that, a blank screen was presented until the beginning of the next trial.
This intertrial period varied randomly between 0.5 and 2.5 seconds.Timing
and experimental paradigm are displayed in Figure1(b).(a) Electrode positions. (b) Timing and experimental paradigm.
(a)(b)


## 2.2. EEG Recording

Continuous EEG signals were recorded
from a grid of fifteen sintered Ag/AgCl ring electrodes (Easycap, Germany) that
were mounted orthogonally in both, horizontal and vertical directions, over the
electrode positions C3, Cz, and C4 (according to the international 10–20 electrode
system, cf. Figure1(a)). The closely spaced interelectrode distance was 2.5 cm. All
electrodes were referenced to the left mastoid. The ground electrode was
mounted at the right mastoid. Impedances were kept below 5 kOhm. For monopolar
EEG derivation a portable amplifier (g.tec, Graz, Austria) was used. Signals
were digitized at 256 Hz and bandpass filtered between 0.5 and 100 Hz.
Sensitivity was set to 100 μV and a notch filter at 50 Hz was used.


## 2.3. Data Analysis

The
method of Common Spatial Patterns (CSP) and Fischer’s linear discriminant
analysis (LDA) classifier were used to discriminate between any 2 classes. The CSP-method
projects multichannel EEG data into a low-dimensional spatial subspace in such
a way that the variances of the filtered time series are optimal for
discrimination. The projection matrix, consisting of the weights of the EEG
channels, is sorted in descending order of the eigenvalues. Before applying CSP
and LDA, a fully automated method for reducing EOG artifacts was applied on the
data. Then, the EEG recordings were visually inspected for remaining EOG and
EMG artifacts and filtered between 8–30 Hz. To get a good generalization
of the classifier a10×10 cross-validation procedure was adopted. The EEG data
from each trial was divided into time segments of 1 s overlapping by half of
their length. For further details see [11, 14].


## 2.4. Calculation of Time-Frequency Maps

To enhance
local oscillations, orthogonal source derivations (Laplacian) were calculated
[15]. After triggering the data, trials of 10 s duration were obtained
including 3 seconds before the cue. The quantification of ERD/ERS was carried
out in four steps: band pass filtering of each trial, squaring of samples (with
smoothing) and subsequent averaging over trials and over sample points. The
ERD/ERS is defined as the percentage power decrease (ERD) or power increase
(ERS) in relation to a one-second reference interval (0.5–1.5 seconds ) before
the warning tone [3]. ERD/ERS values corresponding to 2-Hz frequency bands
ranging from 6–18 Hz (with an overlap of 1 Hz) and 4-Hz frequency
bands ranging from 18–38 Hz (with an overlap of 2 Hz) were calculated. All
values for one EEG channel were subsequently used to construct time-frequency
maps (ERD/ERS maps). The statistical significance of the ERD/ERS values was
verified by applying a t-percentile bootstrap statistic to calculate
confidence intervals with
α=0.05.


## 2.5. Statistical Analysis

An ANOVA was computed in order to
examine whether paraplegic versus tetraplegic patients differ regarding reached
classification accuracy. This ANOVA consisted of the between-subject variable
SCI (2 levels: paraplegics and tetraplegics) and the within-subject variable
ACCURACY (3 levels: left hand versus right hand, left hand versus feet and right hand
versus feet).


