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Experimenters Inuence on Mental-Imagery based
Brain-Computer Interface User Training
Léa Pillette, Aline Roc, Bernard N’kaoua, Fabien Lotte
To cite this version:
Léa Pillette, Aline Roc, Bernard N’kaoua, Fabien Lotte. Experimenters Inuence on Mental-Imagery
based Brain-Computer Interface User Training. International Journal of Human-Computer Studies,
Elsevier, 2021, pp.102603. �10.1016/j.ijhcs.2021.102603�. �hal-03142448�
Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
Experimenters’ Influence on Mental-Imagery
based Brain-Computer Interface User Training
Léa Pillette*
Inria, LaBRI (Univ. Bordeaux, CNRS, Bordeaux-INP),
200 av. de la Vieille Tour, 33400 Talence, France
lea.pillette@ensc.fr
Aline Roc*
Inria, LaBRI (Univ. Bordeaux, CNRS, Bordeaux-INP),
200 av. de la Vieille Tour, 33400 Talence, France
aline.roc@inria.fr
Bernard N’Kaoua
Handicap, Activity, Cognition, Health, Inserm / University of Bordeaux,
146 rue Léo Saignat, Bât 1B, étage 2, 33076 Bordeaux cedex, France
bernard.nkaoua@u-bordeaux.fr
Fabien Lotte
Inria, LaBRI (Univ. Bordeaux, CNRS, Bordeaux-INP),
200 av. de la Vieille Tour, 33400 Talence, France
fabien.lotte@inria.fr
∗: co-first authorship. Both authors contributed equally.
Corresponding author: Léa Pillette (lea.pillette@ensc.fr, +33 6 77 96 81 92)
Quantitative data of the manuscript
Word count for the abstract: 222 words. Word count for the text: 8509 words. Character
count for the title: 88 characters including spaces, punctuation and subtitle. Number of
references: 54. Number of tables: 1. Number of figures: 6. Number of appendix: 3.
Funding information
This work was supported by the French National Research Agency (project REBEL, grant
ANR-15-CE23-0013-01) and the European Research Council with the Brain-Conquest project
(grant ERC-2016-STG-714567).
Declarations of interest
None.
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
Abstract
Context
Motor Imagery based Brain-Computer Interfaces (MI-BCIs) enable their users to interact
with digital technologies, e.g., neuroprosthesis, by performing motor imagery tasks only, e.g.,
imagining hand movements, while their brain activity is recorded. To control MI-BCIs, users
must train to control their brain activity. During such training, experimenters have a funda-
mental role, e.g., they motivate participants. However, their influence had never been formally
assessed for MI-BCI user training. In other fields, e.g., social psychology, experimenters’
gender was found to influence experimental outcomes, e.g., behavioural or neurophysiological
measures.
Objective
Our aim was to evaluate if the experimenters’ gender influenced MI-BCI user training out-
comes, i.e., performances and user-experience.
Methods
We performed an experiment involving 6 experimenters (3 women) each training 5 women
and 5 men (60 participants) to perform right versus left hand MI-BCI tasks over one session.
We then studied the training outcomes, i.e., MI-BCI performances and user-experience, ac-
cording to the experimenters’ and subjects’ gender.
Results
A significant interaction between experimenters’ and participants’ gender was found on the
evolution of trial-wise performances. Another interaction was found between participants’
tension and experimenters’ gender on the average performances.
Conclusion
Experimenters’ gender could influence MI-BCI performances depending on participants’
gender and tension.
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
Significance
Experimenters’ influence on MI-BCI user training outcomes should be better controlled, as-
sessed and reported to further benefit from it while preventing any bias.
Keywords
1. Brain-Computer Interfaces
2. Mental imagery
3. User training
4. Experimenter influence
5. Gender
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
1 Introduction
Motor Imagery based Brain-Computer Interfaces (MI-BCIs) enable their users to send com-
mands to external digital devices by performing motor imagery tasks only, e.g., imagining
hands or feet movements, while their brain activity is being recorded [9]. The system has to
estimate the motor imagery task that the users perform from the variations occurring in their
brain activity, often recorded using electroencephalography (EEG).
The MI-BCI technology has promising medical applications. For instance, BCIs based on
motor imagery and motor attempt were used for motor rehabilitation after stroke [1, 52]. They
can also be used to control interfaces of communication [2], which is particularly useful for
patients with limited or complete loss of the functional ability to communicate caused by a
severe loss of voluntary muscular control [2]. MI-BCIs are also used for non-medical applica-
tions. For instance, they represent a new tool to control video-games [20].
1.1 Brain-Computer Interfaces user training
Before being operational, MI-BCIs require that both the computer and the user learn during
dedicated training phases [9]. On the one hand, the computer must learn to recognize the
variations occurring in users’ brain activity while they perform the different mental-imagery
tasks. On the other hand, the users must learn to produce a stable and distinguishable pattern
of brain activity for each of the commands that they wish to send to the computer [27]. Both
the computer training and the user training are highly interdependent but the user and the
computer are generally trained separately, which probably partly explain the lack of reliability
of the system [35].
This current lack of reliability of the system limits the development of MI-BCI applications.
Indeed, 10 to 30% of naive users cannot control MI-BCIs, even after some training [30]. There
are several lines of research aiming at improving the efficiency of MI-BCIs. Most focus on
the improvement of machine learning methods, see, e.g., [17, 23]. A few also focus on the
improvement of the user training. Indeed, it has been shown both theoretically and exper-
imentally that current user training approaches may not allow all users to acquire the skills
necessary to use MI-BCIs [15, 25].
During their training, users train to control a feedback representing what the computer recog-
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
nizes of the mental task that they are performing. A feedback is an information which is pro-
vided to a learner regarding aspects of the performance or understanding of the task/skills to
learn [12]. It is a fundamental component of the MI-BCI training [25]. Several research were
led in order to improve the feedback [37], for instance by using more realistic cues [34, 47].
Users could need specific feedback characteristics depending on their profile [37]. For in-
stance, previous results indicate that “tensed” and “non-autonomous” people (based on the
dimensions of the 16PF5 psychometric questionnaire [7]) are disadvantaged when controlling
BCIs [16]. Interestingly, “non-autonomous” people are persons who rather learn in a social
context [7]. “Tensed” people might also benefit from a reassuring social presence and emo-
tional feedback.
In a previous BCI experiment, we analysed the influence of a learning companion, i.e., a type
of educational agents which can provide a complex form of social presence and emotional
feedback in a controlled environment. During this last experiment, we designed, implemented
and tested the first artificial learning companion dedicated to BCI user training [38]. This
learning companion was called PEANUT for Personalized Emotional Agent for Neurotech-
nology User Training (see Figure 1). In between two trials, PEANUT provided the learn-
ers with social presence and emotional feedback through interventions that were composed
of both spoken sentences and displayed facial expressions. The interventions were selected
based on the current and previous performances of the learner. We found that such learning
companion had a differential impact on the participants’ performances depending on their
autonomy. Also, the presence of a learning companion influenced how the participants felt
about their ability to learn and memorize how to use a BCI, which is a dimension of the user
experience that we assessed. Thus, we found that a learning companion providing a complex
form of social presence and emotional feedback could influence BCI user training outcomes,
i.e., performances and user-experience.
1.2 Role of experimenters
Very little is known regarding the most prevalent and complex source of social presence and
emotional feedback during experiments which originates from the human supervision (e.g.,
experimenter or caregiver). In experimental settings, experimenters present BCIs to the learn-
ers, ensure the smooth progress of the experiment and might also have an influence on users’
5
Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
Figure 1: A participant training to perform mental tasks on the right with PEANUT, the first learning
companion dedicated to MI-BCI user training, on the left.
states. For instance, in a clinical study, Hammer et al. report “we tried to keep the subjects
motivated and attentive by providing non-alcoholic beverages, sweets and fresh air” [11]. It
has been shown that users’ states (e.g., motivation, attention) can influence the accuracy of
MI-BCI classification [11]. However, the influence that experimenters might have on users’
states and BCI training outcomes remains unknown and was not formally investigated. Only
very few studies in clinical BCI-based motor rehabilitation post-stroke acknowledge and ex-
plicit the role of the therapists, without formally assessing their influence [29, 36, 46].
Rosenthal, who was part of the first in social psychology to stress the importance of studying
the influence of experimenters, describes experimenters as “imperfect tools” [44]. Indeed, the
literature from different fields states that experimenters may consciously or unconsciously
affect their results. Experimenters can influence participants’ responses, behaviour and per-
formances via direct and/or indirect interactions [45]. There are several types of possible
experimenter-related influence, one of them being psychosocial factors. Stereotyped peo-
ple tend to behave in a stereotype-consistent way [53]. For example, elderly people tend to
walk more slowly or to have impaired memory performances if they feel stereotyped [53].
The “experimenter demand effect” is another example of experimenter-related influence. It
can occur when participants unconsciously try to fit the appropriate image reflected by the
experimenter’s behaviour and therefore want to please and assist the experimenters in ob-
taining their expected results [44]. These different influences can be modulated through the
experimenters’ own characteristics (e.g., gender, age, ethnicity and professional status) and/or
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
behaviour (e.g., gaze, touch and verbal interactions) [44].
