Impact of the baseline temporal selection on the
ERD/ERS analysis for Motor Imagery-based BCI
Sébastien Rimbert, David Trocellier, Fabien Lotte

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Sébastien Rimbert, David Trocellier, Fabien Lotte. Impact of the baseline temporal selection on the
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Impact of the baseline temporal selection on the ERD/ERS analysis forMotor Imagery-based BCI
Sébastien Rimbert1 , David Trocellier1 and Fabien Lotte1
Abstract— Motor Imagery-based Brain-Computer Interfaces(MI-BCIs) are neurotechnologies that exploit the modulationof sensorimotor rhythms over the motor cortices, respectively
known as Event-Related Desynchronization (ERD) and Synchronization (ERS). The interpretation of ERD/ERS is directly
related to the selection of the baseline used to estimate them,and might result in a misleading ERD/ERS visualization. In
fact, in BCI paradigms, if two trials are separated by a few
seconds, taking a baseline close to the end of the previous trial
could result in an over-estimation of the ERD, while taking
a baseline too close to the upcoming trial could result in anunder-estimation of the ERD. This phenomenon may cause afunctional misinterpretation of the ERD/ERS phenomena in
MI-BCI studies. This may also impair BCI performances for
MI vs Rest classification, since such baselines are often used
as resting states. In this paper, we propose to investigate theeffect of several baseline time window selections on ERD/ERS
modulations and BCI performances. Our results show thatconsidering the selected temporal baseline effect is essential
to analyze the modulations of ERD/ERS during MI-BCI use.

I. INTRODUCTION
Motor Imagery-based Brain-Computer Interfaces (MIBCIs) are communication and control systems that enableend-users to interact with a computer by performing motor
imagery tasks only, e.g., by imagining hand movements,
usually measured with electroencephalography (EEG) signals [1]. Indeed, movements and imagination of movements
activate similar neural networks [2], enabling MI-based BCIs
to exploit the modulations of sensorimotor rhythms (SMR)
over the motor cortices [3] for control purposes. Such modulations are respectively known as Event-Related Desynchronization (ERD) and Event-Related Synchronization (ERS),
and are coming from the mu (7-13 Hz) and beta (15-30
Hz) frequency bands [3]. MI-BCIs are very promising neurotechnologies for many applications, particularly to control
assistive technologies, for sport training or even for poststroke motor rehabilitation [1].
Many articles have described the robustness of the ERD
and its relevance to design efficient BCIs. Typically, most
MI-BCI designs are based on the ERD motor pattern generated during MI. Indeed, it is well known that MI generates
a contralateral [3] ERD during the task followed by acontralateral ERS afterwards with respect to the requested
limb, thereby enabling MI to be detected. Motor imagery
generates a gradual decrease in power which starts in the*This work was supported by the ERC with project BrainConquest (grant
ERC-2016-STG-714567)
1 S. Rimbert, D. Trocellier and F. Lotte are with the Inria Center at the
University of Bordeaux / LaBRI (CNRS / Univ. Bordeaux / Bordeaux INP),
33405, Talence, France sebastien.rimbert@inria.fr