## 3. Results

The power of discrimination between two
different brain states is indicated by the classification accuracy of single EEG
trials analysed within 1-second  time windows. The discrimination time courses for epochs
of 6 seconds (with 1 second prior to cue-onset) for all task combinations (right versus left
hand, left hand versus feet, and right hand versus feet) are shown in Figure2. The
maximal classification accuracies of the first peak together with the
corresponding latencies, measured from cue onset are summarized in Table 2. The
mean accuracy of all subjects (±SD) was 65.03%  ±  8.51 (left versus right hand MI), 68.18%  ±  11.08 (left hand versus feet MI) , and 65.05%  ±  9.25 (right hand versus feet MI),
respectively. (See Table 3 for the mean accuracy of paraplegic versus tetraplegic
patients.)Table 2
Classification accuracy (%) of the maximal peak and its latency (delay) after
cue onset for all 15 patients and all combinations. Accuracies in bold differ
significantly from chance level according to the number of trials (cf. [13]).
PatientLeft
versus right handLeft
hand versus feetRight
hand versus feetAccuracy
(%)Delay
(s)Accuracy
(%)Delay
(s)Accuracy
(%)Delay
(s)P0156.23.554.84.556.91.0P0278.12.086.82.083.13.0P0363.51.0621.563.31.5P0463.51.562.32.067.71.5P0558.72.556.21.551.44.5P0657.91.565.02.558.52.5P0757.22.064.31.555.92.0P0860.41.563.62.062.01.5P09831.586.31.571.12.5P01068.12.085.72.078.82.0P011761.577.71.575.21.5P01259.82.070.61.563.10.5P01357.12.554.44.056.80.5P014640.065.23.060.13.5P01571.82.067.92.571.82.5Mean65.021.868.192.2365.052.03Median63.5265263.12SD8.520.7711.080.949.241.09Table 3
Mean classification accuracy for tetraplegic and paraplegic patients.
Motor
imagerySpinal cord injuryParaplegicTetraplegicMeanSDMeanSDL versus
R (%)67.568.8662.137.7L versus
F (%)72.359.7263.4311.25R versus
F (%)67.997.2361.7010.67Discrimination time courses for a length of
5 s after cue onset. The onset of cue presentation is at second 3. Data from all
15 patients and all 3 brain states are displayed: right versus left hand MI (left
panel), left hand versus feet MI (middle panel), and right hand versus feet MI (right
panel).
(a)(b)(c)In the tetraplegic patient group only one out
of seven tetraplegics had an accuracy  >70% while from the paraplegics five
out of 8 reached a classification accuracy
>70%. An accuracy of 70% is the border, where control can be possible
[16]. In the majority of participants, feet motor imagery was involved in the
best discrimination between two brain states (see Figure 2 and Table 2).The results of the ANOVA show that the
main effect ACCURACY is insignificant. Paraplegic patients (M = 69.3%) do not differ from
tetraplegic patients (M =  62.41%), F(1,13) =  530.292,p  = .151. Furthermore, no significant effect
emerges for the three classification accuracies, left (L) hand versus right (R)
hand (M = 64.85%), left (L) hand versus
feet (F) (M = 67.89%), and right (R)
hand versus feet (F) (M = 64.84%), but a
tendency can be seen, F(2,26) =
2.877,
p =.074 (cf. Figure 3).Figure 3
Two-class classification accuracy for paraplegic and tetraplegic patients.Although
the discriminations of any two different brain states were based on the analysis
of 15 EEG channels recorded over premotor, motor, and parietal areas, different
patterns were found in spatially filtered (Laplacian) recordings over the
primary motor areas (electrode positions C3, Cz, and C4). For illustration,
time-frequency maps (ERD-maps) of two representative subjects are displayed in
Figure4. In subject P02 (Figure 4(a)) clearly visible is the beta increase (ERS) at
Cz during hand MI and the beta decrease (ERD) at Cz during feet MI. No
clear EEG reactivity patterns can be recognized in subject P01 (Figure 4(b)). In
P02 a high classification accuracy was obtained while in P01 no discrimination
between the motor imagery states was possible.Time-frequency-maps for the three types of motor imagery (left hand, right hand,
and feet) computed at electrode positions C3, CZ, C4 (Laplacian) exemplarily
for (a)  a patient with good performance (p02)
and (b) a patient with bad performance (p01).
(a)(b)