Among experimenters’ characteristics modulating their influence, one of the most prevalent
seems to be the gender. Previous experiments often report a simple effect of the experimenters’
gender or an interaction between experimenters’ and participants’ gender on experimental
outcomes [21, 44, 48]. Indeed, many cultural stereotypes are gender-based. One of which
is that women have weaker math abilities than men. In previous experiments, Spencer et al.
found that depending on women being told that difficult maths tests were respectively gender-
dependent or independent, they did underperform or not compared to men participants [48].
In the neurofeedback field where users are trained to control their brain activity, Wood and
Kober found that experimenters could have a differential impact on neurofeedback training
depending on three parameters : experimenters’ gender, participants’ gender and participants’
level of locus of control in dealing with new technologies [54]. They relate this difference of
performances to psychosocial factors.
An interaction between the experimenter’s and the participant’s gender can also modulate
the experimenter demand effect. For instance, when participants are instructed by an exper-
imenter from the opposite sex, they seem more likely to act in ways that confirm the experi-
menter’s hypothesis [32]. Also, neurophysiological responses associated with defensiveness,
i.e., the aim to avoid being criticised, is associated with greater relative left frontal activation
in the presence of experimenters from the opposite sex compared to experimenters from the
same sex [18]. Thus, an interaction of experimenters’ and participants’ gender can influence
experimental outcomes, including neurological responses measured using EEG [8, 18].
1.3 Research hypotheses
Literature in the field has identified direct factors that affect user learning (e.g., motivation,
attention), although there influence is still understudied. In order to improve BCI reliabil-
ity, it is thus highly relevant to identify, control and manipulate the factors affecting users’
states. Among these many factors (e.g., instructions, feedback or exercise design) our liter-
ature review presented above suggests that the experimental environment may have a major
influence, notably experimenters [42]. Despite the central role that experimenters have in BCI
experimental process and the literature regarding the impact of social presence and emotional
feedback, no studies had yet been led to evaluate their influence on MI-BCI experimental
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
outcomes, i.e., performances and user-training.
Experimenter’s profile includes many aspects such as age or personality. As described in Sec-
tion 1.2, literature from other fields suggests that one of the most prevalent characteristics
modulating experimenters’ influence seems to be the gender. Indeed, experimental outcomes
(including neurological responses) may be significantly influenced by gender-related factors.
Such impact might differ depending on the profile of the participants and experimenters.
Therefore, based on the literature, we formulated the following hypotheses:
•(H1 - MI-BCI performances) MI-BCI performances undergo a gender-related influ-
ence of experimenters, possibly modulated by users’ gender.
•(H2 - User experience) User experience undergo a gender-related influence of experi-
menters, possibly modulated by users’ gender.
•(H3 - Experimenters’ and participants’ profile) These effects are modulated by ex-
perimenters’ and participants’ profile.
The remainder of this paper is organized as follows. In Section 2 -Materials & methods-,
we provide information regarding the implementation of the experimental protocol that en-
abled us to test these hypotheses. Then, in Section 3 -Results- and in Section 4 -Discussion-,
we respectively report and discuss the results from our experiment1. Finally, in Section 5 -
Conclusions and Prospects-, we offer a conclusion on the matter as well as ideas and recom-
mendations for future research.
2 Materials & methods
2.1 Participants
Sixty healthy MI-BCI naïve participants (29 women ; age 19-59, M= 29, SD = 9.32) com-
pleted the study. None of them reported a history of neurological or psychiatric disorder. Six
1Preliminary results regarding the interaction of experimenters’ and participants’ gender
on the evolution of MI-BCI performances were previously published in a short conference
paper presented at the 8th International BCI Conference [43]. Here we present additional and
more complete results regarding potential confounding factors such as motor-related artefacts.
We also present for the first time results related to the participants’ psychological profile that
provide first leads toward a better understanding of this experimenters’ influence. Finally, we
also provide new user-experience related results.
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
experimenters conducted the study (3 women ; age 23-37, M= 29.2, SD = 5.6) among whom
two (1 woman) were experienced in BCI experimentation, having conducted more than 100
hours of EEG-based BCI experiments, and four were beginners (2 women) who were trained
to perform a BCI experiment beforehand. All beginner experimenters were trained in a repro-
ducible way by the experienced experimenters. Each experimenter was randomly assigned to
10 participants (5 women and 5 men) that they had never met before the session. All exper-
imenters had the same ethnicity, i.e., Caucasian white native french, and were asked to wear
their usual work clothing (casual, not extravagant, not sexualized). This choice was made
in order to investigate the potential influence of experimenters in usual BCI experimental
settings.
Our study was conducted in accordance with the relevant guidelines for ethical research ac-
cording to the Declaration of Helsinki. Both participants and experimenters gave informed
consent before participating in the study. In order to avoid biased behaviour, this study was
conducted using a deception strategy, partially masking the purpose of the study. Participants
were told that the study aimed at understanding which factors (unspecified) could influence
BCI outcomes, i.e., performances and/or user experience. Experimenters were aware of the
goal of the study. The study has been reviewed and approved by Inria’s ethics committee, the
COERLE (Approval number: 2018-13).
2.2 Experimental protocol
Each participant completed one session of 2 hours with a MI-BCI. During this session, partic-
ipants were first asked to read and sign the consent form and complete several questionnaires
(see the following Subsection 2.3 -Questionnaires-), which took around 20 min. Once the
EEG cap (see Subsection 2.4 -EEG Recordings & Signal Processing-) was placed on their
head, the participants performed six 7-minutes runs during which they had to learn to per-
form two MI tasks with the BCI, i.e., imagine right or left hand movements (around 60 min,
including breaks between the runs). Finally, the participants were asked to fill the post-session
questionnaires, the EEG cap was uninstalled and a debriefing was made (around 15 min).
The Graz training protocol was used [35]. It is divided into two steps: first, the training of
the system and second, the training of the user. The first two runs were used as calibration in
order to provide to the system examples of EEG patterns associated with each of the MI tasks.
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
During the first two runs, as the classifier was not yet trained to recognize the mental tasks be-
ing performed by the user, it could not provide a consistent feedback. In order to limit biases
with the other runs, e.g., EEG changes due to visual processing differences between runs, the
user was provided with an equivalent sham feedback, i.e., a blue bar randomly appearing and
varying in length. These two steps and their respective runs are visually depicted in Figure 2.
Figure 2: The BCI session included 6 runs divided into two steps: (1) data acquisition to train the
system (2 runs) and (2) user training (4 runs). After Run 2, the classifier is trained on the data acquired
during the two first runs.
During each run, participants had to perform 40 trials (20 per MI-task, presented in a ran-
dom order), each trial lasted 8s. At t = 0s, a cross was displayed on the screen. At t = 2s, an
acoustic signal announced the appearance of a red arrow, which appeared one second later (at
t = 3s) and remained displayed for 1.25s. The arrow pointed in the direction of the task to be
performed, namely left or right to imagine a movement of the left or right hand. Participants
are instructed to start performing the corresponding MI-task as soon as the arrow appeared,
and to keep doing so until the cross disappeared. Finally, from t = 4.25s, a visual feedback
was continuously provided in the shape of a blue bar, the length of which varied according
to the BCI classifier output. Only positive feedback was displayed, i.e., the feedback was
provided only when the instruction matched the recognized task. The feedback was provided
for 3.75s and was updated at 16Hz, using a 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. The timeline of a trial is shown in Figure 3.
Following the recommendations from the literature, the participants were encouraged to per-
form a kinesthetic imagination [31] and to choose their own mental imagery strategies [19],
e.g., imagining waving at someone or playing the piano. Participants were instructed to find a
strategy for each MI task so that the system would display the longest possible feedback bar.
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
Figure 3: Timeline of a trial.
Instructions were written in advance and read by the experimenters so that all the participants
started with the same standardized information. As they would have been in any standard
BCI experiment, the experimenters were free to interact with the participants before, during
and after the experiment, e.g., seating and/or standing. They were in charge of welcoming
participants in the lab, showing them the way to the experimental room, making them sign
the consent form, explaining them what would happen during the whole experiment, setting
up the EEG cap on them, asking them to fill-in various questionnaires, calibrating the BCI
system and making it run for the participants, answering questions that the participants may
have, providing them with water if they required some, removing the cap and debriefing with
the participant at the end of the experiment. Experimenters were only asked not to reveal the
aim of the experiment before its very end.
2.3 Questionnaires
As stated in the introduction, the personality and the cognitive profile of participants and ex-
perimenters can respectively influence BCI performances and the experimenter bias [37].
Therefore, we assessed the personality and the cognitive profile of both the participants and
the experimenters. The 5th edition of the 16 Personality Factors (16PF5), i.e., a validated
psychometric questionnaire to assess different aspects of people’s personality and cognitive
profile was filled by both experimenters and participants [7]. This questionnaire identifies 16
primary factors of personality, including tension and autonomy. Participants also completed a
mental rotation test measuring their spatial abilities [51].
