Accepted Manuscript

Wireless EEG with individualized channel layout enables efficient motor im-agery training

Catharina Zich, Maarten De Vos, Cornelia Kranczioch, Stefan Debener

PII: S1388-2457(14)00370-8DOI: http://dx.doi.org/10.1016/j.clinph.2014.07.007Reference: CLINPH 2007167

To appear in: Clinical Neurophysiology

Accepted Date: 7 July 2014

Please cite this article as: Zich, C., Vos, M.D., Kranczioch, C., Debener, S., Wireless EEG with individualizedchannel layout enables efficient motor imagery training, Clinical Neurophysiology (2014), doi: http://dx.doi.org/10.1016/j.clinph.2014.07.007

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customerswe are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, andreview of the resulting proof before it is published in its final form. Please note that during the production processerrors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Wireless EEG with individualized channel layout enables efficient

motor imagery training

Catharina Zich1, Maarten De Vos

2,3,4, Cornelia Kranczioch

1,3, Stefan Debener

1,3,4

1 Neuropsychology Lab, Department of Psychology, European Medical School, Carl von Ossietzky

University of Oldenburg, Germany

2 Methods in Neurocognitive Psychology, Department of Psychology, European Medical School, Carl

von Ossietzky University of Oldenburg, Germany

3 Neurosensory Science Research Group, Carl von Ossietzky University of Oldenburg, Germany

4 Cluster of Excellence Hearing4all, Carl von Ossietzky University of Oldenburg, Germany

Corresponding Author:

Catharina Zich

Department of Psychology

University of Oldenburg

26111 Oldenburg, Germany

Tel: +49 441 798 2172

Fax: +49 441 798 5522

E-mail: atharina.zich@uni-oldenburg.de

Keywords: Motor imagery, Mobile EEG, Brain-computer interface, Electrode reduction.

Highlights

1. To reduce the number of channels to two bipolar channels a novel reduction method for

motor imagery is presented, which outperformed a published procedure and the standard

placement.

2. Using an individualized electrode subset, a longitudinal study was performed over four

days of motor imagery practice, in which significant learning effects could be observed.

3. On days 2 to 4 practice took place in an everyday environment using a very user-friendly

EEG system consisting of individualized caps and a low-cost wireless hardware.

Abstract

Objective: The study compared two channel-reduction approaches in order to investigate the

effects of systematic motor imagery (MI) neurofeedback practice in an everyday environment

using a very user-friendly EEG system consisting of individualized caps and highly portable

hardware.

Methods: Sixteen BCI novices were trained over four consecutive days to imagine left and

right hand movements while receiving feedback. The most informative bipolar channels for

use on the subsequent days were identified on the first day for each individual based on a

high-density online MI recording.

Results: Online classification accuracy on the first day was 85.1% on average (range: 64.7 -

97.7 %). Offline an individually-selected bipolar channel pair based on common spatial

patterns significantly outperformed a pair informed by independent component analysis and a

standard 10-20 pair. From day 2 to day 4 online MI accuracy increased significantly (day 2:

69.1%; day 4: 73.3%), which was mostly caused by a reduction in ipsilateral event-related

desynchronizantion of sensorimotor rhythms.

Conclusion: The present study demonstrates that systematic MI practice in an everyday

environment with a user-friendly EEG system results in MI learning effects.

Significance: These findings help to bridge the gap between elaborate laboratory studies with

healthy participants and efficient home or hospital based MI neurofeedback protocols.

INTRODUCTION

Motor imagery (MI) electroencephalogram (EEG) neurofeedback training is a promising

therapeutic approach for the recovery of lost motor function. Patients can learn to modulate

the amplitude of sensorimotor rhythms (SMRs) as recorded with electroencephalogram (EEG)

and magnetoencephalography (MEG). This may initiate cortical reorganization and thereby

support functional recovery (Buch et al., 2008; Caria et al., 2011). However,

neurorehabilitation training is in general effortful and a large amount of training over a

prolonged period of time seems necessary to facilitate adaptive cortical reorganization

(Langhorne et al, 2009). Since commonly available EEG technology is rather cumbersome

and not well suited for frequent use outside of the laboratory, the clinical utility of MI

neurofeedback, and in particular the potential benefit it may have for motor rehabilitation,

remains poorly understood. By reducing considerably the number of electrodes and

combining an individualized electrode subset with a small wireless EEG system the present

study aimed to contribute to bridging this gap and to prepare the ground for an MI

neurofeedback system that can efficiently be applied at home or in a rehabilitation setting.

The neurophysiological basis of motor imagery brain-computer interfaces (BCIs) are

amplitudes of SMRs spanning the 8 to 30 Hz frequency range (Pfurtscheller and Neuper,

2001). The modulation of SMRs can be induced by voluntary internal drives, such as the

execution or imagination of movements (Jasper and Penfield, 1949; Pfurtscheller and

Aranibar, 1979; Pfurtscheller et al., 1997; Pfurtscheller et al., 2006). SMR event-related

desynchronization (ERD) patterns seem to be very similar for executed and imagined

movements (McFarland et al., 2000), which supports the idea that motor imagery involves

similar neural mechanisms to those operating during real movements (Jeannerod, 1995;

2001). Since many individuals can voluntarily control their SMR amplitude using motor

imagery as a mental strategy, SMR-based neurofeedback and BCI applications are very

promising novel neurorehabilitation tools (Prasad et al., 2010; Cincotti et al., 2012; Ortner et

al., 2012).

To be useful as neurorehabilitation tools, BCI applications should be suitable for independent

domestic application. This requires affordability, short set-up time, reliability, and usability of

the tools (Sellers et al., 2010; Holz et al., 2013). Achieving these requirements involves

minimizing EEG preparation time. To this end, the number of EEG channels could be

minimized as much as possible. To achieve and optimize this for a MI neurofeedback was the

first main objective of the present study. For right- and left-hand MI paradigms the minimum

number of electrodes is three, supporting one bipolar channel per hemisphere with the same

common midline reference, plus one ground electrode. Here a symmetric electrode layout that

includes the standard electrode positions C3 and C4 (e.g. Kaiser et al., 2011) or adjacent

positions that are approximately located over the primary motor cortex (Wolpaw et al., 2002)

and a midline FCz position as reference are common (e.g., Wang et al., 2006; 2007a).

However, due to individual differences in brain structure and function, a “one cap layout fits

all” strategy may be suboptimal. Hence, it has been argued that individually determined

channel configurations are preferable (Schröder et al., 2005; Lou et al., 2008). An open

question however is how to derive the most optimal electrode sites for each individual. One

possibility would be to compare each potential electrode pair, but because processing

demands would be very high approaches that restrict the search space are favorable. Such

approaches are available, yet to the best of our knowledge systematic comparisons or a

validation over several days are lacking. We therefore aimed not only to reduce the number of

electrodes for an MI neurofeedback to an individually selected bipolar channel pair but,

importantly, to compare two selection approaches.

In this comparison we firstly considered the selection approach proposed by Lou et al. (2008)

that is based on independent component analysis (ICA). Secondly, we suggest and consider a

novel channel selection method. This method is based on common spatial patterns (CSP),

which is currently the state-of-the-art spatial filter in MI BCIs (Blankertz et al., 2008a). The

two selection procedures were compared to a standard symmetrical 10-20 channel pair. We

used inherently independent data obtained on different days in order to verify the individually

selected channels while avoiding circularity. Avoiding circularity is a very important aspect in

cognitive neuroscience in general (Kriegeskorte et al., 2009) and this holds also for the

selection of channel subsets for the long-term use of MI BCIs. To achieve independence we

applied the two channel selection procedures to EEG data collected in a high-density 94-

channel laboratory recording session and tested the selection on independent data from three

consecutive days recorded outside the laboratory.

The reduction of the number of channels considerably reduces EEG preparation time from

about half an hour to five minutes and thus helps to meet the requirements for a BCI-based

rehabilitation tool. Another important aspect of such a tool is the recording hardware, which

should be mobile, affordable and easy to use. We therefore not only reduced the number of

channels, but also changed from a lab-mounted EEG system to a small, wireless and easy to

set up EEG system (Debener et al., 2012; De Vos et al., 2013; De Vos et al., 2014). We also

moved out of the laboratory to mimic the environment of the end-user, while participants

practiced the MI task over three consecutive days. This enabled us to pursue the second main

objective of this study, which was to investigate whether MI learning takes place given the

massive reduction of EEG channels, hardware adaptations, and the weakly controlled

environment. Many studies in the MI BCI field are based on small sample sizes, tend to lack

clear protocols, or mix BCI novices and experienced BCI users. For example Neuper et al.

(1999) trained four MI and BCI experienced subjects over 5 to 10 sessions, scheduled one to

three sessions per week, while Guger et al. (2000) examined three BCI experiences subjects

over three days and McFarland et al. (2010) trained four patients, three with and one without

previous BCI experience, over two to three weeks. However in spite of these shortcomings, a

number of studies have demonstrated that practicing MI tasks that involve EEG-feedback

results in significant learning effects (Neuper et al., (1999), Guger et al., (2000) and Ono et al.