preparatory phase of the motor task in the mu and beta
rhythms and reaches a maximum during its execution. In
general, the ERD has a larger amplitude in the mu-band thanin the beta-band, and seems to be modulated according to
the experimental criteria (e.g., type of MI performed [4],level of consciousness [5], type of feedback received [6] oruser’s traits [7]). We believe that a better understanding of
the ERD/ERS modulations during MI-BCI tasks is crucial
for developing effective interfaces.
ERD/ERS modulations are often very well described in
the literature but appearances can be deceptive, particularly
because of the baseline used in ERD estimations. Indeed,
ERD/ERS are computed as a change of band power during
MI with respect to such a baseline without MI, using a ratioof band powers (e.g., [8]). The term ’baseline’ thus usuallyrefers to a resting condition of a few seconds before MIthat would maximize the ERD phase occurring during MI.
Since ERD is a ratio, the estimation of ERD is directly
related to the selection of the baseline used to computeit [3], [9]. Several studies have already shown that the
selection of the nature of the baseline is significant and could
affect the visualization of ERD/ERS [10], [9]. In particular,
Tangwiriyasakul et al., used five different baseline movies
in a motion imagery task and showed that the majority ofthe subjects had significantly higher mu power for particularbaseline conditions, thus resulting in more pronounced mu
ERD [9]. Another study showed that a baseline taken during
a static image seems better (i.e., lead to a larger ERD)
compared to a baseline taken during a period of stillness[10]. While the selection of the nature of the baseline seems
to affect the visualized ERD, the effect of the time at which
the baseline is selected is not well-documented.
There are several possible choices of baselines to visualize
the ERD during a motor task: before each trial, at the
beginning of a run or a session, or by averaging the complete
EEG signal over a session [11]. The most common practicein BCI is to use a baseline taken before each trial, thus
providing a baseline that corresponds to the user’s state atthat moment (with the same level of fatigue, workload orconcentration which are known to affect the EEG signal [12],[11]). However, BCI paradigms are typically designed insuch a way that each trial follows the other very quickly, i.e.,there are only a few seconds between the moment when an
MI is completed and when the next MI begins. Consequently,
the time window for choosing which baseline to use is often
limited and might result in a misleading ERD visualization
due to the ERD amplitude variation associated with specificperiods of the trial [6]. For example, choosing a baseline

Fig. 1: Detailed steps of two MIs trial with the corresponding visual feedback. 4 different baseline time windows wereselected ([-5;-2]s, [-4;-1]s, [-3;-0]s, [-2;+1]s), all of the same length (3 seconds).

close to the end of the previous MI could result in a selectedbaseline occurring during the ERS phase (e.g., which occurs
one to two seconds after the end of the previous MI),thus significantly increasing the probability of visualizing an
ERD, even if there is no absolute power decrease during MI.
Therefore, we believe that the inter-subject variability of
ERD/ERS previously described by numerous studies [13],[4] could be partly explained by the temporal baseline selection used. Such temporal baseline selection would influence
the ERD visualization and understanding but could also
be crucial in terms of BCI classification performances ofresting state versus MI, since the resting state class is often
selected similarly to the baseline. Consequently, a better
understanding of the effect of temporal baseline selection
may also lead to better BCI classification performance. In
this paper, we thus propose to investigate and quantify theeffects of several baseline time window selections on both
ERD/ERS modulations and BCI performances. Our results
show that the choice of the baseline is essential to understandand analyze the ERD/ERS modulations during MI-BCI use.
II. MATERIAL AND METHODS
A. Participants
Our analyses were performed on a data base of 71 righthanded healthy and MI-BCI-naive subjects (35 women;
25.83 ± 9.52 y.o.), coming from two previous experiments[14], [15]. These experiments followed the statements of the
WMA declaration of Helsinki on ethical principles for medical research involving human subjects, and were approved by
Inria ethical committee (COERLE, approval number: 201813). All participants signed an informed consent form.

filtered in a data-driven user-specific frequency band (5-35Hz), then spatially filtered using Common Spatial Patterns(CSP) filters. Finally, the resulting features were classified
using a Linear Discriminant Analysis (LDA) classifier (see[14], [15] for details). The trained MI-BCI was used toprovide online feedback based on right-hand vs. left-hand
MI discrimination during the four subsequent runs.
During the first two runs (calibration phase), users were
provided with a sham feedback, i.e., a blue bar randomly
appearing and varying in length. Such feedback (whether
sham here, or real during the next phase) represents the BCI
classifier decision (left or right hand MI when the bar goesleft or right) and its associated confidence in such decision(i.e., the longer the bar the more confident). During eachrun (see Figure 1), users had to perform 40 trials (20 per
MI-task, presented in a random order), each trial lasted 8s.
At t = -3s, a cross was displayed on the screen. At t = -1s,an acoustic signal announced the appearance of a red arrow,
which appeared one second later (at t = 0s) and remained
displayed for 1.25s. The arrow pointed in the direction of
the task to be performed. Participants were instructed to start
performing the corresponding MI-task as soon as the arrowappeared, and to keep doing so until the cross disappeared.
Finally, from t = 1.25s, a visual feedback was continuously
provided in the shape of a blue bar, the length of which varied
proportionally to the BCI classifier output, i.e., the distanceto the LDA hyperplane (for run 3 to 6) or randomly (forthe first two runs). The feedback was provided for 3.75s andwas updated at 16Hz (1s sliding window). After 8 seconds,
the screen turned black again until the beginning of the next
trial. The participant could then rest for a few seconds: from
1.5s to 3.5s, randomly chosen.