The participants also filled pre and post experiment questionnaires especially developed for
BCI purpose by Hakoun et al. These questionnaires assessed the participants’ states and the
user-experience [3, 13]. Based on validated questionnaires, it determines five dimensions of
the user-state and/or the user-experience. Three dimensions, i.e., the mood, mindfulness and
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
motivational states, were assessed pre and post training. The evolution of the participant’s
states provides an information regarding the user-experience. Two dimensions, i.e., the cog-
nitive load (amount of cognitive process required to control the MI-BCI system) and the sense
of agency (feeling of control of the participant over the feedback provided by the MI-BCI)
assessed the user-experience post-training.
2.4 EEG Recordings & Signal Processing
To record the EEG signals, 27 active scalp electrodes, referenced to the left earlobe, were
used (Fz, FCz, Cz, CPz, Pz, C1, C3, C5, C2, C4, C6, F4, FC2, FC4, FC6, CP2, CP4, CP6, P4,
F3, FC1, FC3, FC5, CP1, CP3, CP5, P3, 10-20 system). The electromyographic (EMG) ac-
tivity of the hands was recorded using two active electrodes located 2.5cm below the skinfold
on each wrists. The electrooculographic (EOG) activity of one eye was recorded using three
active electrodes. Two of them were located below and above the eye and one was located
on the side. They aimed at recording vertical and horizontal movements of the eye. Physio-
logical signals were measured using a g.USBAmp (g.tec, Austria), sampled at 256 Hz, and
processed online using OpenViBE 2.1.0 [41]. To classify the two MI tasks from EEG data,
we used participant-specific spectral and spatial filters. To do so, we used the now standard
algorithms proposed by Blankertz et al. in [5]. More precisely, from the EEG signals recorded
during the calibration runs, we first identified a participant-specific discriminant frequency
band using the heuristic algorithm proposed in [5] (Algorithm 1 of that paper). Roughly, this
algorithm selects the frequency band whose power in the sensorimotor channels maximally
correlates with the class labels. Here we used channels C3 & C4 after spatial filtering with a
Laplacian filter as sensorimotor channels, as recommended in [5]. The algorithm selected a
discriminant frequency band within the interval from 5 Hz to 35 Hz, with 0.5Hz large bins.
Once this discriminant frequency band automatically identified, we filtered EEG signals in
that band using a Butterworth filter of order 5.
Then, still has recommended in [5], we used the Common Spatial Pattern (CSP) algorithm
[40], in order to optimize 3 pairs of spatial filters, still using the data from the two calibration
runs. Such spatially filtered EEG signals should thus have a band power which is maximally
different between the two MI conditions. We then computed the band power of these spatially
filtered signals by squaring the EEG signals, averaging them over a 1 second sliding window
12
Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
(with 1/16th second between consecutive windows), and log-transforming the results. This led
to 6 different features per time window, which were used as input to a Linear Discriminant
Analysis (LDA) classifier [24]. As mentioned above, this LDA was calibrated on the data
from the two calibration runs. These filters and classifier were then applied on the subsequent
runs to provide online feedback. It should be noted that this BCI design and EEG signal pro-
cessing is a rather standard approach, that has been used in numerous previous experiments
by various laboratories, see, e.g., [4, 5, 14].
2.5 Variables, Factors & Statistical analyses
As presented in the introduction, our experiment aimed at testing three different hypothesis.
In the following Subsections we present the variables, factors and statistical analyses used
to test each of these hypotheses. The statistical analyses mostly consist of ANOVAs, that are
considered as robust against the normality assumption. To the best of our knowledge, no other
non parametric test enabled to perform the analysis that we were interested in. Spearman or
Pearson correlations were also obtained depending on the distribution of the data collected
(assessed using Shapiro-Wilk tests).
2.5.1 H1 - MI-BCI performances
To test our first hypothesis (H1), i.e., MI-BCI performances undergo a gender-related influ-
ence of experimenters, possibly modulated by users’ gender, two measures of performance
were used.
The first performance metric we used is the online Trial-wise Accuracy (TAcc). This metric
is computed by first summing the (signed) LDA classifier outputs (distance to the separating
hyperplane) over all epochs (1s long epochs, with 15/16 s overlap between consecutive win-
dows) during the trial feedback period. If this sum sign matched the required trial label, i.e.,
negative for left hand MI and positive for right hand MI, then the trial was considered as cor-
rectly classified, otherwise it was not. The TAcc for each run was estimated as the percentage
of trials considered as correctly classified using this approach. TAcc is the default accuracy
measure provided online in the MI-BCI scenarios of OpenViBE, and the only performance
metric that the experimenters were seeing online. It should be noted that this metric takes into
account the classifier output and is thus also related to the feedback bar length as it is propor-
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
tional to the classifier output. Our participants were instructed to train to obtain not only a
correct classification, but also a feedback bar as long as possible, the TAcc metrics thus take
into account both aspects. Offline, we also computed the Epoch-wise Accuracy (EAcc) as the
percentage of epochs (1s long time windows) from the feedback periods that were correctly
classified. Thus, this metric only considers whether the classification was correct, but not the
feedback bar length as it does not take into account the classifier output. However, it does
reflect how often EEG epochs were correctly classified, and thus how often the subjects re-
ceived correct positive feedback. It is also a rather standard classification performance metrics
in BCI Machine Learning [49], we thus also provide it for reference.
These two measures of MI-BCI performances over the series of 4 user-training runs, i.e.,
“Run”, were then used in two 3-way repeated measures mixed ANOVAs with “ExpGender”,
“ParGender” and “Run” as independent variables and the repeated measures of performance
over the runs, i.e., TAcc or EAcc, as dependent variable. The results are reported in Subsec-
tion 3.1 -H1 - MI-BCI performances-.
2.5.2 H2 - User experience
Second, we wanted to assess the potential impact of the experimenters’ and participants’ gen-
der on the user experience (H2). The user experience is defined by the two percentages pro-
vided by the questionnaire of Hakoun et al. [3, 13] regarding the amount of cognitive load and
sense of agency felt during the training. It is also defined by the evolution of mood, mindful-
ness and motivation of the participants between the beginning and end of the training. This
evolution is assessed by subtracting the measure post training to the measure pre training,
both assessed in percent. The higher the percentages are, the more participants increased their
reported levels of positive emotions and calm, mindfulness, motivation, cognitive load and
sense of agency.
These five measures of user experience were then used in five 2-way ANOVAs or ANCOVAs,
one per dimension, with “ExpGender" and “ParGender” as independent variables and either
the measure of cognitive load, sense of agency, mood, mindfulness or motivation as depen-
dent variable. Performances averaged over all runs, i.e., TAcc or EAcc, were used as covariate
if they were correlated to the dependent variable. The results are reported in Subsection 3.2
-H2 - User experience-.
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
2.5.3 H3 - Experimenters’ and participants’ profile
Finally, we wanted to know if other characteristics of the experimenters’ and/or participants’
profile than the gender could provide first elements of comprehension regarding the potential
difference in MI-BCI performances or user-experience (H3). We focused on characteristics
of the profile that were shown to have an influence on BCI performances in previous studies,
i.e., mental rotation scores (MRS), tension and autonomy [16]. Participants with low MRS
[51], tensed and/or non-autonomous (both measured using the 16PF5 questionnaire [7]) were
shown to have lower BCI performances than the others [16].
The groups formed by experimenters’ and participants’ gender did not have similar MRS and
autonomy. Thus, we assessed the influence of these two measures on the results obtained for
H1 using the same ANOVAs that were used to test the hypothesis (two 3-way repeated mea-
sures mixed ANOVAs with “ExpGender”,“ParGender” and “Run” as independent variables
and the repeated measures of performance over the runs, i.e., TAcc or EAcc, as dependent
variable) and the autonomy, i.e., “Autonomy”, or the mental rotation score, i.e., “MRS”, of
the participants as covariate. The results are reported in Annex B -Details regarding the analy-
ses on the potential influence of MRS and autonomy differences in participant groups-.
Then, we focused on the potential influence of the tension. We separated the participants into
two groups depending on their tension “ParTension”. The threshold between high and low
tension was defined using the median tension score (i.e., median of 6, low and high tension
respectively corresponding to scores of [1, 5] and [6, 10], 10 being the maximum). We per-
formed two 3-way ANOVAs with “ParTension",“ExpGender" and “ParGender” as indepen-
dent variables and one of the measures of performance averaged over all runs, i.e., TAcc or
EAcc, as dependent variable. The results are reported in Subsection 3.3 -H3 - Experimenters’
and participants’ profile-.
3 Results
Among the 60 participants, 1 participant did not complete all of the four runs of participant
training due to a technical issue and 3 outperformed the others (by more than two SDs) both
in term of TAcc (respectively, outliers Ms1 = 98.13, Ms2 = 98.13, Ms3 = 99.38 ; Mgrp = 62.78%,
SDgrp = 16.2) and EAcc (outliers Ms1 = 88.94, Ms2 = 90.36, Ms3 = 94.51 ; Mgrp = 59.33%,
15
Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
SDgrp = 12.3). Thus, the following analyses are based on the results of 56 participants (27
women).