(2013); but also see Neuper et al., 2009; Friedrich et al., 2013), and we similarly expected to

see MI learning effects. To demonstrate that MI learning takes place in spite of the number of

channels, hardware and recording environment as used in our study is essential for all further

efforts to implement a BCI-based rehabilitation tool.

To summarize EEG-based MI neurofeedback is a promising therapeutic approach for the

recovery of lost motor function. However, there is a gap in the way in which research on

EEG-based MI neurofeedback is conducted and the requirements for an EEG-based MI

neurofeedback system that can be efficiently applied at home or in a rehabilitation setting. In

order to reduce this gap we firstly compared two electrode selection approaches for finding

optimal bipolar channels for each individual with a standard channel pair. Secondly, using the

selected channels in combination with a low cost, small and mobile EEG system we

investigated the feasibility of MI learning in an everyday environment.

METHODS

Participants

The original sample consisted of 22 individuals (14 females; age range 18-33 years; mean

25.1 years) with no history of neurological or psychiatric disorders. All participants were right

handed according to the Edinburgh handedness inventory (Oldfield, 1971) and initially naïve

to MI neurofeedback and BCI experiments. Written informed consent was obtained from each

participant. The study was approved by the local ethics committee. After the first day six

participants (3 females, 3 males) were excluded from further participation. One did not follow

task instructions and for five participants the bipolar channel selection strategy was not

applicable (see section Excluded participants for details).

Experimental Paradigm

N=16 individuals participated on all four consecutive days. Recording time was kept constant

for each participant over the four days, to avoid performance fluctuations depending on the

time of day. Every day three experimental sessions (8 min each) were held, one training

session and two feedback sessions. Figure 1 illustrates the study design. Between the sessions,

there were short breaks of 3 to 5 minutes. Individual sessions follow the standard Graz-MI

protocol and consisted of 40 trials (20 left and 20 right hand trials) presented in randomized

order (Pfurtscheller and Neuper, 2001). Note that the machine learning perspective would

recommend many trials within a single session, as this yields a more stable spatial filter and

classifier (e.g. Blankertz et al., 2008a). The study was however designed to accommodate the

end-users’ needs, which are for instance sleepier and suffer from fatigue (Billows et al.,

2013). This requires short MI sessions. It has been recommended for motor recovery after

stroke (Langhorne et al. 2009) and confirmed in clinical MI studies (Kübler et al., 2005; Buch

et al., 2008; McFarland et al., 2010), that motor practice should be repetitive and high-

intensity. In the present study this was accounted for by testing over four consecutive days. In

accordance with this Blankertz and colleagues (2008a) could not observe training effects over

a single long practice session, which led them to conclude that one day of MI practice is not

enough to show learning effects.

Each trial started with a fixation cross in the center of the screen. After 3 s the cross was

overlaid for 1.25 s with a red arrow pointing to the right or left, indicating the beginning of

the motor imagery period. Participants were instructed to imagine the kinesthetic experience

of a sequential finger tapping task (index-middle-ring-little-index-middle-ring-little) from the

first-person perspective with either the right or left hand as indicated by the direction of the

arrow (Neuper et al., 2005). After 8 s the trial ended with a black screen. Inter-trial intervals

ranged from 0.5 to 2.5 s. In the feedback sessions a light blue horizontal bar varying in length

was displayed during the motor imagery interval, representing the real-time classifier output.

The feedback signal was updated at a frequency of 16 Hz. Before the first training and before

the first feedback session, a few sample trials were presented to the participants to ensure they

understood the task. Stimulus presentation was controlled with OpenViBE 0.15.0 (Renard et

al., 2010) running on day one on a local computer (screen diagonal: 24 inch; screen resolution

1920 x 1080 pixels; distance: 160 cm) and on days 2 to 4 on a standard notebook (screen

diagonal: 11.6 inch; resolution 1366 x 768 pixels; distance: 70 cm).

To evaluate the general ability of motor imagery before the first and after the last recording

session, the German Kinesthetic and Visual Imagery Questionnaire (KVIQ-G) was completed

(Schuster et al., 2012) twice. This self-assessment questionnaire is a reliable and valid tool to

evaluate initial motor imagery abilities and possible changes in motor imagery capacity with

practice (Malouin et al., 2007b; Randhawa et al., 2010; Schuster et al., 2012).

--- FIGURE 1 HERE ---

EEG recordings

On the first day, EEG data were acquired from 94 equidistant infra-cerebral scalp sites with a

central fronto-polar site as ground and a nose-tip electrode as reference using a BrainAmp

system (BrainProducts GmbH, Gilching, Germany). Data were recorded with a resolution of

0.1µV and a sampling rate of 500 Hz (0.1 to 250 Hz bandpass). In addition, a surface

electromyogram (EMG) was recorded from two electrodes per hand, placed on the Opponens

Pollicis and First Dorsal Interossei. EMG signals were used to confirm the absence of target

related muscle activity and to ensure that participants followed task instructions. No

participant used EMG activity systematically to operate the BCI. Due to limited channel

capacities EMG was recorded from only one hand.

At days 2 to 4 continuous EEG data were collected with a modified Emotiv

(www.emotive.com) system as described previously (Debener et al., 2012). Briefly, the

original hardware (128 Hz sampling rate; 0.16 and 45 Hz bandpass) was relocated into a small

and light box (49x44x25 mm; 48 grams total weight), which was tightly attached to a state-of-

the-art infra-cerebral electrode cap from Easycap (www.easycap.de) at the back of the head

with an elastic band. The electrode caps had exactly the same 94 openings as the caps used for

the laboratory 94-channel recordings. Sintered Ag/AgCl electrodes in plastic adaptors were

used for the recordings. Five standard electrodes were fixed to the caps: a midline occipital

site as ground and a midline frontopolar electrode as reference; close approximations of the

10-20 electrodes C3, C4 and FCz. The positions of a maximum of eight further electrodes

were determined for each subject individually, after common spatial pattern and ICA analysis

of the 94-channel EEG recordings from the first day. For all electrodes the same reference

was used and bipolar derivations were calculated on- and offline. The resulting channels are

further referred to as bipolar channels. In addition to the scalp channels four EMG electrodes

were positioned, two on the left and two on the right hand. As before EMG data were

recorded for only one hand.

On all four days electrode impedances where kept below 20 kOhm before the recording

started using the BrainAmp Software (BrainProducts GmbH, Gilching, Germany). At the first

day data were acquired in a laboratory environment (i.e. a dimly lit and sound shielded room),

at the following three days data were collected in a rather noisy office room. The room was

chosen with the purpose to achieve recording conditions that corresponded closely to an

everyday environment: day light could not be controlled, noise from a frequently used

adjacent entry hall was clearly audible, and no hardware other than a notebook and the

wireless, mobile EEG device was used (see Fig. 1). While, for the laboratory recording

participants were seated in a comfortable arm chair optimized for EEG recordings, for the

office recordings participants were seated on an ordinary chair. All data were recorded with

OpenViBE. In all recording sessions participants’ hands rested on their lap with palms up.

Participants were asked to keep their arms and hands relaxed and to minimize eye movements

during the recordings.

Data analysis

Data were analyzed offline using EEGLAB 10.2.2.4b (Delorme and Makeig, 2004) and

BCILAB v1.0 (Delorme et al., 2011), running under MATLAB R2011b (Mathworks, Inc.,

Natick, MA).

CSP-informed channel selection

Because CSP is computationally efficient and maximizes the variance of one class while

simultaneously minimizes the variance for the second, CSP has been established as the state-

of-the-art spatial filter in MI applications. The outcome of the CSP is a square matrix W (filter

x channels), which contains the unmixing weights of spatial filter coefficients. As the first and

the last rows of the matrix represent the most discriminative filter with respect to variance, we

used those to select the most discriminative bipolar channel pair. From each filter one bipolar

channel was derived, consisting of the channels with the absolute maximum and minimum

weight. We reasoned that given the restriction of the number of channels to two, these two

would represent the filter best. Initially raw data were bandpass filtered between 8-30 Hz

(Müller-Gerking et al., 1999; Blankertz et al., 2008a) and segmented from -1.0 s before to 4.0

s after onset of the red arrow. Segments containing large artifacts were rejected (on average

13/120 epochs). The interval from 0.5 to 4.0 s after arrow onset was submitted to a CSP

implementation following Ramoser et al. (2000). From the unmixing CSP matrix W (filter x

channels) the channel (column) with the absolute minimum weight and the channel with the

absolute maximum weight were determined, ideally from the first and the last filter (row). If

either the first or the last filter was clearly distorted by artifact, the next filters (up to five from

the front or back respectively) were evaluated. Each of the two bipolar channels consisted of a

signal channel (maximum) and reference channel (minimum). Selected channels had to fulfill

two restrictions. First, each channel should be part of the top/centered 35 channels of the 94-

channel layout, and second, a bipolar channel had to remain within one hemisphere. Midline

electrode positions were considered valid for both hemispheres. Pilot data indicated that this

channel selection procedure returned reasonable results, despite its simplicity. Iterative CSP

procedures as proposed previously (Farquhar et al., 2006; Popescu et al., 2007; He et al.,

2010) were not applied for the channel selection step, as they are not designed to identify

bipolar channels per hemisphere. Figure 2 displays exemplary for right and left hand MI CSP

topographies and the resulting bipolar channels for two individuals, and the group average

maps and frequency maps of the selected electrodes.