B. Experimental BCI protocol
Each participant completed one MI-BCI session which
comprised six 7-minutes runs during which they had to learn
to perform left and right hand MI tasks with the BCI. The
Graz training protocol was used and was divided into two
steps: first, the training of the system (i.e., calibration phase,collecting data to train the EEG machine learning classifier)and second, the training of the user with online feedback(see Figure 1). The EEG data of the first two runs wereused as calibration data for the MI-BCI machine learningalgorithms. More precisely, EEG signals were first band-pass

C. Electrophysiological recordings
EEG signals were recorded with two g.USBAmp amplifiers (g.tec, Austria), sampled at 512 Hz, from 27 electrodes
around the primary motor, the motor and the somatosensory
cortices (for more details see [7]).
1) Topographic ERD/ERS map: All offline analyses of
ERD/ERS modulations were performed using the EEGLAB
toolbox 22.1 and MATLAB 2016a. The EEG data was divided into 13 second long epochs corresponding to 5 seconds
before the MI-cue (t= 0s; see Figure 1) until 8 seconds after,

for each trial. Then, we have selected 4 different baseline
time windows ([-5;-2]s, [-4;-1]s, [-3;-0]s, [-2;+1]s) all of thesame length (3 seconds) in accordance with our hypotheses
to compute the ERD/ERS modulations (see Figure 1).
Brain topographies allow us to display the possible
changes over different electrodes on the scalp in order tolocalize which part of the brain was involved when thesubject performed the MI. In this article, we computed
the topographic Event-Related Spectral Perturbations (ERSP;
which is equivalent to ERD and ERS) in the mu + beta (830 Hz) band during the left-hand and right-hand MI tasks
for both the calibration and user training phases in thetime window [0;+5]s according to 4 different baseline time
windows ([-5;-2]s, [-4;-1]s, [-3;-0]s, [-2;+1]s (Figure 2).
2) Offline BCI performances: We computed offline BCI
performances for classifying resting state EEG signals froma single hand MI task EEG (either left-hand or right-handMI): rest VS left hand MI and rest VS right hand MIclassification accuracies. All offline analyses of classification
were performed using the MNE and Scikit-learn package
in Python 3.9. These performance metrics were estimatedoffline by first band-pass filtering EEG signals in 8-30Hz
and then extracting 3 second long MI and rest epochs fromeach trial. The MI corresponding to [0.5;3.5]s and the rest
phase is identical to the different baseline time windows
i.e. ([-5;-2]s, [-4;-1]s, [-3;-0]s, [-2;+1]s (Figure 1). For eachbaseline/rest paradigm, a left hand MI vs rest and a right MI
vs rest classifiers were built using CSP spatial filters and an
LDA classifier, as done online for left-hand VS right-hand
MI classification. Such classifiers were also trained on thefirst two calibration runs and tested on the four subsequent
runs to obtain (offline) classification accuracies.

III. RESULTS
A. ERD modulations according to baseline time selection
As expected, and as previously described [7], [16], ourresults showed an activation of the motor cortex with acontralateral ERD, i.e., covering either right and left-handmotor areas depending on the task, during the 5s when the
MI task was performed (Figure 2). This result is in harmony
with the literature: the ERD phase is mainly observed during
the MI task and represents an activation of the motor cortex.
The comparison of ERDs across baselines shows thatthe closer the baseline used is to the preceding trial (e.g.,
Baseline 1 or Baseline 2), i.e. after the end of the previous
MI, the more global the ERD is (more electrodes show an
ERD) and the stronger its amplitude (p < 0.01). This is true
both for right and left-hand MIs, but also for both calibration
and user training phases (Figure 2).