The automatically selected and subject-specific discriminant frequency bands used to classify
the two MI tasks from EEG data were in the range of 16.4 ±7.78 Hz to 19.58 ±7.44 Hz
with an average length of 3.17 ±2.99 Hz (see Subsection 2.4 -EEG Recordings & Signal
Processing-).
Before it all, we verified that groups formed by participants’ gender, i.e., “ParGender”, and
experimenters’ gender, i.e., “ExpGender”, had comparable profiles. To check that groups
were comparable, we ran 2-way ANOVAs with “ExpGender” and ”ParGender” as indepen-
dent variables and either MRS, tension or autonomy as dependent variable.
Results indicate that groups are comparable in terms of tension. Though, participants’ gender
influence their MRS [F(1, 52) = 17.47, p≤10−3,η2= .25]. Men (Mmen = 0.07, SD = 0.02)
had higher MRS than women (Mwomen = 0.05, SD = 0.02), which is in accordance with the
literature [22]. Furthermore, participants training with men or women experimenters did not
have the same level of autonomy [F(1, 52) = 4.01, p= .05, η2= .07]. Participants training
with men experimenters (MmenExp = 6.35, SD = 1.74) were more autonomous than partici-
pants training with women experimenters (MwomenExp = 5.67, SD = 1.66). As the autonomy
and MRS of participants was found to influence their BCI performances [16], we controlled
for the potential influence of these variables in our subsequent analyses (see Appendix B -
Details regarding the analyses on the potential influence of MRS and autonomy differences in
participant groups-).
In the following sections, we report the results of the analyses presented in Section 2.5 that we
performed to test each of our hypotheses.
3.1 H1 - MI-BCI performances
We started by testing the H1 hypothesis, i.e., MI-BCI performances undergo a gender-related
influence of experimenters, possibly modulated by users’ gender. As stated in 2.5.1 -H1 -
MI-BCI performances-, we performed two 3-way repeated measures mixed ANOVAs with
“ExpGender”,“ParGender” and “Run” as independent variables and the repeated measures
of performance over the runs, i.e., TAcc or EAcc, as dependent variable.
First, we performed such ANOVA using the TAcc. After correction of sphericity using the
16
Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
Huynh-Feldt method (= 0.92), the results revealed no simple effect of “Run” [F(2.8, 144) =
1.81, p= .15, η2= .03], “ExpGender” [F(1, 52) = 0.54, p= .47, η2= .01] nor “ParGender”
[F(1, 52) = 0.09, p= .76, η2=.01]. They also revealed no interaction of “Run*ExpGender”
[F(2.8, 144) = 0.08, p= .96, η2=10−2] nor “ParGender*ExpGender” [F(1,52) = 0.60, p=
.44, η2= .01]. Though, the “Run*ParGender” interaction was significant [F(2.8, 144) =
5.98, p= .001, η2= .1]. Figure 4 represents the evolution of the participants’ TAcc depending
on their gender.
Figure 4: TAcc evolution depending on participants’ gender.
A significant “Run*ParGender*ExpGender” interaction was found as well [F(2.8, 144) =
3.46, p= .02, η2= .06]. Figure 5 represents the participants’ TAcc evolution depending on the
experimenters’ and participants’ gender.
Next, we performed this same analysis using the EAcc. After correction of sphericity using
the Huynh-Feldt method (= 0.8), the results revealed no simple effect of “Run” [F(2.4, 125)
= 1.53, p= .22, η2= .03], “ExpGender” [F(1, 52) = 0.26, p= .61, η2≤0.01] and “Par-
Gender” [F(1, 52) = 0.23, p= .64, η2≤0.01]. They revealed no interaction of “Run*Par-
Gender” [F(2.4, 125) = 1.92, p= .14, η2= .04], “Run* ExpGender” [F(2.4, 125) = 0.23, p=
.83, η2=0.01] nor “ParGender*ExpGender” [F(1, 52) = 0.92, p= .34, η2= .02]. Finally, the
interaction of “Run*ParGender*ExpGender” [F(2.4, 125) = 1.38, p= .26, η2= .03] was not
17
Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
Figure 5: TAcc evolution depending on the experimenters’ and participants’ gender.
significant either.
We controlled for the potential influence of the most common artefact sources, i.e., electroocu-
lography (EOG) and electromyography (EMG) [10], on our performances measures, i.e.,
TAcc and EAcc, in additional analyses that are presented in Appendix A -Details regard-
ing the analyses on the potential influence of artefact sources-. These analyses did not reveal
an influence of EOG or EMG artefacts that could have affected the EEG-based BCI perfor-
mances.
We also controlled for the potential influence of MRS and autonomy differences in partic-
ipant groups formed using the participants’ and experimenters’ gender. These analyses are
presented in Appendix B -Details regarding the analyses on the potential influence of MRS
and autonomy differences in participant groups- and did not reveal any potential bias from
MRS and autonomy differences in participant groups.
3.2 H2 - User experience
Then, we tested the H2 hypothesis, i.e., user experience undergo a gender-related influence of
experimenters, possibly modulated by users’ gender. As stated in 2.5.1 -H1 - MI-BCI per-
formances-, we analysed the influence of participants’ and experimenters’ gender on five
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
indicators of user-experience, i.e., mood, mindfulness, motivation, cognitive load and sense
of agency.
First, we checked if the performances had an impact on the reported user-experience mea-
sures. We found that the sense of agency post training was positively correlated to both the
TAcc [Spearman correlation, r(56) = .38, p<10−2] and EAcc [Spearman correlation, r(56) =
.34, p= .01] metrics.
Then, we performed five 2-way ANOVAs or ANCOVAs, one per dimension, with “ExpGen-
der" and “ParGender” as independent variables and either the measure of cognitive load,
sense of agency, mood, mindfulness or motivation as dependent variable. Performances av-
eraged over all runs, i.e., TAcc or EAcc, were used as covariate if they were correlated to the
dependent variable.
We did not find any significant single effect or interaction including the experimenters’ gender
for the cognitive load, sense of agency, mood, mindfulness or motivation (see Appendix C).
We only found a significant influence of “ParGender” [F(1, 52) = 6.23, p= .02, η2= .11]
on the difference of mindfulness post and pre training. Overall, men participants had a
decrease of mindfulness (MmindfulnessMen = -8.33 ±3.01) whereas women participants had
an increase of mindfulness (MmindfulnessWomen = 2.5 ±3.12) over the session.
3.3 H3 - Experimenters’ and participants’ profile
As presented in the introduction, a previous study has shown that participants’ autonomy and
tension both respectively correlate positively and negatively with BCI performances [16]. As
there were differences in autonomy between the participant groups formed by experimenters’
and participants’ gender, we analysed the potential influence of participants’ autonomy in spe-
cific analyses whose results are presented in Appendix B -Details regarding the analyses on
the potential influence of MRS and autonomy differences in participant groups-. These anal-
yses did not reveal any potential influence of the differences in autonomy and MRS on our
results. Thus, in this Section, we only focused on the tension to perform analyses related to
the psychological profile of the participants and experimenters. High tension scores computed
from the 16PF5 questionnaire indicate highly tensed, impatient and frustrated personalities
whereas low scores indicate relaxed, patient and composed personalities.
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
3.3.1 Assessing the influence of participants’ tension
We checked if an influence of participants’ tension could be found in our results by perform-
ing an analysis of correlation between participants’ tension and our measures of performance.
It revealed a negative correlation between participants’ tension and both the TAcc [Spear-
man correlation, r(56) = -.39, p<10−2] and EAcc [Spearman correlation, r(56) = -.29, p
= .03] metrics, which is in accordance with previous results [16].
Therefore, we investigated if the tension could explain the differences of performances’ de-
pending on the participants’ and experimenters’ gender. As stated in 2.5.1 -H1 - MI-BCI per-
formances-, we performed two 3-way ANOVAs with “ParTension",“ExpGender" and “Par-
Gender” as independent variables and one of the measures of performance averaged over all
runs, i.e., TAcc or EAcc, as dependent variable.
When using the TAcc as a measure of performance, we did not find any simple effect of “Exp-
Gender” [F(1, 48) = 1.51, p= .23, η2= .03], nor “ParGender” [F(1, 48) = 1.72, p= .2, η2=
.04]. Though, a trend toward a weak impact of “ParTension” was found [F(1, 48) = 3.8, p=
.06, η2= .07]. No interactions were found for “ExpGender*ParGender” [F(1,48) < 10−3,p=
1, η2<10−3], “ParTension*ParGender” [F(1, 48) = 0.18, p= .67, η2<10−2], “ParTension*Exp-
Gender*ParGender” [F(1, 48) = 0.47, p= .5, η2= .01]. Though a significant and strong
interaction was found between “ParTension*ExpGender” [F(1, 48) = 18.94, p<10−3,η2
= .28].