--- FIGURE 2 HERE ---

ICA-informed channel selection

For ICA-based electrode selection the EEG raw data were initially filtered between 1 and 40

Hz, segmented into consecutive time intervals of one second and segments containing large

artifacts were rejected. The remaining data were submitted to extended infomax ICA (Bell

and Sejnowski, 1995) as implemented in EEGLAB 10.2.2.4b (Delorme and Makeig, 2004) to

estimate the unmixing weights (W) of 60 independent components (ICs) from the EEG data.

The resulting weights matrix was applied to the raw data (Debener et al., 2010). This strategy

provides a reasonable trade-off between retaining as much variance in the data as possible and

protecting ICA against the negative influence of channel drift and other processes

incompatible with the statistical assumptions of ICA (De Vos et al., 2012). All obtained ICs

were back-projected to the sensor level. Note that this included ICs reflecting stereotypical

artifact, because the goal was to determine the optimal channels for further online application,

in the presence of those stereotypical artifacts. For further analysis data were again filtered

between 8-30 Hz, segmented from 0.5 s before to 4.0 s after arrow stimulus onset and

segments containing large artifacts were rejected.

Bipolar channel selection procedure closely followed the one proposed by Lou et al. (2008),

with few exceptions. In their study involving eight MI trained subjects Lou and colleagues

acquired right and left hand MI data from 32 channels (120 trials per class), in order to reduce

the number of electrodes to two bipolar channel pairs based on ICA. Therefore the authors

first identified one SMR IC for the left and one for the right hemisphere based on three

criteria: (1) the IC activation spectrum had to include a peak in the µ-frequency range (8-13

Hz), (2) the spatial distribution of the source (forward matrix W-1) had to have a focus over

sensorimotor areas; and (3) the 8-30 Hz band-power of the source had to correlate with the

site of the imagination task, that is, it was expected to show a contralateral power suppression

and an ipsilateral enhancement. In the present study the SMR ICs were selected by two

independent evaluators (CZ, SD) and criterion 1 was tightened as in addition a to a first peak

in the µ-frequency range a second one in the β-band had to be present, located approximately

at twice the frequency than the first peak. Moreover, while Lou et al. selected three task-

unrelated α-ICs to represent noise, we used all ICs except for the selected SMR ICs as noise

estimator. By this we could avoid the additional IC selection step. Moreover, we considered

our approach of noise estimation more realistic for the subsequent low-density recordings.

Despite the minor changes regarding the IC selection, during the channel selection procedure

within the defined ICs we closely followed the method proposed by Lou and colleagues.

Specifically, the channel with the absolute maximum weight in the selected SMR IC was

chosen as signal channel. The corresponding reference channel was required (1) to have the

lowest absolute weight in the selected SMR IC; and (2) to have a noise level as similar as

possible to the noise level of the selected signal channel. To this end the signal-to-noise ratio

(SNR), ratio of task-related power (signal) to noise power, was calculated for all channels and

the channel with the highest SNR was chosen as the respective reference channel. The

reference channel for the left signal channel L was calculated as follows:

SNRL(c) = |���������∗�|

|�������∗|�∑ | ����������∗�� |����

Here wcn is an element of W-1, where the first index indicates the channel number and the

second the IC number. Ideally, ICA decomposed the bandpass filtered data in two SMR ICs

(L, R) and 58 noise ICs ni, i=1,…,k. The band power P of the ICs was obtained by bandpass

filtering, squaring and averaging all samples in the 3.5 s time window. Similar to the CSP-

based channel selection approach, channels were required to be part of the top/centered 35

channels, and a bipolar channel had to stay within one hemisphere. Figure 2 displays

exemplary SMR IC topographies and the selected bipolar channels of two subjects for right

and left hand MI, and the normalized group average and relative frequency of the selected

channels for all participants.

Standard channels

The standard bipolar channel pair consisted of close approximations of the 10-20 electrodes

C3 and C4, respectively referenced to FCz.

Classification

OpenViBE was used to calculate the single-trial classification accuracies online as well as

offline. Band power features were computed by first applying a spatial filter. For the first day

CSP was applied to calculate the appropriate coefficients and for days 2 to 4 the three bipolar

channel pairs were used. EEG data were bandpass filtered (8-30 Hz), segmented into motor

imagery intervals covering 0.5 s until 4.0 s after stimulus onset and squared, to obtain the

power. These intervals were subdivided into 40 bins ((3.5-1)/0.0625), resulting in 1 s lasting

time windows with an overlap of 1-0.0625 s. Samples within one time window were

averaged. The resulting features were submitted to linear discriminant analysis where the

mean single-trial classification accuracy was determined with a seven-fold cross-validation

procedure. To this end each dataset was divided randomly into seven parts of equal size, then

feature selection and classifier training was done on 6/7 of the data and obtained predictions

compared to the real labels of the remaining seventh part.

ERD

To compute the ERD% time course, the procedure proposed by Pfurtscheller and Lopes da

Silva (1999) was used, while keeping most parameter settings as for the single-trial

classification in OpenViBE identical. Specifically, to obtain the power samples of a bipolar

channel the difference between reference and signal channel of the filtered signal (8-30 Hz)

was squared. Task related trials were extracted by segmenting the data from -3.0 s before until

4.0 s after stimulus onset. After averaging the power samples across trials, 96 overlapping

time windows were generated and the samples within each time window averaged. ERD%

was computed as follows: ERD%(t)=(A(t)-R)/Rx100, where R is the power in the frequency

band of 2 s baseline period before stimulus onset, and A the power in the frequency band at

time point t, with reference to stimulus onset. By using this equation, ERD% is reflected by

negative numbers. For further analysis, left and right ERD% time courses were averaged and

only contra- and ipsilateral effects are reported, because hemispheric differences were not

considered in this study. For the statistical analysis of ERD% the average of the time window

from 1.0 s to 4.0 s after stimulus onset was used.

To verify that changes in ERD% over the three consecutive days MI practice are caused by

narrow-band modulations, the log power spectra (1-40 Hz) for day 2 and day 4 were

calculated from raw data for the contra- and ipsilateral hemisphere within the MI interval. The

frequency range of 1 to 40 Hz was subdivided into four frequency bands, namely pre-MI

frequency range (1-7 Hz), µ- (8-12 Hz) and β- band (13-30 Hz) and post-MI frequency range

(31-40 Hz).

Iterative CSP

Reducing the number of channels to one bipolar channel pair resulted in a very lean system,

but the leanest system is only in rare cases the one that performs best for a particular

individual. The iterative CSP, as proposed by Popescu et al. (2007) is a channel reduction

approach and can be used to find the optimal tradeoff between the leanest and the best

performing system. The procedure works as follows: Once the unmixing CSP matrix W is

calculated, the least-discriminative channel (the column with the lowest sum of the absolute

values of the first and last filter), is removed and CSP is applied to the remaining N-1

channels. In the present study this process was repeated until only two channels were left. To

do this, Popescu’s approach had to be modified such that not the sum of the first and last two

filters, but the sum of the first and last filter was used to find the least-discriminative channel.

To further explore the relationship between classification accuracy and number as well as

position of channels for every iteration step, the relative importance for any electrode and the

accuracy were determined. Therefore CSP coefficients were calculated on the training

datasets and applied to the two concatenated feedback sessions. Classification accuracy was

again determined using seven-fold cross-validation. Note that the iterative CSP could not be

considered in the first place because it does not result in a bipolar channel pair.

Statistical analysis

All analysis of variance (ANOVA) models consisted of repeated measurement factors. To

analyze if the three sessions recorded within one day differed in classification accuracy, a

one-way ANOVA with factor session (training, feedback 1, feedback 2) was conducted on the

data obtained on the first day with the 94 channel cap. After channel selection an ANOVA

with the factor channel pair (CSP, ICA, standard) was used to statistically test for the

hypothesized higher accuracies for individualized compared to standard channel pairs. To test

for learning over days 2 to 4 and for differences between the channel pairs, classification

accuracies and ERD% as dependent variables were analyzed with two separate ANOVAs,

comprising the factors day (day 2, day 3, day 4) and bipolar channel pair (CSP, ICA,

standard). Furthermore, one ANOVA with factors day (day 2, day 3, day 4) and laterality

(contra-, ipsilateral) was conducted on the ERD% as dependent variable, to statistically check

if any increase in ERD% was caused by changes within the ipsi- or contralateral hemisphere.