## 4. Discussion

In our
study, we applied a classification procedure to multichannel, single-trial EEG
data recorded during classical brain-computer interface training sessions with
3-motor imagery tasks: right-hand, left-hand, and feet movement [17, 18]. One
method suitable for studying temporal aspects of brain activation using
multichannel EEG recordings consists in computing common spatial patterns (CSPs)
[14]. This CSP-method leads to spatial filters that are optimal in the sense
that they extract signals which maximally discriminate between any 2
conditions. A subsequent linear classification of these extracted signals
results in a good recognition rate. With the CSP-method it is possible to study
the separability of EEG patterns associated with 2-motor imagery states with a high
time resolution.The discrimination
time courses in the patients with complete spinal cord injury studied were highly
variable in its shape and magnitude and started in generally with an initial
peak about 1.5 seconds after cue-onset, with a fast increase before and a slow
decline thereafter (Figure2). The great intersubject-variability may be explained
by the used mental strategy (e.g., visual versus kinaesthetic motor imagery, [19]),
the vividness of the imagery process, the mental effort and other psychological
factors as, for example, motivation and attention. Even in one and the same subject the
same mental motor imagery strategy can result in completely different EEG
reactivity patterns dependent on the degree of imagined effort [20].The
main finding of the present study is that there are distinct EEG patterns in
the majority of patients with complete spinal cord injury when they imagine
different movements of hands and feet the first time. These patterns are
however not very pronounced and the mean classification rate was relatively low
around 67%. In contrast, motor imagery in healthy subjects results in clearly
discriminable EEG patterns, when 2-motor imagery tasks are compared. Blankertz
et al. [17] reported a mean classification accuracy of 88.4% in a so-called
calibration session with 3 types of motor imagery (right hand, left hand, and right
foot) in untrained healthy subjects. This data are based on 128 EEG channels
and CSP analysis. Also with CSP analysis applied to 32 EEG channels mean
classification accuracies between 80.0% and 83.3% are reported for left versus
right hand MI and hand versus feet MI [12]. In both studies in the majority of
subjects the best classification results were achieved when foot MI was
involved. One major difference between healthy subjects and patients is very
often that patients have very often cramps and/or spasms and therefore a number
of muscle artefacts in the EEG (see e.g., Table 1 artefact-free versus total
trials).Of interest
is a recently published fMRI study where control subjects and patients had to
kinaesthetically imagine movements of their feet [21]. In the paraplegic
patient group the primary motor cortex was consistently activated, even to the
same degree as during movement execution in the healthy controls. In contrast
to this one other study [22] reported inconsistent fMRI activation in the
primary motor cortex during self-paced foot motor imagery in complete SCI
patients. Of interest is that in the study of Alkadhi et al. [21], a strong
positive correlation was found between the vividness scores of motor imagery in
paraplegics and the activation (fMRI BOLD signal) in cortical areas including
the primary motor cortex and the supplementary motor area (SMA). This can be
interpreted that vividness of motor imagery and/or their mental effort plays an
important role in cortical activation and is perhaps more intensive in SCI
patients than in healthy controls.One
point needs discussion, namely, the slightly higher (but not significant)
classification accuracy of hand versus feet MI as compared to right versus left hand
MI found in patients but also reported in healthy subjects. This can be
interpreted to mean that the EEG patterns induced by feet or foot MI are better
discriminable from the brain state associated with either left or right hand
MI. One reason for this could be the antagonistic behaviour of the upper mu ERD
and ERS during motor imagery known as “focal ERD/surround ERS” [3]. Feet MI
results not only in a midcentrally focused mu and/or beta ERD but very often
also in a bilateral mu ERS over the hand representation area [23]. These
authors reported on a much larger difference in band power changes in the
10–12 Hz frequency band when different (hand versus foot MI) and not homologous limbs
(right versus left hand MI) are compared.In conclusion,
we demonstrated that in the majority of paraplegic patients motor imagery
induced EEG patterns can be discriminated. From this follows that with a small
number of feedback training sessions the separability between motor
imagery-related brain states can be reinforced and a good BCI performance can
be expected. In tetraplegic patients the situation is less clear. Only in one
patient motor imagery-related EEG patterns could be discriminated in the
initial training session. Here extensive trainings sessions without and with
feedback are necessary to achieve a satisfied BCI performance at least in some
patients.



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*Source: 104180-2009-04-29.xml*