When using the EAcc as measure of performance we did not find any simple effect of “Exp-
Gender” [F(1, 48) = 1.12, p= .3, η2= .02], nor “ParGender” [F(1, 48) = 2.59, p= .11, η2=
.05]. Though, a weak but significant impact of “ParTension” was found [F(1, 48) = 4.43, p
= .04, η2= .08]. No interactions were found for “ExpGender*ParGender” [F(1, 48) = 0.02,
p= .89, η2<10−3], “ParTension*ParGender” [F(1, 48) = 0.1, p= .75, η2<10−2], “ParTen-
sion*ExpGender* ParGender” [F(1, 48) = 0.72, p= .1, η2= .02]. Though, a significant in-
teraction was found between “ParTension*ExpGender” [F(1, 48) = 21.98, p<10−3,η2=
.31].
Figure 6 represents the average performances of participants with tensed and non-tensed per-
sonalities when taking into account the gender of their experimenters. Non-tensed participants
seem to have higher performances, i.e., TAcc and EAcc, when training with women exper-
imenters while tensed participants seem to have higher performances, i.e., TAcc and EAcc,
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
when training with men experimenters.
Figure 6: Estimated mean performances depending on participants’ tension and experimenters’ gender.
3.3.2 Assessing the influence of experimenters’ tension
Previous results found that a similarity between participants’ and experimenters’ profile could
lead to higher bias in experimental results [44]. As participants’ level of tension had a signif-
icant impact on their results, we analysed the potential influence of the level of tension of our
experimenters. The tension score in the personality of the three men and three women experi-
menters were respectively of [5, 5 and 7] and [3, 4 and 5], indicating a higher level of tension
among men experimenters than among women experimenters. Therefore, we investigated
further to know if the influence of the experimenters’ came from a psychosocial factor related
to their gender or from their level of tension which was higher among men experimenters than
women participants.
We checked if, independently of gender, there was of correlation between the tension of the
experimenter and the performances of the participants. We did not find any correlation of the
experimenters’ tension with the TAcc [Spearman correlation, r(56) = .03, p= .83], nor with
the EAcc [Spearman correlation, r(56) = .11, p= .44].
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
3.4 Summary of the results
Hypothesis Analyses Significant results
H1-
MI-BCI
performances
3-way repeated measures mixed ANOVA
with “ExpGender”, “ParGender” and
“Run” as independent variables and the
repeated measures of TAcc performance
over the runs as dependent variable
“Run*ParGender”
[F(2.8, 144) = 5.98, p= .001, η2= .1]
“Run*ParGender*ExpGender”
[F(2.8, 144) = 3.46, p= .02, η2= .06]
H2-
User experience
2-way ANOVA with “ExpGender*
ParGender” as independent variables and
the measure of mindfulness as dependent
variable
“ParGender”
[F(1, 52) = 6.23, p= .02, η2= .11]
H3-
Experimenters’
and participants’
profile
Spearman correlation
Negative correlation between participants’ tension
and both TAcc
[Spearman correlation, r(56) = -.39, p<10−2]
and EAcc
[Spearman correlation, r(56) = -.29, p= .03]
3-way ANOVA with “ParTension",
“ExpGender",“ParGender” as independent
variables and the TAcc measures of
performance averaged over all runs as
dependent variable
“ParTension*ExpGender”
[F(1, 48) = 18.94, p<10−3,η2= .28]
3-way ANOVA with “ParTension*
ExpGender*ParGender” as independent
variables and the EAcc measures of
performance averaged over all runs as
dependent variable
“ParTension”
[F(1, 48) = 4.43, p= .04, η2= .08]
“ParTension*ExpGender”
[F(1, 48) = 21.98, p<10−3,η2= .31]
Table 1: Summary of the significant results per hypothesis.
4 Discussion
In the following Subsections, we discuss the results obtained for each of our hypothesis.
4.1 H1 - MI-BCI performances
To test the H1 hypothesis, i.e., MI-BCI performances undergo a gender-related influence of
experimenters, possibly modulated by users’ gender, we used two metrics of performances.
The TAcc, which represented what the participants were instructed to improve during train-
ing, and the EAcc, a traditional measure of BCI performances. We did not find a single influ-
ence of the experimenters’ and/or participants’ gender on these performances. Though, we
found a significantly different evolution across runs of the TAcc between men and women
participants (see Figure 4). Women participants seemed to start the training with already good
TAcc, which decreased during the second run and increased again during the last run. Men
participants, however, started with rather low TAcc and then drastically improved during the
second run and then stagnated to reach slightly higher final TAcc than women.
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
In addition, experimenters’ gender seemed to have an influence on this previous interaction.
Indeed, the evolution of the TAcc appears to depend on participants’ and experimenters’ gen-
der (see Figure 5). We found the same tendency for men participants to start with lower TAcc
at the beginning of the session independently of the experimenter’s gender. However, men
seemed to start with drastically lower TAcc when they were training with men experimenters.
They also seemed to have higher TAcc throughout the session when they were training with
women experimenters. Women participants seemed to start with higher TAcc when training
with men experimenters, though their TAcc tended to drop throughout the session. However,
when training with women experimenters, they seemed to have a great increase in TAcc dur-
ing the last run. In social psychology, Nichols and Maner found that participants who are
instructed by an opposite-sex experimenter tend to confirm the experimenter’s expectation
regarding the experimental results [32]. The initial performances (during R3) are consistent
with their findings. However, this does not seem to hold true for the evolution of the partici-
pants’ performances.
Interestingly enough, our results regarding the impact of participants’ and experimenters’
gender do not match those of a recently published neurofeedback study [54]. We do concur
on the fact that an interaction of participants’ and experimenters’ gender influences perfor-
mances. Though, Wood and Kober found that the combination of women participants training
with women experimenters hampered the training outcomes of the participants [54]. They ob-
served no learning effect in this group. The influence of the participants’ tension found in our
results might partly explain this difference of results. In their article, they found a strong and
significant positive correlation between the locus of control in dealing with technology, i.e.,
the level of control that people feel that they have over the control of a technology, and the
performances of women participants training with women experimenters. We did not assess
this trait of our participants, thus the difference in results might also arise from a difference in
the locus of control of our women participants. Even though the locus of control of our par-
ticipants was not assessed, we assessed the sense of agency they felt toward the feedback that
their were provided with during the training. We did not observe any gender influence over
the sense of agency reported by our participants. Overall, our analysis of the user-experience
metrics only revealed an influence of participants’ gender on the evolution of the mindfulness
metric. Men participants tended to have a decrease of mindfulness over the session, when
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
women participants tended to increase their level of mindfulness. Also, Wood and Kober do
not report controlling for the prior acquaintanceship between their participants and experi-
menters [54]. Rosenthal found that this could modulate the bias induced by experimenters
mostly between men experimenters and women participants [44]. Another explanation of the
differences found between our two studies would be that, as stated by Wood and Kober, by
asking their participants to fill a questionnaire regarding their locus of control in dealing with
technology, they might have activated a stereotype bias [54]. Such stereotype was not acti-
vated in our study. Finally, the protocol used by Wood and Kober was a neurofeedback one
aiming at up-regulating the sensorimotor rhythm, and not a two-commands MI-BCI training.
This most possibly also contributes to the differences of results obtained.
Current results do not seem to be biased by the mental rotation scores nor the autonomy of the
participants. Indeed, the same analysis that led us to these conclusions were run with these
variables as covariate. Results do not reveal any impact of these variables, and revealed the
same significant effect as mentioned above. Artefacts potentially arising from eye or hand
movements did not seem to bias of our results either.
4.2 H2 - User experience
Our results did not indicate any influence related to the gender of the experimenter on the par-
ticipants’ user experience. Such influence could have been expected based on previous results
indicating that a social presence and an emotional feedback provided through the use of a
learning companion impacted one dimension of the user experience, i.e., how the participants
felt about their ability to learn and memorize how to use a MI-BCI. Further experiments using
different metrics of the user experience might provide more insight on the potential influence
of experimenters on the user experience.
4.3 H3 - Experimenters’ and participants’ profile
When investigating the influence of the tension of the participants on these results, we found
results that tend to be in accordance with the ones of Jeunet et al. [16]. Participants with tensed
personality trait tend to have lower performances than non-tensed participants. An influence
of participants anxiety was already found in early researches on regulation of alpha [50]. Our
results revealed that the influence of the participants’ tension on MI-BCI performances seems
24
Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
to be modulated by the gender of the experimenter. Tensed and non tensed participants had
better performances when training respectively with men experimenters and women experi-
menters. This result might provide a first lead toward understanding the interaction between
experimenters’ and participants’ influence on MI-BCI performances. We did not find any sig-
nificant influence of experimenters’ tension on participants’ performances. In the future, test-
ing whether a similarity of experimenters’ and participants’ psychological profiles could lead
to higher potential bias in the results would be of interest. In studies on social psychology,
Rosenthal found that participants were more likely to respond to experimenters’ expectancy
when their level of anxiety was similar to their experimenter’s level of anxiety [44]. He hy-
pothesised that a similarity of experimenters’ and participants’ psychological profiles could
lead to higher potential bias in the results. We can make the same hypothesis as Rosenthal
to explain our results as men experimenters in our study had higher scores of tension than
women experimenters [44]. Non-tensed participants might have been more inclined to re-
spond to women experimenters’ expectancy, i.e., to have high MI-BCI performances, who
also tended not to be tensed. Tensed participants, however, might have been more inclined
to respond to men experimenters’ expectancy who also tended to be tensed. The number of
participants did not enable to perform an analysis of both the experimenters’ and participants’
gender and tension at once, as the number of participants per group would have been too low.