To test that changes in ERD% over the practice period are caused by narrow band power

modulations we performed an ANOVA with the factor day (day 2, day 4) and frequency band

(1-7 Hz, 8-12 Hz, 13-30 Hz, 31-40 Hz). To estimate the test-retest reliability of single-subject

measures Pearson correlations were calculated for classification accuracies and ERD%

between day 2 and day 4. KVIQ scores were analyzed by an ANOVA comprising the factors

day (day 1, day 4) and subscale (visual, kinesthetic), to statistically confirm for the assumed

stronger increase in scores for the kinesthetic subtest compared to the visual subtest. For all

ANOVAs, Greenhouse-Geisser epsilon values were used to account for violations of

sphericity. Effect sizes described are η2 (partial eta squared). Paired sample t-tests were

conducted to follow up significant main effects and interaction effects, reported are two-tailed

p-values. Single-subject classification accuracies were considered significantly above chance-

level if they were above a cut-off value. The cut-off value depended on the number of epochs

(two feedback sessions with 40 trials each) and was defined as chance-level (0.5) plus two

standard errors 0.5 + 2 ∗ ��0.5 ∗ 0.5�/80 = 0.612 (Hill and Schölkopf, 2012). Correlation

analyses were performed to explore the possible association of initial KVIQ scores and initial

accuracy, change over the days in KVIQ scores and accuracy as well as change in KVIQ

scores and change in ERD%.

RESULTS

Day 1 – initial online and offline MI accuracy

On the first day, MI single trial accuracy for the training session was on average 84.2%

ranging from 66.7% to 94.8%. This indicated that individuals experienced having control over

their SMR activity, in the absence of extensive training. Online, the single-trial classification

accuracy was 83.1% (range: 64.7%−96.9%) for the first feedback session and 87% for the

second (range: 69.3%−97.7%). Descriptively, higher accuracies for the second feedback

session indicated better control and confirmed that participants remained motivated over the

three recording sessions, but the difference was not significant (F2,30=1.40, p=.263, η²=.09).

Therefore, for all further analyses data were collapsed across the two qualitatively similar

feedback sessions. The single-trial classification accuracies for each individual and group

average results are shown in Figure 3.

After bipolar channel selection the accuracies for every channel pair and all three pairs

together were calculated offline. Note that this was done for the data of the first day. The

single subject and group average classification accuracies in Figure 3 also show the

performance for each individual channel pair (group average for CSP: 71.3% ± 10.1; ICA:

68.3% ± 11; standard: 66.3% ± 10.8) and all three pairs together (group average: 74.4% ±

9.7). Fourteen out of the sixteen participants performed significantly above chance-level when

either all three pairs together, or just the CSP-based channel pair, was considered. This was

the case for ten individuals for the ICA-based and eleven individuals for the standard channel

pair. The three bipolar channel pairs differed significantly in classification accuracy

(F2,30=7.40, p=.003, η2=.33). Pairwise comparisons showed a significant difference between

CSP and standard channel pair (t15=3.39, p=.004) as well as between CSP and ICA channel

pair (t15=2.43, p=.028), indicating that the CSP-informed bipolar channel pair performed best.

No significant difference was found between ICA and standard channel pair (t15=1.69,

p=.112).

--- FIGURE 3 HERE ---

Day 2 to day 4 – learning effects

The classification accuracies over the three days obtained with the different bipolar channel

pairs were analyzed with a two-way ANOVA with factors day (day 2, 3, 4) and channel pair

(CSP, ICA, standard). A significant main effect of day (F2,30=3.62, p=.041, η2=.19) was found

but no significant main effect of channel pair (F2,30=.01, p=.990, η2=.001), suggesting similar

changes in accuracy over days for all bipolar channel pairs. Therefore, in the following

analysis the online accuracies, based on all three channel pairs together, were considered. The

group average for day 2 to 4 of online single trial classification accuracies, based on all three

bipolar channel pairs together, is displayed in Figure 4. Pairwise comparison confirmed that

accuracies were significantly higher on day 4 than on day 2 (t15=-3.58, p=.003) indicating

learning, in terms of increased accuracies over three days practice. Descriptively, accuracies

improved on average from 69.1% on day 2 to 73.3% on day 4. A strong positive correlation

between classification accuracies of the feedback sessions across channel pairs between day 2

and day 4 (r=.86, p<.001) confirmed an excellent test-retest reliability.

--- FIGURE 4 HERE ---

Confirming previous reports MI caused an ERD prominent above the contralateral

sensorimotor area in the MI specific frequency band spanning the 8 to 30 Hz range1. Grand

average ERD% for the days 2, 3 and 4 are displayed in Figure 5 for the contra- and ipsilateral

hemisphere along with the difference between both. Two ANOVAs, one for the contra- and

one for the ipsilateral ERD%, with factors day (day 2, 3, 4) and channel pair (CSP, ICA,

standard) were conducted. Neither for the contralateral nor for the ipsilateral hemisphere a

significant difference on ERD% between channel pairs was found (contralateral: F2,30=2.85,

p=.101, η2=.16; ipsilateral: F2,30=2.80, p=.103, η2=.16), therefore the following analysis was

based on the average of all three channel pairs. Changes in the difference of contra- and

ipsilateral ERD% over three days of MI training were analyzed by the ANOVA with factor

day (day 2, 3, 4), which revealed a significant effect (F2,30=4.64, p=.024, η2=.24). The

difference of contra- and ipsilateral ERD% was significantly higher on day 4 compared to day

2 (t15=2.44, p=.028). This indicates learning in terms of more discriminative ERD% of the

two hemispheres. The relative power improved in the motor imagery period from 11.7% on

day 2 to 17.0% on day 4. As illustrated, the increase in difference between contra- and

ipsilateral ERD% over the three days was related to changes in ERD% of the ipsilateral rather

than of the contralateral hemisphere. An ANOVA with factors laterality (contra-, ipsilateral)

and day (day 2, day 3, day 4) revealed a significant main effect of laterality (F2,30=16.97,

p=.001, η2=.53) and a significant interaction between day and laterality (F2,30=4.64, p=.024,

η2=.24). Within each day the ERD% of the contralateral hemisphere (day 1: -33.2 ± 19.9; day

2: -33.7 ± 18.5; day 3: -33.6 ± 20.6) was significantly higher than that of the ipsilateral

hemisphere (day 1: -21.2 ± 21.7; day 2: -20.5 ± 21.1; day 3: 16.6 ± 24.8) (all p=.001).

Pairwise comparisons on ERD% within the ipsilateral hemisphere revealed a close to

significant difference between day 2 and 4 (t15=-2.10, p=.053), which was not observed for

the contralateral hemisphere (t15=-.29, p=.775). This indicates that the reported changes in

ERD% can be mainly traced back to the ipsilateral hemisphere.

--- FIGURE 5 HERE --- 1 Additional analyses confirmed that no ERD was evident in the pre- (1-7 Hz) and post-frequency of interest (31-

40 Hz) over the three days of MI practice, see Supplementary Figure S1.

In order to test the frequency-band specificity of the training effect, log power spectra were

calculated. Spectra for the two feedback sessions and the three channel pairs were averaged

because the learning effect was restricted to the feedback sessions (cf. figure 4) and no

significant difference between channel pairs was found with regard to the learning effect on

the three consecutive days (cf. figure 5). Figure 6 illustrates the difference of contra- and

ipsilateral hemisphere power at day 2 and day 4 for four frequency bands. An ANOVA with

the factors day (day 2, day 4) and frequency band (1-7 Hz, 8-12 Hz, 13-30 Hz, 31-40 Hz)

revealed a significant main effect of frequency band (F3,45=7.37, p=.002, η²=.33), weather

factor day failed to reach significance (F1,15=4.04, p=.063, η²=.21). Comparing the power

from day 2 and day 4 for each frequency band, a significant increase could be observed for

the MI relevant frequency bands (µ and β), whereas no significant difference was found for

the other frequency bands (1-7 Hz: t15=-0.91, p=.38; 8-12 Hz: t15=-2.39, p=.031; 13-30 Hz:

t15=-2.17, p=.046; 31-40 Hz: t15=-.68, p=.508). This result was confirmed when repeating this

analysis with a higher spectral resolution with a frequency bin width of 2 Hz (see

Supplementary Figure S2). Thus, the learning effect was frequency-band specific rather than

broadband. Excellent test-retest reliabilities were confirmed by a strong positive correlation of

ERD% across channel pairs between day 2 and day 4. These correlations were significant for

the contra- and ipsilateral hemisphere as well as for the difference between both (r=.96,

p<.001; r=.94, p<0.001; r=.90, p<.001).

--- FIGURE 6 HERE ---

KVIQ

To explore the relationship between initial KVIQ scores and initial online accuracy, Pearson

correlations were calculated, but neither for the total nor for the single subscales a significant

correlation (total: r=-.16, p=.54; kinesthetic: r<-.01, p=.99; visual: r=-.33, p=.22) was found.