Fig. 3: Boxplots showing the distribution of classification
accuracies (N=71) for the Left-hand MI vs. Rest and Righthand MI vs. Rest classes according to 4 different restingstate times selected ([-5;-2]s, [-4;-1]s, [-3;-0]s, [-2;+1]s).
*corresponds to a p-value <0.05, ** to a p-value <0.01 and*** to a p-value <0.001.
B. BCI performances according to baseline time selection
Fig. 2: Topographic map of ERD/ERS% (grand average,n=71) in the alpha/mu+beta band during the right-hand andleft-hand MIs for both calibration and user training phases.

Regarding classification performances for MI vs Rest forvarious baseline/rest time windows, Figure 3 shows thatthe closer the selected baseline/rest time window to thepreceding trial, i.e. to the end of the previous MI, thehigher the BCI performance. In contrast, if the selected
resting time is close to the MI cue, the BCI performance isworse. BCI performance is significantly better for Baseline 1
compared to Baseline 3 (+4%, p < 0.05) or Baseline 4 (+8%,p < 0.001) for both right-hand MI and left-hand MI tasks.
Interestingly, Baseline 2 is an interesting alternative since it
improves the classification comparing to Baseline 4 (+7%,p < 0.001). Although the use of baseline 4 in a practicalcase is rare since the person has already started to performthe MI, it provides a perspective on the impact of having a
BCI paradigm designed in such a way that the participant
anticipates task performance or does not follow instructions.
IV. DISCUSSION AND CONCLUSION
A. Functional interpretation of the ERD
Our results show that the baseline selected can have an
impact on the interpretation of the ERD. If a baseline isselected closely to the cue of the following trial, it is possiblethat the subject’s concentration and anticipation will decrease
the SMR power, resulting in an under-representation of the
ERD. In contrast, our results suggest that the selection of abaseline close to the end of the preceding MI, probably in the
middle of the ERS phase, results in an over-representation ofa global ERD on many electrodes (Figure 2). This suggeststhat it is necessary to be careful in interpreting results related
to SMR activity, especially because the baseline temporalselection can possibly affect the identification of task-related
SMR modulations. This may be an issue for studies preselecting participants or channels based on ERD modulations
or simply to enable comparisons between different studies.
B. Implications for MI-BCIs
When designing an experiment to measure SMR, it isimportant to know what the ideal baseline measurement isand when it should be presented, which is rarely a priority inthe BCI field. Indeed, BCI paradigms are typically designed
in such a way that each trial follows the other very quickly,i.e., there are only a few seconds between the moment
when an MI task is completed and when the next one
begins. Our result suggested that this situation can make thebaseline choice having a strong impact on the overall results,
both in terms of neurophysiological interpretation and BCI
performance (for rest VS MI classification). This mostly
depends on whether the post-MI ERS from the previous
trial affects the baseline measurement of the current trial.
Thus, we would recommend designing MI-BCI protocols
that either comprise dedicated resting state trials (i.e., with
no MI) or that have an inter-trial duration that is longenough to ensure that baselines taken in that period are
not contaminated by post-MI ERS from the previous trial.
For the later point, since post-movement ERS typically peak
1.5s after the movement end [6], we recommend at least
3s in addition to the baseline duration for the inter-trial
length, e.g., 6s between trials for a 3s long baseline/rest.
For the same reasons, to enable between-studies comparisons
between ERD/ERS, we encourage authors to carefully report

the timing and length of the baselines they use for ERD/ERS
estimation, notably with respect to previous trials.
C. Baseline selection variability
In previous studies [10], [9], the optimal baseline wasfound to be subject-dependent, with no common preferredbaseline for all subjects. Such a time-specific analysis of the
ERD should be performed on our data and could confirm
these inter-subject variability findings. Additional analysis
showed that a majority of subjects has the best BCI performance with either Baseline 1 (for 55% of subjects forleft-hand MI vs rest and for 52% of subjects for right-hand
MI vs rest) or Baseline 2 (for 30% of subjects for left-hand
MI vs rest and for 22% of subjects for right-hand MI vs rest).
However, using a subject specific baseline only improved theclassification performance (+2%) non significantly.
Overall, we hope this study showed the need to carefully
study and report the ERD/ERS baselines for MI-BCI design.
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