Furthermore, experimenters’ level of tension was highly dependent on their gender. Larger
scaled experiments with a greater number of experimenters would provide insight on this
hypothesis.
4.4 Limitations
While this study does provide first insights on the interaction between experimenters’ and par-
ticipants’ gender, future studies are needed to further explore it and explore its unknown long
term influence. Studies with a larger number of experimenters and participants might provide
more information regarding the underlying factors of this gender influence. For instance, it
could confirm or disprove the interaction between experimenters’ gender and participants’
tension. If confirmed, our hypothesis regarding the beneficial similarity between the level of
tension in participants’ and experimenters’ personality could be assessed.
Furthermore, our results might be explained by other factors. Indeed, inter-experimenter vari-
25
Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
ability other than gender (e.g., teaching competence), intra-experimenter variability (e.g.,
appearance and outfit, fatigue, expectations), inter- and intra-participants variability (e.g.,
attractiveness, or motivation) - plus the interaction’s characteristics (e.g., physical proximity,
use of humour, familiarity, verbal and non-verbal communication, quantity of interaction, etc.)
were not analysed. Indeed, many of these variables are very difficult to measure formally
and objectively. Moreover, we were already measuring various aspects of the users and ex-
perimenters personality and states, using validated questionnaires, and the experiment was
already long. Thus, measuring these additional factors would have required to remove some
of the factors actually measured (to keep a reasonable experiment duration), which, according
to the literature, were the one with the most influence, at least theoretically. In summary, our
study shows an interaction between experimenters and participants on the evolution of MI-
BCI performances. This interaction seems related to the experimenters’ and participants’
gender. However, future experiments should confirm and provide more insights regarding this
interaction.
5 Conclusions and Prospects
In this paper, we investigated the presence of an experimenters’ and participants’ gender in-
teraction on MI-BCI training outcomes, i.e., performances and user-experience. We led this
work in response to the fact that previous BCI experiments indicated an influence of social
presence and emotional feedback on BCI user training. Experimenters are the main source of
such presence and feedback during BCI user training. Though, their impact on the MI-BCI
user training outcomes remained unassessed. Also, results from different fields indicate that
an interaction between experimenters’ and participants’ gender is likely to influence experi-
mental outcome. Therefore, we asked 6 experimenters to each train 5 women and 5 men (60
participants in total) to perform right versus left hand motor imagery-BCI control over one
session.
We did find an interaction between experimenters’ and participants’ gender on the evolution
of trial-wise accuracy over a session. Furthermore, participants’ mean performances were
influenced by an interaction of the experimenters’ gender and level of tension in participants’
personality. No single effect or interaction related to the experimenters could be found on the
26
Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
user-experience.
Our results highlight the need for research methods that formally take into account a greater
amount of influencing factors (such as the experimenter) emerging from the experimental pro-
tocol and its context. For instance, the instructions that participants are provided with regard-
ing the strategies they should adopt to perform mental-imagery tasks, are rarely formalized or
mentioned in papers. Furthermore, most published experimental studies do not report taking
into account the potential influence of experimenters. Both the literature and experimental
results indicate that experimenter-related factors might explain part of the between-subject
and/or between-study variability and contribute to the improvement and adaptation of MI-BCI
training.
We argue that in the future the influence of experimenters should be considered carefully
while designing and reporting experimental protocols. Such consideration would benefit many
fields, in particular the Human Computer Interface and the BCI ones. A better understand-
ing of the experimenters’ influence could particularly lead to an improvement of MI-BCIs
as they rely on a long and tedious user training during which experimenters have an impor-
tant role. Other BCIs paradigms, such as P300-based BCIs2, do not have such user training.
However, regardless of the BCI paradigm or even field, during experimental studies assessing
experimenter-unrelated factors and while experimenters’ influence is not well understood,
the bias that can arise from experimenters should be limited and controlled. Double-blind
methods, in which neither the experimenters nor the participants know the group in which
the participant is included, do limit the experimenter related bias. They are already used in
clinical research. It would be worth applying similar methods in non-clinical experiments.
It should be noted that hiring research assistants to perform the experiments might not be
a solution to limit experimenter-related bias. Indeed, it was shown that experimenters can
unconsciously transmit their bias to their research assistants [44]. The literature suggests sev-
eral other solutions to limit and control the potential bias arising from the experimenter [28,
45]. These methods include: monitoring participant-experimenter interaction, increasing the
number and diversity of data collectors, pre-testing the method and controlling expectancy,
providing an extensive training for administrators/ data collectors, monitoring and standard-
2P300-based BCIs rely on the elicitation of a characteristic neurophysiological response,
i.e., the P300, following the presentation of an expected and unpredictable stimulus that the
participants attend to.
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
izing the behaviour of experimenters with detailed protocol and pre-written instructions for
the participant, and statistically controlling for bias. The use of learning companions, such
as PEANUT (see Figure 1) [38], could also limit the experimenters’ role while providing the
important social presence and emotional feedback in a more reproducible form [39].
In conclusion, social presence and emotional feedback are meant to increase the effort, mo-
tivation and engagement of the participants throughout the learning. As any feedback, they
must be carefully studied as they can be double-edged. On the one hand, they can benefit the
learning outcome, depending on the participants’ profile [6, 26, 33, 38]. On the other hand,
as any feedback, they can have a detrimental impact on the user training and the reliability of
experimental results when they are incorrectly designed and assessed [54].
Acknowledgements
We would like to thank all the participants and the experimenters, i.e., Aurélien Appriou,
Camille Benaroch and Damien Caselli, for dedicating the time to conduct some of the experi-
ments.
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Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
References
[1] Biasiucci, A., Leeb, R., Iturrate, I., Perdikis, S., Al-Khodairy, A., Corbet, T., Schnider, A.,
Schmidlin, T., Zhang, H., Bassolino, M., et al. (2018). Brain-actuated functional electrical
stimulation elicits lasting arm motor recovery after stroke. Nature communications, 9(1):1–
13.
[2] Birbaumer, N. (2006). Breaking the silence: brain–computer interfaces (BCI) for
communication and motor control. Psychophysiology, 43(6):517–532.
[3] Bismuth, J., Vialatte, F., and Lefaucheur, J.-P. (2020). Relieving peripheral neuropathic
pain by increasing the power-ratio of low-βover high-βactivities in the central cortical re-
gion with EEG-based neurofeedback: study protocol for a controlled pilot trial (SMRPain
study). Neurophysiologie Clinique.
[4] Blankertz, B., Sannelli, C., Halder, S., Hammer, E. M., Kübler, A., Müller, K.-R.,
Curio, G., and Dickhaus, T. (2010). Neurophysiological predictor of SMR-based BCI
performance. Neuroimage, 51(4):1303–1309.
[5] Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., and Muller, K. (2008). Optimizing
spatial filters for robust EEG single-trial analysis. IEEE Signal processing magazine,
25(1):41–56.
[6] Bonnet, L., Lotte, F., and Lécuyer, A. (2013). Two brains, one game: design and
evaluation of a multiuser BCI video game based on motor imagery. IEEE Transactions
on Computational Intelligence and AI in games, 5(2):185–198.
[7] Cattell, R. and P. Cattell, H. (1995). Personality structure and the new fifth edition of the
16PF. Educational and Psychological Measurement, 55(6):926–937.
[8] Chapman, C., Benedict, C., and Schiöth, H. (2018). Experimenter gender and replicability
in science. Science advances, 4(1):e1701427.
[9] Clerc, M., Bougrain, L., and Lotte, F. (2016). Brain-Computer Interfaces 2: Technology
and Applications. John Wiley & Sons.
29
Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
[10] Fatourechi, M., Bashashati, A., Ward, R. K., and Birch, G. E. (2007). EMG and
EOG artifacts in brain computer interface systems: A survey. Clinical neurophysiology,
118(3):480–494.
[11] Hammer, E. M., Halder, S., Blankertz, B., Sannelli, C., Dickhaus, T., Kleih, S., Müller,
K.-R., and Kübler, A. (2012). Psychological predictors of SMR-BCI performance.
Biological psychology, 89(1):80–86.
[12] Hattie, J. and Timperley, H. (2007). The power of feedback. Review of educational
research, 77(1):81–112.
[13] Jaumard-Hakoun, A., Chikhi, S., Medani, T., Nair, A., Dreyfus, G., and Vialatte, F.-B.
(2017). An apparatus to investigate western opera singing skill learning using performance
and result biofeedback, and measuring its neural correlates. Interspeech.
[14] Jeunet, C., Jahanpour, E., and Lotte, F. (2016a). Why standard brain-computer interface
(BCI) training protocols should be changed: an experimental study. Journal of neural
engineering, 13(3):036024.