Furthermore, KVIQ scores from the first and last day were compared in an ANOVA with

factors day (day 1, day 4) and subscale (visual, kinesthetic). A significant main effect for

factor subscale (F1,15=7.23, p=.017, η2=.33) and a marginal day x subscale interaction

(F1,15=4.48, p=.052, η2=.23) was followed up by t-tests. At the first day scores obtained from

the visual subscale were significantly higher than those obtained from the kinesthetic one

(t15=2.69 p=.017). For the kinesthetic subscale, the scores at the last day were significantly

higher than on the first day (t15=-2.49, p=.025), but no significant effect emerged for the

visual subscale (t15=.20 p=.848). Neither for the total test nor for one of the subscales the

changes between the first and the last day in KVIQ scores correlated significantly with the

changes between day 2 and day 4 in accuracy or in ERD%.

Post Hoc Analyses

To explore the relationship between the number of EEG channels and MI accuracy, the data

from day 1 were submitted to an iterative CSP approach. Figure 7 shows the single-trial

classification accuracies for decreasing number of channels, from all 94 down to two

channels. Topographies illustrate at selective iteration stages the relative frequency of non-

eliminated channels, considering all 16 datasets. In most individuals, the number of channels

could be reduced to ten, without dropping dramatically in performance. The most important

ten channels were located on central scalp sites, very similar to the pattern reported by

Popescu et al. (2007). However, individuals differed largely in overall accuracy, as well as in

the impact of channel reduction on MI accuracy. On a single subject level we categorized the

datasets into three groups. A first group (01, 05, 10, 11, 12, 15, 16) delivered a high level

performance with all channels (>82%) and maintained this level until very few channels were

left. A second group (03, 07, 08, 14) performed in the beginning on a lower but still sufficient

level, but the performance worsened with decreasing number of channels and to a different

degree. The performance of individuals in the third group (02, 04, 06, 09, 13) was poor

(<75%) with all 94 channels and remained low with decreasing number of channels.

Topographies illustrating the relative frequencies of the remaining channels across subjects

revealed more clearly electrodes in proximity to sensorimotor cortex with fewer channels. For

the last few iteration stages a pronounced lateralization to the left hemisphere was observable,

presumably reflecting the inclusion of only right-handed participants into this study.

--- FIGURE 7 HERE ---

A further post-hoc analysis evaluated the role of the number of channels for MI performance

on days 2 to 4. This is because the CSP- and ICA-informed channel selection steps were

performed independent from each other. Therefore, in some cases the same channel was

selected twice, which resulted in differences regarding the total number of channels (seven to

ten) that were used for individual subjects days 2 to 4. This might have caused an artificial

increase of the inter-individual differences observed. To evaluate this, individuals were

divided into two groups (group 1: 7 or 8 channels, N = 6; group 2: 9 or 10 channels, N = 10).

A pairwise comparison showed no significant difference in MI accuracy between these two

groups (t5=.31 p=.766).

DISCUSSION

The present study investigated effects of systematic multi-day MI practice in a natural

environment using a portable EEG system. To this end, based on an initial high-density

recording in the laboratory, the number of channels was reduced to a minimum. Two methods

to obtain an individually selected bipolar channel pair were used and compared with a

standard C3/4 versus FCz bipolar pair. The study reports two important findings: Firstly,

individually selected channels outperformed the standard channels, and the pair based on CSP

performed significantly better than the one obtained by ICA. Secondly, with the low-density

individualized cap and the wireless EEG system we found an MI learning effect, expressed in

higher MI accuracies, ERD% and KVIQ scores after three days MI practice in an everyday

environment.

Unfortunately only a limited number of previous studies examined the effect of MI practice

and a direct comparison of accuracies and ERD% across studies is difficult for several

reasons. Firstly, the participants’ level of previous BCI and/or MI experience ranges from

naïve (Blankertz et al., 2008a; Neuper et al., 2009; Friedrich et al., 2013; Ono et al., 2013),

through experienced (Guger et al., 2000; Lou et al., 2008) up to participant selection

depending on previously achieved performances (Wang et al., 2006; 2007b). Furthermore,

different class types (Blankertz et al., 2007; 2008a), number of classes (Friedrich et al, 2013),

instructions (Neuper et al., 2005; Schuster et al., 2011), frequency bands, channel numbers

and applied spatial filters make it even more difficult to compare the performance obtained in

this study with previous findings.

Day 1 – initial online and offline MI accuracy

Taking the described differences between studies into account, the obtained MI accuracies

from the first day using the high-density cap correspond to previous 2-class MI data obtained

from naïve individuals. Experienced participants, however, like those participating in the

study of Lou et al. (2008), can obtain higher accuracies. The difference at the low-density

level, by comparing the accuracies obtained with the bipolar channel pair based on ICA, is

even larger (Lou et al.: mean: 91.1%, range: 69.8% - 98.3%), despite the fact that the present

study closely followed the channel selection procedure by Lou et al. We attribute this

difference to the fact that our participants had no previous MI experience, while those of Lou

et al. were very experienced. Our own results clearly show that having practiced MI for only

three consecutive days is sufficient to improve MI accuracies and ERD%. Since MI practice

leads to more focused SMR topographies (McFarland et al., 2010; Friedrich et al., 2013), the

discrepancy in accuracy between experienced and naïve individuals may be inflated at low-

density levels.

By comparing the three bipolar channel pairs, our results confirmed that individually selected

channel pairs perform better than standard ones. Within the two individually selected bipolar

channel pairs the pair based on the spatial filter CSP significantly outperformed the ICA

based channel pair. Because not only the underlying spatial filter differed, but also the

selection procedures and SMR topographies (unmixing, forward) used were different this

could be due to several reasons. One possibility is that the advantage is inherent in the CSP

algorithm, because the simultaneous variance maximization of the first class and

minimization of the second class provide better-represented features than ICA components.

However, this is not supported by the literature (Naeem et al., 2009; Wang et al., 2012).

Moreover, one could argue that either the channel selection used for determining the CSP

channels and/or the underlying unmixing weights better reflect the mental state of the

participants. Another reason could be that despite careful screening of all ICs by two

independent evaluators, the optimal SMR components were not found. The manual

identification of ICs with the characteristics associated with MI is difficult, especially when

more than one possible SMR component per hemisphere is present. Such a manual

identification of ICs is subjective, time consuming and error prone if not enough expert

knowledge is available. To overcome these limitations, a future goal should be to develop

software that automatically identifies and clusters SMR components, similar to previous

software solutions, which however were not applicable in the present case (Viola et al., 2009;

Viola et al., 2011).

Reducing the number of channels from high-density to bipolar level was motivated through

issues of practicability and affordability. Our results support that by increasing the number of

channels at appropriate sites higher accuracies can be achieved. But how many channels

represent the optimal trade-off between a lean system and acceptable performance is not well

understood. Schröder et al. (2005) already showed that this question should be answered for

each subject individually, as the performance with individual number and individual

positioning of electrodes revealed better accuracies. The iterative CSP approach as proposed

by Popescu et al. (2007) and applied in the present study as a post hoc analysis is one

approach which could be used to identify the optimal number of channels and channel

positions for each individual. Other iterative procedures based on CSP have been proposed

(Farquhar et al., 2006; He et al. 2010), but a comparable procedure for ICA is missing. From

our iterative CSP results one could conclude that, on average, the number of channels

providing a faire trade-off between a lean system with a short set-up time and an acceptable

performance is close to ten. However, the number of channels needed to perform

approximately at the same level as with the full 94-channel cap varied strongly between

individuals. Therefore, the best strategy seems to be to define both, optimal low-density

channel number and optimal layout for each person separately. For excessive longitudinal

training studies the extra effort going into an individualized cap design would pay off quickly.

In addition to the number of electrodes also the type of electrodes used is crucial for a

practicable EEG system. Without doubt applicability of regular MI practice would be

improved further by replacing the traditional wet electrodes with high quality dry electrodes

(Chi et al., 2010; Liao et al., 2012). To the best of our knowledge no previous MI study used

dry electrodes in a home setting and signal quality issues remain with currently available dry

electrodes. Therefore we refrained from using dry electrodes in the present study.

Day 2 to day 4 – learning effects

The most crucial aspect of any rehabilitation program is the resulting benefit for the patient. It

is likely that the patients’ benefit depends very often on the ability to perform a rehabilitation

task appropriately, which means in the case of MI training to imagine moving distinct body

parts. The high relevance of the individual ability to learn MI is reflected among other things

by the attempts of Blankertz et al. (2008a) to show a learning effect within one practice

session. Despite a large number of trials no significant trend in accuracy over time was found,

therefore the authors concluded that one day of MI practice is not enough to show learning

effects. In contrast to this study we kept the recording sessions per day very short (approx. 24

min MI practice per participant per day), in order to sustain attention and keep participants’

compliance up over the four consecutive days MI practice, both particularly important issues

for patients.