[15] Jeunet, C., N’Kaoua, B., and Lotte, F. (2016b). Advances in user-training for mental-
imagery-based BCI control: Psychological and cognitive factors and their neural correlates.
In Progress in brain research, volume 228, pages 3–35. Elsevier.
[16] Jeunet, C., N’Kaoua, B., Subramanian, S., Hachet, M., and Lotte, F. (2015). Predicting
mental imagery-based BCI performance from personality, cognitive profile and neurophys-
iological patterns. PloS one, 10(12):e0143962.
[17] Jin, J., Miao, Y., Daly, I., Zuo, C., Hu, D., and Cichocki, A. (2019). Correlation-based
channel selection and regularized feature optimization for mi-based bci. Neural Networks,
118:262–270.
[18] Kline, J., Blackhart, G., and Joiner, T. (2002). Sex, lie scales, and electrode caps: An
interpersonal context for defensiveness and anterior electroencephalographic asymmetry.
Personality and Individual Differences, 33(3):459–478.
[19] Kober, S. E., Witte, M., Ninaus, M., Neuper, C., and Wood, G. (2013). Learning to
30
Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
modulate one’s own brain activity: the effect of spontaneous mental strategies. Frontiers
in human neuroscience, 7:695.
[20] Lécuyer, A. (2016). BCIs and Video Games: State of the Art with the OpenViBE2
Project. Brain–Computer Interfaces 2: Technology and Applications, pages 85–99.
[21] Levine, F. and De Simone, L. (1991). The effects of experimenter gender on pain report
in male and female subjects. Pain, 44(1):69–72.
[22] Linn, M. and Petersen, A. (1985). Emergence and characterization of sex differences in
spatial ability: A meta-analysis. Child development, pages 1479–1498.
[23] Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., and
Yger, F. (2018). A review of classification algorithms for EEG-based brain–computer
interfaces: a 10 year update. Journal of neural engineering, 15(3):031005.
[24] Lotte, F. and Jeunet, C. (2018). Defining and quantifying users’ mental imagery-based
BCI skills: a first step. Journal of neural engineering, 15(4):046030.
[25] Lotte, F., Larrue, F., and Mühl, C. (2013). Flaws in current human training protocols
for spontaneous brain-computer interfaces: lessons learned from instructional design.
Frontiers in human neuroscience, 7.
[26] Mathiak, K. A., Alawi, E. M., Koush, Y., Dyck, M., Cordes, J., Gaber, T., Zepf, F.,
Palomero-Gallagher, N., Sarkheil, P., Bergert, S., et al. (2015). Social reward improves
the voluntary control over localized brain activity in fMRI-based neurofeedback training.
Frontiers in behavioral neuroscience, 9:136.
[27] McFarland, D. J. and Wolpaw, J. R. (2018). Brain–computer interface use is a skill that
user and system acquire together. PLoS biology, 16(7):e2006719.
[28] Miyazaki, A. and Taylor, K. (2008). Researcher interaction biases and business ethics
research: Respondent reactions to researcher characteristics. Journal of Business Ethics,
81(4):779–795.
[29] Morone, G., Pisotta, I., Pichiorri, F., Kleih, S., Paolucci, S., Molinari, M., Cincotti,
F., Kübler, A., and Mattia, D. (2015). Proof of principle of a brain-computer interface
31
Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
approach to support poststroke arm rehabilitation in hospitalized patients: design, accept-
ability, and usability. Archives of physical medicine and rehabilitation, 96(3):S71–S78.
[30] Neuper, C. and Pfurtscheller, G. (2010). Brain-Computer Interfaces, chapter Neurofeed-
back Training for BCI Control, pages 65–78. The Frontiers Collection.
[31] Neuper, C., Scherer, R., Reiner, M., and Pfurtscheller, G. (2005). Imagery of motor
actions: Differential effects of kinesthetic and visual–motor mode of imagery in single-trial
EEG. Cognitive brain research, 25(3):668–677.
[32] Nichols, A. and Maner, J. (2008). The good-subject effect: Investigating participant
demand characteristics. The Journal of general psychology, 135(2):151–166.
[33] Nijboer, F., Furdea, A., Gunst, I., Mellinger, J., McFarland, D., Birbaumer, N., and
Kübler, A. (2008). An auditory brain–computer interface (BCI). J Neur Meth.
[34] Ono, T., Kimura, A., and Ushiba, J. (2013). Daily training with realistic visual feedback
improves reproducibility of event-related desynchronisation following hand motor imagery.
Clinical Neurophysiology, 124(9):1779–1786.
[35] Pfurtscheller, G. and Neuper, C. (2001). Motor imagery and direct brain-computer
communication. Proceedings of the IEEE, 89(7):1123–1134.
[36] Pichiorri, F., Morone, G., Petti, M., Toppi, J., Pisotta, I., Molinari, M., Paolucci, S.,
Inghilleri, M., Astolfi, L., Cincotti, F., et al. (2015). Brain–computer interface boosts motor
imagery practice during stroke recovery. Annals of neurology, 77(5):851–865.
[37] Pillette, L. (2019). Redefining and Adapting Feedback for Mental-Imagery based
Brain-Computer Interface User Training to the Learners’ Traits and States. PhD thesis,
Université de Bordeaux.
[38] Pillette, L., Jeunet, C., Mansencal, B., N’kambou, R., N’Kaoua, B., and Lotte, F. (2020).
A physical learning companion for Mental-Imagery BCI User Training. International
Journal of Human-Computer Studies, 136:102380.
[39] Pillette, L., Jeunet, C., N’Kambou, R., N’Kaoua, B., and Lotte, F. (2018). Towards
artificial learning companions for mental imagery-based brain-computer interfaces. In
Workshop sur les “Affects, Compagnons Artificiels et Interactions”(ACAI), pages 1–8.
32
Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
[40] Ramoser, H., Muller-Gerking, J., and Pfurtscheller, G. (2000). Optimal spatial filtering
of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation
Engineering, 8(4):441–446.
[41] Renard, Y., Lotte, F., Gibert, G., Congedo, M., Maby, E., Delannoy, V., Bertrand, O., and
Lécuyer, A. (2010). OpenViBE: An open-source software platform to design, test and use
brain-computer interfaces in real and virtual environments. Presence: teleoperators and
virtual environments, 19(1):35–53.
[42] Roc, A., Pillette, L., Mladenovic, J., Benaroch, C., N’Kaoua, B., Jeunet, C., and Lotte,
F. (2020). A review of user training methods in brain computer interfaces based on mental
tasks. Journal of Neural Engineering.
[43] Roc, A., Pillette, L., N’Kaoua, B., and Lotte, F. (2019). Would motor-imagery based
BCI user training benefit from more women experimenters? In 8th International BCI
Conference, pages 1–7.
[44] Rosenthal, R. (1963). On the social psychology of the psychological experiment: 1, 2
the experimenter’s hypothesis as unintended determinant of experimental results. American
Scientist, 51(2):268–283.
[45] Rosnow, R. and Rosenthal, R. (1997). People studying people: Artifacts and ethics in
behavioral research. WH Freeman.
[46] Sexton, C. (2015). The overlooked potential for social factors to improve effectiveness
of brain-computer interfaces. Frontiers in systems neuroscience, 9:70.
[47] Sollfrank, T., Ramsay, A., Perdikis, S., Williamson, J., Murray-Smith, R., Leeb, R.,
Millán, J., and Kübler, A. (2016). The effect of multimodal and enriched feedback on
SMR-BCI performance. Clinical Neurophysiology, 127(1):490–498.
[48] Spencer, S. J., Steele, C. M., and Quinn, D. M. (1999). Stereotype threat and women’s
math performance. Journal of experimental social psychology, 35(1):4–28.
[49] Thomas, E., Dyson, M., and Clerc, M. (2013). An analysis of performance evaluation
for motor-imagery based BCI. Journal of neural engineering, 10(3):031001.
33
Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
[50] Tyson, P. D. (1982). The choice of feedback stimulus can determine the success of alpha
feedback training. Psychophysiology, 19(2):218–230.
[51] Vandenberg, S. G. and Kuse, A. R. (1978). Mental rotations, a group test of three-
dimensional spatial visualization. Perceptual and motor skills, 47(2):599–604.
[52] Vourvopoulos, A. T., Jorge, C., Abreu, R., Figueiredo, P., Fernandes, J.-C., and
Bermúdez i Badia, S. (2019). Efficacy and brain imaging correlates of an immersive motor
imagery bci-driven vr system for upper limb motor rehabilitation: A clinical case report.
Frontiers in Human Neuroscience, 13:244.
[53] Wheeler, S. C. and Petty, R. E. (2001). The effects of stereotype activation on behavior:
a review of possible mechanisms. Psychological bulletin, 127(6):797.
[54] Wood, G. and Kober, S. (2018). EEG Neurofeedback Is Under Strong Control of
Psychosocial Factors. Applied psychophysiology and biofeedback, 43(4):293–300.