Our results show frequency specific MI learning effects over a time range of four consecutive

days. Neuper et al. (1999) similarly observed learning in terms of improved classification

accuracies and increased differences of contra- and ipsilateral ERD% after 5 to 10 sessions in

all of their four experienced individuals with a fixed bipolar channel pair, and Guger et al.

(2000) reported an improvement in classification accuracy for three experienced individuals

after three days of MI practice. In spite of these parallels a direct comparison with our results

is not possible because firstly, Guger and colleagues the actual individual learning effect

could not be obtained due to irregular adaptation of spatial filter coefficients, and secondly,

our participants were novice BCI users. For naïve individuals, improved classification

accuracies and significantly increased ERD% effects were reported by Ono et al. (2013). They

observed that practicing MI over five consecutive days induced prolonged and more robust

contralateral ERD% as well as smaller inter-trial variability compared to the initial session.

The lateralization of the learning effect towards the contralateral side is not supported by our

study however, as our results provide evidence that MI practice leads to a decrease of

ipsilateral rather than an increase in contralateral ERD%. Our observation of an ipsilateral

learning effect could indicate that MI practice goes along with a higher degree of decoupling

of the hemispheres. Testing this idea will require further MEG or high-density EEG studies to

obtain detailed spatial and temporal information of the SMRs (Cheyne, 2013).

More complex 2D motor imagery based BCI experiments also provided evidence that

participants gradually learned to control their SMRs over 21 to 68 sessions (Wolpaw and

McFarland, 2004; McFarland et al., 2010). Royer et al. (2010) reported that their well-trained

participants learned to successfully navigate a helicopter continuously in a 3D space with a 4-

class motor imagery BCI within 8 to 11 sessions. In contrast, some MI studies did not find a

general improvement of performance or larger contra- versus ipsilateral ERD% differences

over several days of practice (Neuper et al., 2009; Friedrich et al., 2013). This discrepancy

may be explained by differences in how frequent the classifier was updated. The importance

of a frequent update of the classifier (Shenoy et al., 2006) and a sufficient number of trials for

stability (Blankertz et al., 2008a) has been reported before, and it is conceivable that

suboptimal settings will hamper MI learning effects. Moreover, in the case of Friedrich et al.

(2013) it should be taken into consideration that a 4-class MI paradigm was used. It is very

likely that learning to operate a 4-class MI paradigm is much more difficult and thus requires

more practice than learning to steer a 2-class paradigm.

To the best of our knowledge the present study is the first to demonstrate MI learning in a

comparatively large sample of naïve participants in an everyday environment. Learning was

reflected in higher classification accuracies and an increased difference of contra- and

ipsilateral ERD% after continued practice in an everyday environment. The learning effect

was likely facilitated by the short duration of the practice sessions, which helped to avoid

exhaustion and boredom. Another probable reason for why we obtained a significant learning

effect despite a relative short practice period is that our practice schedule was well defined,

meaning that every participant practiced on four consecutive days, every day at the same time,

and every day supervised by the same experimenter. The number of sessions was constant

every day and known to the participants. Thus, unlike other studies (Millán et al., 2004;

McFarland et al., 2010; Royer et al., 2010), we avoided varying intervals between practice

days and kept as many variables of the design as possible constant. But in spite of the overall

learning effect, drops in performance between days were evident in nearly all participants.

Such inconsistency is typical for BCI experiments and has been reported previously in studies

conducted in a laboratory environment (McFarland et al., 2010). The drops in performance

likely relate to motivational issues or to attentional lapses. It is important to note that even in

light of inconsistencies in the learning curves, a common trend in learning curves is

recognizable. First, performance improved within one day between sessions. Second, this

improvement increased over days, that is, on the last day the improvement from the first to the

last session was higher than on the first day. This pattern is also evident in the group average

of the present data, and it is in line with observations made by Millán et al. (2004). As a final

observation, our data indicate that the learning effect is limited to the feedback sessions as

performance during the training session was comparable for all four days. This emphasizes

the importance of feedback for MI learning.

With regard to stroke rehabilitation, at present we can only speculate whether patients would

show the same changes in ERD as our healthy volunteers. Neural plasticity after stroke may

compensate for the loss of motor function. On the other hand, unimpaired motor projections

and competitive interaction contribute to maladaptive plasticity, which negatively affects

motor recovery. Therefore one reasonable assumption is that patients show pre-training a

weaker desynchronization of the lesioned hemisphere, and due to maladaptive plasticity a

stronger bilateral activation than healthy individuals (e.g. Johansen-Berg et al, 2002; Grefkes

and Ward, 2013). Training effects could firstly manifest itself in an increase in ERD% in the

hemisphere ipsilateral to the impaired limb, in parallel to the ipsilateral MI learning effect

observed in the present study. This could then be interpreted as minimization of maladaptive

reorganization. The importance of the ipsilateral hemisphere after stroke, as adaptive and

compensatory hemisphere, has been reported before (Fridman et al., 2004; Buch et al., 2008;

Caria et al., 2011). Secondly, we would predict a training-related increase in

desynchronization for the lesioned hemisphere if motor functions of the lesioned hemisphere

recover. Evidently, this prediction can only be investigated in a patient population. We

believe that alongside these speculations our results clearly show that it is important not to

neglect the unlesioned hemisphere in neurorehabilitation training protocols. However, as

bilateral training may prevent learned nonuse but enhance maladaptive plasticity (Takeuchi

and Izumi, 2012), the hands should be unilaterally imagined. Consequently, in the case of 2-

class paradigms, right versus left hand imagination is more advisable than one hand

imagination versus rest.

KVIQ

KVIQ scores obtained on the first day were higher for the visual than for the kinesthetic

subscale, which corresponds to previous findings (Malouin et al., 2007a). Good test-retest

reliabilities of the total KVIQ and its subscales have been reported for non-disabled

individuals (Malouin et al., 2007b) and patients (Malouin et al., 2007b; Randhawa et al.,

2010; Schuster et al., 2012), which allowed for a fair comparison between the KVIQ scores

obtained on the first and last day in our study. Therefore, higher scores for the kinesthetic but

not for the visual subscale on the last day in comparison to the first day indicate improved

kinesthetic motor imagery skills after four days of practice.

Excluded participants

From the initially twenty-two subjects who participated in the study, five had to be excluded

because the obtained bipolar channels pairs were not suitable. For one participant neither the

ICA reveal motor imagery sensitive ICs, nor the CSP result in MI sensitive filter. For four

participants MI ICs could be selected, but for none of them did the CSP result in an MI

sensitive filter, even if the first and last five filters were considered. The mean accuracy for

these four participants using the ICA-based channel pair was 53.3%, indicating that their

EEGs were not characterized by clear sensorimotor cortex activity. This may explain why the

CSP approach failed. The ICA approach seems less affected by this lack of clear sensorimotor

cortex activity, which is also reflected in the finding that with ICA SMR components can

even be obtained from resting state EEG (Wang et al., 2012). But regardless of spatial filter,

the inability of a non-negligible proportion of individuals to successfully do MI has been

reported in many previous studies (Blankertz et al. 2010). Guger et al. (2003) for instance

tested 99 naïve participants and found that only approximately half of them reached

classification accuracies above 70%. To improve the applicability of MI based neurofeedback

further studies are clearly required that investigate the relationship between MI BCI

performance and individual differences in the neurophysiological correlates of MI.

Future issues

To conclude, this study demonstrates that MI performance and ERD% can be significantly

improved after three days MI practice in an everyday environment using individually

designed electrode caps in combination with a wireless EEG system. Furthermore, our results

show that MI neurofeedback can be realized with a bipolar channel pair and better so by

taking individually-selected sites instead of standard channel sites. If taking this finding a step

further then the system best adapted to a user should return the highest performance. That is,

performance can likely be maximized when in addition to the individual selection of channel

number and position, the number of features and the features themselves (Schröder et al.,

2003; Fabiani et al., 2004), as well as the frequency range (Blankertz et al., 2006; 2007;

2008a) are determined individually. It would be desirable to implement this in an automatic

and user-friendly way, similar to recent advances in P300 speller application (Kaufmann et

al., 2012).

Moreover, our results show that MI practice leads to an increase in the difference between

contra- and ipsilateral ERD%. Others observed changes in SMR topographies following MI

practice (McFarland, 2010; Friedrich et al., 2013). It is now important to evaluate how

changes in SMR relate to clinical improvement, for instance after stroke (Buch et al., 2008).

But in the meanwhile tentative recommendations for long-term MI based neurorehabilitation

protocols can be derived. To achieve optimal therapeutic success it seems important to collect

high-density reference EEG data after regular intervals of MI practice and, if necessary, to

adapt individual low-density channel positions.

ACKNOWLEDGEMENTS

We thank Jeremy Thorne and Martin Bleichner for proofreading the manuscript. CZ was

funded by the PhD program ‘Signals and Cognition’ (Niedersächsisches Ministerium für

Wissenschaft und Kultur). CK is supported by grant KR 3433/2-1, German Research

Foundation (DFG).