34
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Appendix
A Details regarding the analyses on the potential influence of artefact
sources
Because brain signals are really small in amplitude and EEG suffers from very low signal
to noise ratio (SNR), i.e., high vulnerability to artefact sources, we controlled for the most
common artefact sources, i.e., electrooculography (EOG) and electromyography (EMG) [10].
The aim was to check if specific patterns could be found in EOG or EMG signals that could
have affected MI classification by the BCI. The presence of such task-specific patterns could
have confounded the measured MI-BCI performances. We thus wanted to assess how much
EMG or EOG artefacts could have affected the recorded EEG signals and influenced the MI-
BCI classification output and accuracy. To do so, we computed two metrics per source of
potential artefacts.
First, we looked at left vs right MI classification accuracy, i.e., TAcc and EAcc, based on
EOG or EMG signals, using a classifier built on the calibration runs. This was computed us-
ing CSP/LDA calibrated on the EOG or EMG signals only from the two calibration runs,
filtered in the participant-specific discriminant frequency band. Note that we used the same
frequency band as for the online experiment since only task-related EMG and EOG variations
occurring in the same frequency band as the one used by the EEG-BCI classifier could have
affected this classifier output, and therefore the resulting BCI accuracy. The resulting classi-
fier was then applied on the subsequent runs to obtain a measure of EOG or EMG accuracy
per run. The accuracies based on such calibration run can reflect the presence of task-specific
EMG or EOG artefacts in EEG signals, during both the calibration and the training phases,
which might have influenced online EEG-based BCI performances.
Second, the run-specific left vs right MI EOG or EMG accuracies were computed using a
cross-validation method. EOG or EMG data only from each run, filtered in the participant-
specific discriminant frequency band, were divided into five subsets of data. The CSP and
an LDA were successively calibrated on four sets and tested on the remaining one. The run-
specific EOG or EMG metric is the mean classification accuracy obtained for the five subsets
for each of the runs. The run-specific accuracies reflect the presence of task-specific EOG or
35
Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
EMG artefacts that could have affected online EEG-based BCI performance, during each run.
The results of these analyses are presented in the sections below.
A.1 Checking the influence of EMG artefacts
We first assessed whether EMG artefacts, or real unsolicited hand movements from our partic-
ipants, could have had an impact on our main results, i.e., the interactions we found between
the evolution of trial-wise accuracy and experimenters’ and participants’ gender that we ob-
tained with an EEG-based classification accuracy.
We inspected the potential relation between mean EEG-based classification accuracies, i.e.,
TAcc and EAcc, and EMG-based classification accuracies, i.e., calibration runs based and
run specific, by performing analyses of correlation. We did not find any correlation between
the mean calibration runs based EMG accuracy and the mean TAcc [Spearman correlation,
r(54) = -.2, p= .15] nor with the mean EAcc [Spearman correlation, r(52) = -.15, p= .29]. No
correlation could be found either between the mean run specific EMG accuracy and the TAcc
[Spearman correlation, r(53) = -.1, p= .49] nor the EAcc [Spearman correlation, r(51) = -.86,
p= .55].
A.2 Checking the influence of EOG artefacts
Similarly to the previous section, we inspected if EOG artefacts or eye movements performed
by our participants could have had an impact on our main results that we obtained with EEG-
based classification accuracies.
We inspected the potential relation between mean performances, i.e., TAcc and EAcc, and
EOG-based classification accuracies, i.e., calibration runs based and run specific, by perform-
ing analyses of correlation. We did not find any correlation between the mean calibration
runs based EOG accuracy and the mean TAcc [Spearman correlation, r(54) = -.23, p= .11]
nor with the mean EAcc [Spearman correlation, r(52) = -.17, p= .22]. Though, a significant
correlation could be found between the mean run specific EOG accuracy and both the TAcc
[Spearman correlation, r(56) = .31, p= .02] and the EAcc [Spearman correlation, r(54) = .36,
p<10−2].
We hypothesized that these significant correlations resulted from EEG acquisitions from the
electrodes positioned to measure EOG. Indeed, when the same analysis was performed us-
36
Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
ing cross-validation on data filtered on EOG frequency band, i.e., 0.5-4Hz, we did not find
any correlation with the mean TAcc [Spearman correlation, r(54) = .05, p= .73] nor with the
mean EAcc [Spearman correlation, r(52) = .12, p= .39].
B Details regarding the analyses on the potential influence of MRS and
autonomy differences in participant groups
As stated in Section 3 -Results-, the groups of participants formed using the participants’
and experimenters’ gender had differences in terms of mental rotation scores and autonomy.
Therefore, we studied the potential impact of these differences on the results presented in
Section 3.1 -H1 - MI-BCI performances-.
We ran our same main analyses than in this section (two 3-way repeated measures mixed
ANOVAs with “ExpGender”,“ParGender” and “Run” as independent variables and the
repeated measures of performance over the runs, i.e., TAcc or EAcc, as dependent variable)
using the autonomy, i.e., “Autonomy”, or the mental rotation score, i.e., “MRS”, of the par-
ticipants as covariate. When performing the analysis on the TAcc we found no impact of the
autonomy (“Autonomy” [F(1, 51) = 0.26, p= .61, η2<10−2], “Autonomy*Run” [F(2.48,
126.6) = 0.81, p= .47, η2= .02]) nor of the mental rotation score (“MRS” [F(1, 51) = 1.75, p
= .19, η2= .03], “MRS*Run” [F(2.47, 125.79) = 1.52, p= .22, η2= .03]). When investigating
the EAcc we did not find any single effect or interaction of the autonomy (“Autonomy” [F(1,
51) = 0.44, p= .51, η2=10−2], “Autonomy*Run” [F(2.1, 107.14) = 1.46, p= .24, η2= .03])
nor of the mental rotation score (“MRS” [F(1, 51) = 1.05, p= .31, η2= .02], “MRs*Run”
[F(2.18, 111.18) = 1.35, p= .27, η2= .03]) either.
C Details regarding the analyses on the potential influence of experi-
menters’ gender on the user-experience
We analysed the influence of experimenters’ and participants’ gender on the five dimensions
of the user-experience, i.e., mood, mindfulness, motivation, cognitive load and sense of agency.
First, we checked if the performances had an impact on the reported user-experience mea-
sures. We found that both the TAcc [Spearman correlation, r(56) = .38, p<10−2] and EAcc
37
Experimenters’ Influence on MI-BCI User Training, L. Pillette, A. Roc, B. N’Kaoua, F. Lotte
[Spearman correlation, r(56) = .34, p= .01] metrics were positively correlated to the sense of
agency post training.
Therefore, we performed five 2-way ANOVAs or ANCOVAs, one per dimension, with “Exp-
Gender*ParGender” as independent variables and either the measure of cognitive load, sense
of agency, mood, mindfulness or motivation as dependent variable. Performances averaged
over all runs, i.e., TAcc or EAcc, were used as covariate if they were correlated to the depen-
dent variable.
No influence was found on the cognitive load reported post training of “ExpGender” [F(1,
52) = 1.65, p= .2, η2= .03], “ParGender” [F(1, 52) = 2.89, p= .1, η2= .05] nor “ExpGen-
der*ParGender” [F(1, 52) = 0.05, p=0.95, η2<10−3].
No influence was found on the sense of agency of “ExpGender” [F(1, 52) = 0.03, p= .85, η2
=10−3], “ParGender” [F(1, 52) = 0.01, p= .92, η2<10−3] nor “ExpGender* ParGender”
[F(1, 56) = 0.44, p= .51, η2<10−2] using the TAcc as covariable. Neither was there any
influence found with the EAcc as covariable of “ExpGender” [F(1, 56) = 0.08, p= .78, η2
=10−2], “ParGender” [F(1, 52) = 10−3,p= .97, η2<10−3] nor “ExpGender* ParGender”
[F(1, 52) = 0.52, p= .47, η2= .01].
No influence was found on the difference of mood reported post and pre training of “ExpGen-
der” [F(1, 52) = 0.06, p= .81, η2=10−3], “ParGender” [F(1, 52) < 10−2,p= .93, η2<10−3]
nor “ExpGender*ParGender”[F(1, 52) = 0.13, p= .72, η2<10−2].
No influence was found on the difference of mindfulness reported post and pre training of
“ExpGender” [F(1, 52) = 0.04, p= .85, η2=10−3] or “ExpGender*ParGender” [F(1, 52)
= 0.92, p= .34, η2= .02]. Though, a significant impact of “ParGender” [F(1, 52) = 6.23, p
= .02, η2= .11] was found. Overall, men participants had a decrease of mindfulness (Mmind-
fulnessMen = -8.33, SD = 3.01) whereas women participants had an increase (Mmindfulness-
Women = 2.5, SD = 3.12) of mindfulness over the session.
No influence was found on the difference of motivation reported post and pre training of “Exp-
Gender” [F(1, 52) = 0.63, p= .43, η2= .01], “ParGender” [F(1, 52) = 0.78, p= .38, η2=
.02] nor “ExpGender*ParGender” [F(1, 52) = 0.97, p= .33, η2= .02].
38