LEGEND TO FIGURES

Fig. 1 Experimental paradigm and pictures illustrate the different recording environments and

EEG systems. Left: Rear view of a participant wearing the high-density EEG system while

performing the motor imagery task in the laboratory, as on the first day. Right: Participant

performing the same task in an everyday environment using a low-density wireless EEG

system, as on days 2 to 4. The amplifier is fixed to the lower edge of the EEG cap. Electrodes

are placed forming two bipolar channel pairs (highlighted in red: ICA-based, highlighted in

green: CSP-based) in accordance to the scalp distribution of the sensory motor rhythm of the

participant.

Fig. 2 CSP (a, b) and ICA (e, f) SMR topographies for right (a,e) and left (b,f) hand MI of two

exemplary subjects and the normalized grand average from N = 16 subjects (bottom row).

Middle columns (c, d) displays the selected bipolar channel pairs respectively and the relative

frequency of selected channels (bottom row; blue = reference, orange = signal channel).

Fig. 3 Single subject and group average EEG single-trial classification results plotted as

percent accuracy for: online high-density performance using CSP (blue) and offline calculated

low-density performance for all channel pairs together (grey), CSP (green), ICA (red) and

standard channel pair (black).

Fig. 4 Group average (N=16) EEG single-trial classification results of all three channel pairs

together plotted as percent accuracy for the training, feedback 1 and feedback 2 sessions for

days 2, 3 and 4.

Fig. 5 Grand average ERD% from N=16 for every bipolar channel. Straight line indicate

contralateral and dotted line ipsilateral bipolar channel, with respect to stimuli direction (a).

Differences between contra- and ipsilateral ERD% for all three channel pairs, horizontal line

represents the average ERD% for the MI time window (b). Grey shaded area indicate time

window of stimulus onset.

Fig. 6 Log power spectra of four frequency bands for days 2 and 4. Shown is the difference

between contra- and ipsilateral hemisphere, with respect to stimulus direction. The difference

is significantly larger on day 4 in the frequency bands covering the µ (8-12 Hz) and β (13-30

Hz) range.

Fig. 7 Single subject and group average offline classification accuracies for decreasing

number of channels, ranging from all 94 (left) to 2 (right side). Topographies show the

relative frequency of remaining channels at selective iteration stages considering all subjects.

REFERENCES

Bell AJ, Sejnowski TJ. An information-maximization approach to blind source separation and

blind deconvolution. Neural Comput 1995;7:1129-1159.

Billows IJ, Khan S, Sterr A. Exploring sleep, sleepiness and fatigue in chronic stroke: A mixed methods study. Int J Stroke 2013;8:73-73.

Blankertz B, Dornhege G, Krauledat M, Müller KR, Curio G. The non-invasive Berlin brain–

computer interface: fast acquisition of effective performance in untrained subjects.

Neuroimage 2007;37:539-550.

Blankertz B, Dornhege G, Krauledat M, Müller KR, Kunzmann V, Losch F, et al. The Berlin

Brain-Computer Interface: EEG-based communication without subject training. IEEE

Trans Neural Syst Rehab Eng 2006;14:147-152.

Blankertz B, Losch F, Krauledat M, Dornhege G, Curio G, Müller KR. The Berlin Brain–Computer Interface: accurate performance from first-session in BCI-naive subjects.

IEEE Trans Biomed Eng 2008a;55:2452-2462.

Blankertz B, Sannelli C, Halder S, Hammer EM, Kübler A, Müller KR, et al.

Neurophysiological predictor of SMR-based BCI performance. Neuroimage

2010;51:1303-1309.

Blankertz B, Tomioka R, Lemm S, Kawanabe M, Müller KR. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Processing Mag 2008b;25:41-56.

Buch E, Weber C, Cohen LG, Braun C, Dimyan MA, Ard T, et al. Think to Move: a

Neuromagnetic Brain-Computer Interface (BCI) System for Chronic Stroke. Stroke

2008;39:910-917.

Caria A, Weber C, Brötz D, Ramos A, Ticini LF, Gharabaghi A, et al. Chronic stroke

recovery after combined BCI training and physiotherapy: A case report.

Psychophysiology 2011;48:578-582.

Cheyne D. MEG studies of sensorimotor rhythms: A review. Exp Neurol 2013;245:27-39.

Chi YM, Jung TP, Cauwenberghs G. Dry-contact and noncontact biopotential electrodes:

methodological review. IEEE Rev Biomed Eng 2010;3:106-19.

Cincotti F, Pichiorri F, Aricò P, Aloise F, Leotta F, De Vico Fallani F, et al. EEG-based

Brain-Computer Interface to support post-stroke motor rehabilitation of the upper limb.

Conference Proceedings - IEEE Eng Med Biol Mag 2012:4112-4115.

De Vos M, Thorne JD, Yovel G, Debener S. Let’s face it, from trial to trial: Comparing procedures for N170 single-trial estimation. Neuroimage 2012;63:1196-1202.

De Vos M, Gandras K, Debener S. Towards a truly mobile auditory brain-computer interface:

Exploring the P300 to take away. Psychophysiology 2013;91:46-53.

De Vos M, Kroesen M, Emkes R, Debener S. P300 speller BCI with a mobile EEG system: Comparison to a traditional amplifier. J Neural Eng. doi: 10.1088/1741-

2560/11/3/036008.

Debener S, Minow F, Emkes R, Gandras K, De Vos M. How about taking a low-cost, small,

and wireless EEG for a walk? Psychophysiology 2012;49:1449-1453.

Debener S, Thorne JD, Schneider TR, Viola FC. Using ICA for the Analysis of Multi-

Channel EEG Data. In: Ullsperger M, Debener S, editors. Simultaneous EEG and fMRI:

Recording, Analysis, and Application. Oxford University Press, 2010:121-133.

Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG

dynamics including independent component analysis. J Neurosci Methods 2004;134:9-

21.

Delorme A, Mullen T, Kothe C, Akalin AZ, Bigdely-Shamlo N, Vankov A, et al. EEGLAB, SIFT, NFT, BCILAB, and ERICA: new tools for advanced EEG processing. Comput

Intell Neurosci 2011;130714. doi:10.1155/2011/130714.

Fabiani GE, McFarland DJ, Wolpaw JR, Pfurtscheller G. Conversion of EEG activity into

cursor movement by a brain-computer interface (BCI). IEEE Trans Neural Syst Rehab

Eng 2004;12:331-338.

Farquhar J, Hill J, Lal TN, Schölkopf B. Regularised CSP for sensor selection in BCI. In:

Müller-Putz GR, Brunner C, Scherer R, Schlögl A, Wriessnegger S, Pfurtscheller G,

editors. Proceedings of the 3rd International Brain-Computer Interface Workshop and

Training Course 2006. Graz, 2006:14-15.

Fridman EA, Hanakawa T, Chung M, Hummel F, Leiguarda RC, Cohen LG. Reorganization of the human ipsilesional premotor cortex after stroke. Brain 2004;127:747-758.

Friedrich EVC, Scherer R, Neuper C. Long-term evaluation of a 4-class imagery-based brain–

computer interface. Clin Neurophysiol 2013;124:916-927.

Grefkes C, Ward NS. Cortical reorganization after stroke: how much and how functional? Neuroscientist 2014;20:56-70.

Guger C, Edlinger G, Harkam W, Niedermayer I, Pfurtscheller G. How many people are able

to operate an EEG-based brain-computer interface (BCI)? IEEE Trans Neural Syst

Rehab Eng 2003;11:145-147.

Guger C, Ramoser H, Pfurtscheller G. Real-time EEG analysis with subject-specific spatial

patterns for a brain-computer interface (BCI). IEEE Trans Rehabil Eng 2000;8:447-456.

He L, Gu Z, Li Y, Yu Z. Classifying Motor Imagery EEG Signals by Iterative Channel

Elimination according to Compound Weight. In: Wang FL, Deng H, Gao Y, Lei J, editors. Artificial Intelligence and Computational Intelligence, Lecture Notes in

Computer Science. Berlin Heidelberg: Springer, 2010:71-78.

Hill NJ, Schölkopf B. An online brain–computer interface based on shifting attention to

concurrent streams of auditory stimuli. J Neural Eng 2012;9. 026011.

Holz EM, Botrel L, Kübler A. Bridging Gaps: Long-Term Independent BCI Home-Use by a

Locked-In End-User. In: Proceedings of the TOBI Workshop IV, Sion, 2013:35-36.

Jasper H, Penfield W. Electrocorticograms in man: Effect of voluntary movement upon the

electrical activity of the precentral gyrus. Archiv für Psychiatrie und Zeitschrift Neurologie 1949;183:163-174.

Jeannerod M. Mental Imagery in the Motor Cortex. Neuropsychologia 1995;33:1419-1432.

Jeannerod M. Neural Stimulation of Action: A Unifying Mechanism for Motor Cognition.

Neuroimage 2001;14:103-109.

Johansen-Berg H, Rushworth MF, Bogdanovic MD, Kischka U, Wimalaratna S, Matthews

PM. The role of ipsilateral premotor cortex in hand movement after stroke. PNAS

2002;99:14518-23.

Kaufmann T, Völker S, Gunesch L, Kübler A. Spelling is Just a Click Away - A User-

Centered Brain-Computer Interface Including Auto-Calibration and Predictive Text Entry. Front Neurosci 2012;6:72.

Kriegeskorte N, Simmons WK, Bellgowan PS, Baker CI. Circular analysis in systems

neuroscience: the danger of double dipping. Nat Neurosci 2009;12:535-40.

Kübler A, Nijboer F, Mellinger J, Vaughan TM, Pawelzik H, Schalk G, et al. Patients with

ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 2005; 64:1775-1777.

Langhorne P, Coupar F, Pollock A. Motor recovery after stroke: a systematic review. Lancet

Neurol 2009;8:741-54.

Liao LD, Chen CY, Wang IJ, Chen SF, Li SY, Chen B, et al. Gaming control using a wearable and wireless EEG- based brain-computer interface device with novel dry

foam-based sensors. J Neuroeng Rehabil 2012; 9:5.

Lou B, Hong B, Gao X, Gao S. Bipolar electrode selection for a motor imagery based brain–

computer interface. J Neural Eng 2008;5:342-349.

Malouin F, Richards CL, Durand A, Doyon J. Clinical Assessment of Motor Imagery After

Stroke. Neurorehabil Neural Repair 2007a;22:330-340.

doi:10.1177/1545968307313499.

Malouin F, Richards CL, Jackson PL, Lafleur MF, Durand A, Doyon J. The Kinesthetic and Visual Imagery Questionnaire (KVIQ) for Assessing Motor Imagery in Persons with

Physical Disabilities: A Reliability and Construct Validity Study. J Neurol Phys Ther 2007b;31:20-29.

McFarland DJ, Miner LA, Vaughan TM, Wolpaw JR. Mu and beta rhythm topographies

during motor imagery and actual movements. Brain Topogr 2000;12:177-186.

McFarland DJ, Sarnacki WA, Wolpaw JR. Electroencephalographic (EEG) control of three-

dimensional movement. J Neural Eng 2010;7:252-259.

Millán JR, Renkens F, Mouriño J, Gerstner W. Brain-Actuated Interaction. Artificial Intelligence 2004;159:241-259.

Müller-Gerking J, Pfurtscheller G, Flyvbjerg H. Designing optimal spatial filters for single-

trial EEG classification in a movement task. Electroenceph clin Neurophysiol

1999;110:787-798.

Naeem M, Brunner C, Pfurtscheller G. Dimensionality Reduction and Channel Selection of

Motor Imagery Electroencephalographic Data. Comput Intell Neurosci 2009;1:1-8.

Neuper C, Scherer R, Reiner M, Pfurtscheller G. Imagery of motor actions: Differential

effects of kinesthetic and visual–motor mode of imagery in single-trial EEG. Cognitive Brain Res 2005;25:668-677.

Neuper C, Scherer R, Wriessnegger S, Pfurtscheller G. Motor imagery and action

observation: Modulation of sensorimotor brain rhythms during mental control of a

brain–computer interface. Clin Neurophysiol 2009;120:239-247.

Neuper C, Schlögl A, Pfurtscheller G. Enhancement of left-right sensorimotor EEG

differences during feedback-regulated motor imagery. Electroenceph cin Neurophysiol 1999;16:373-382.

Oldfield RC. The assessment and analysis of handedness: The Edinburgh inventory.

Neuropsychologia 1971;9:97-113.

Ono T, Kimura A, Ushiba J. Daily training with realistic visual feedback improves

reproducibility of event-related desynchronisation following hand motor imagery. Clin Neurophysiol 2013;124:1779-1786.

Ortner R, Irimia DC, Scharinger J, Guger C. A motor imagery based brain-computer interface

for stroke rehabilitation. Stud Health Technol Inform 2012;181:319-323.

Pfurtscheller G, Aranibar A. Evaluation of event-related desynchronization (ERD) preceding

and following voluntary self-paced movement. Electroenceph clin Neurophysiol 1979;46:138-146.

Pfurtscheller G, Brunner C, Schlögl A, Lopes da Silva FH. Mu rhythm (de)synchronization

and EEG single-trial classification of different motor imagery tasks. Neuroimage

2006;31:153-159.

Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and

desynchronization: basic principles. Electroenceph clin Neurophysiol 1999;110:1842-

1857.

Pfurtscheller G, Neuper C. Motor imagery and direct brain-computer communication. Proc IEEE 2001;89:1123-1134.

Pfurtscheller G, Neuper C, Flotzinger D, Pregenzer M. EEG-based discrimination between

imagination of right and left hand movement. Electroenceph clin Neurophysiol

1997;103:642-651.

Popescu F, Fazli S, Badower Y, Blankertz B, Müller KR. Single Trial Classification of Motor

Imagination Using 6 Dry EEG Electrodes. PLoS ONE 2007;2:e637.

Prasad G, Herman P, Coyle D, McDonough S, Crosbie J. Applying a brain-computer interface

to support motor imagery practice in people with stroke for upper limb recovery: a

feasibility study. J Neuroeng Rehabil 2010;7:60.

Ramoser H, Müller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG

during imagined hand movement. IEEE Trans Rehabil Eng 2000;8:441-446.

Randhawa B, Harris S, Boyd LA. The Kinesthetic and Visual Imagery Questionnaire Is a Reliable Tool for Individuals With Parkinson Disease. J Neurol Phys Ther 2010;34:161-

167.

Renard Y, Lotte F, Gibert G, Congedo M, Maby E, Delannoy V, et al. OpenViBE: an open-

source software platform to design, test, and use brain-computer interfaces in real and

virtual environments. Presence 2010;19:35-53.

Royer AS, Doud AJ, Rose ML, He B. EEG control of a virtual helicopter in 3-dimensional

space using intelligent control strategies. IEEE Trans Neural Syst Rehab Eng

2010;18:581-589.

Schröder M, Bogdan M, Hinterberger T, Birbaumer N. Automated EEG feature selection for

brain computer interfaces. In: Proceedings of the 1st International IEEE EMBS

Conference on Neural Engineeging, Capri Island, 2003:626-629.

Schröder M, Lal TN, Hinterberger T, Bogdan M, Hill NJ, Birbaumer N, et al. Robust EEG

channel selection across subjects for brain-computer interfaces. EURASIP JASP 2005

2005:3103-3112.

Schuster C, Hilfiker R, Amft O, Scheidhauer A, Andrews B, Butler J, et al. Best practice for

motor imagery: a systematic literature review on motor imagery training elements in

five different disciplines. BMC Med 2011;9:75. http://www.biomedcentral.com/1741-

7015/9/75.

Schuster C, Lussi A, Wirth B, Ettlin T. Two assessments to evaluate imagery ability:

translation, test-retest reliability and concurrent validity of the German KVIQ and

Imaprax. BMC Med Res Methodol 2012;12:127. http://www.biomedcentral.com/1471-

2288/12/127.

Sellers EW, Vaughan TM, Wolpaw JR. A brain-computer interface for long-term independent

home use. Amyotroph Lateral Scler 2010;11:449-455.

Shenoy P, Krauledat M, Blankertz B, Rao RPN, Müller KR. Towards adaptive classification

for BCI. J Neural Eng 2006;3. doi:10.1088/1741-2560/3/1/R02.

Takeuchi N, Izumi S. Maladaptive plasticity for motor recovery after stroke: mechanisms and

approaches. Neural Plast 2012;2012:359728.

Viola FC, Thorne JD, Bleeck S, Eyles J, Debener S. Uncovering auditory evoked potentials

from cochlear implant users with independent component analysis. Psychophysiology

2011;48:1470-1480.

Viola FC, Thorne JD, Edmonds B, Schneider T, Eichele T, Debener S. Semi-automatic

identification of independent components representing EEG artifact. Clin Neurophysiol

2009;120:868-877.

Wang Y, Hong B, Gao X, Gao S. Phase synchrony measurement in motor cortex for

classifying single-trial EEG during motor imagery. In: Engineering in Medicine and Biology Society 2006 - 28th Annual International Conference of the IEEE, New York,

2006:75-78.

Wang Y, Hong B, Gao X, Gao S. Design of electrode layout for motor imagery based brain–

computer interface. Electron Lett 2007a;43:557-558.

Wang Y, Hong B, Gao X, Gao S. Implementation of a brain-computer interface based on

three states of motor imagery. In: Engineering in Medicine and Biology Society, 2007 -

29th Annual International Conference of the IEEE, Lyon, 2007b:5059-5062.

Wang Y, Wang YT, Jung TP. Translation of EEG Spatial Filters from Resting to Motor Imagery Using Independent Component Analysis. PLoS ONE 2012;7:e37665.

Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. Brain–computer

interfaces for communication and control. Clin Neurophysiol 2002;113:767-791.

Wolpaw JR, McFarland DJ. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc Natl Acad Sci USA 2004;101:17849-17854.