# Alpha power increases no matter what during a neurofeedback session

Jacob Maaz, Alexandra Dia, Laurent Waroquier, Véronique Paban, Arnaud Rey

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Jacob Maaz, Alexandra Dia, Laurent Waroquier, Véronique Paban, Arnaud Rey. Alpha power increases no matter what during a neurofeedback session. 2025. hal-05312109

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# Alpha power increases no matter what during a neurofeedback session

Jacob Maaz,¹,²,³ Alexandra Dia,¹,² Laurent Waroquier,⁴ Véronique Paban,¹,² and Arnaud Rey¹,³

Author affiliations:

1 Aix-Marseille Université, CNRS, CRPN, 13331 Marseille, France

2 Institute Neuro-Marseille, Aix-Marseille Université, France

3 Institute of Language, Communication and the Brain, Aix-Marseille Université, France

4 Aix-Marseille Université, PSYCLE, 13621 Aix-en-Provence, France

Correspondence to: Jacob Maaz

Centre for Research in Psychology and Neuroscience (CRPN) – UMR 7077

CNRS – Aix-Marseille Université

3, place Victor Hugo – Case D

13331 Marseille Cedex 3 – France

jacob.maaz@univ-amu.fr
**Abstract**

Electroencephalographic neurofeedback is often assumed to provide volitional control over neural oscillations, with the alpha rhythm regarded as a particularly accessible target. Yet, evidence supporting single-session alpha modulation remains controversial, largely because of insufficient controls for non-specific influences such as time-on-task effects. This study examined whether individuals can downregulate alpha parietal-scalp power during a single neurofeedback session while controlling for feedback veracity and targeted modulation direction. Healthy individuals completed three training blocks in which the size of a visual stimulus reflected either their real-time alpha power (Alpha-Down group), alpha power in the opposite direction (Alpha-Up group), or prerecorded trajectories (Sham group), followed by a transfer block without feedback. The frequency of feedback update was varied across training blocks (1, 5, or 10 Hz). Results showed robust within-session increases in alpha power across all groups, independent of targeted direction, feedback veracity, or update frequency. Theta and sensorimotor rhythm bands also demonstrated an independent increase, while beta remained stable. These findings indicate that apparent alpha modulation in single-session protocols reflects spontaneous, non-specific increases in oscillatory activity rather than genuine volitional control. The results generalise previous evidence on alpha upregulation to its attempted downregulation, reinforcing that time-dependent dynamics such as arousal, fatigue or mind wandering may dominate single-session outcomes. This work highlights the need for stringent methodological controls when evaluating the specificity of neurofeedback interventions.

**Keywords:** EEG; Neurofeedback; Non-specific influences; Alpha activity.

**Running title:** NO ALPHA CONTROL
**Introduction**

Electroencephalographic neurofeedback (EEG-NF) is a closed-loop training approach
in which individuals learn to volitionally modulate neural oscillations (Sitaram et al., 2017).
By repeatedly coupling real-time brain activity to an explicit sensory feedback, EEG-NF aims
to shape self-regulation skills that can generalise beyond the training context towards clinical
or behavioural improvements (Ros et al., 2020; Strehl, 2014). Among EEG rhythms, alpha
oscillations (8-12 Hz) have been a central target because of their robust prevalence in the EEG
signal and their established link to perceptual, attentional and memory processes (Jensen, 2024;
Klimesch, 2012; Rogala et al., 2016). As a result, alpha activity is considered to be easily
modulated by EEG-NF, even during single-session protocols (Agnoli et al., 2018; Belinskaia
et al., 2020; Chikhi et al., 2023; Escolano et al., 2014; Ros et al., 2010). Alpha modulation has
been applied to both improve healthy cognitive functioning (Berger & Davelaar, 2018;
Hanslmayr et al., 2005; Naas et al., 2019) and treat several clinical conditions such as anxio-
depressive and post-traumatic stress disorders (Hardt & Kamiya, 1978; Kluetsch et al., 2014;
Peeters et al., 2014).

Despite this conceptual appeal, EEG-NF currently faces critics regarding the specificity
of its modulatory effects on brain, clinical and behavioural outcomes (McGough, 2022;
Thibault & Raz, 2017). One core EEG-NF assumption is that providing real-time feedback
enables participants to achieve active EEG self-regulation which, in turn, is specifically
responsible for targeted clinical or behavioural improvements. However, robust studies have
instead established that multiple non-specific confounds (e.g., general neuroscientific context,
expectancies, motivation, and/or placebo-like effects) drive these improvements during EEG-
NF interventions (Dessy et al., 2018; Neurofeedback Collaborative Group, 2021; Schabus et
al., 2017; Schönenberg et al., 2017, 2021). Furthermore, these diverse non-specific confounds
have been argued to influence brain outcomes as well (Maaz, Paban, et al., 2025a; Witte et al.,
2018). In particular, the alpha band power is known to exhibit positive non-stationarities at multiple timescales (Benwell et al., 2019; Cohen, 2014; Maaz, Paban, et al., 2025b). Notably, these increases in alpha power may be attributed to non-specific phenomena that are influenced by time-on-task in traditional EEG experiments. These include fluctuations in arousal, fatigue and mind wandering occurrence (Arnau et al., 2020; Compton et al., 2019). However, the majority of alpha EEG-NF studies lacks proper controls to address non-specific influences on outcomes (Chiasson et al., 2023; Rogala et al., 2016). As a result, previous alpha upregulation during single EEG-NF sessions might be inadequately attributed to the historical hypothesis of EEG volitional control (e.g., Micoulaud Franchi et al., 2020).

Accordingly, our group recently conducted a randomised sham-controlled study to evaluate the specificity of single-session alpha upregulation (Maaz, Waroquier, et al., 2025). Using a double-blind design, participants were randomly divided into two groups. Both groups completed the same EEG-NF training with identical instructions. During training, the diameter of a circle was updated in real time to provide visual feedback. This update depended either on participant's ongoing alpha power (i.e., Alpha-Up group which aimed alpha upregulation) or on prerecorded alpha trajectories (i.e., so-called Sham procedure, see Sorger et al., 2019). Importantly, alpha power increased throughout the session irrespectively of feedback veracity. Furthermore, subsequent comparisons between the sham group and a complementary passive group (i.e., instructed only to visualise random fluctuations of the same circle size) revealed parallel similar alpha time courses. These results indicate that, under standard single-session settings, apparent alpha upregulation can be explained by general, non-specific drifts that are unrelated to the EEG-NF procedure or the engagement in self-regulation. They call into question prior claims of specific volitional alpha enhancement that did not fully neutralise non-specific confounds such as general time-on-task effects.
Similarly to alpha upregulation, a separate line of work reported that alpha power can be effectively reduced within single EEG-NF sessions (Kluetsch et al., 2014; Nan et al., 2020; Nowlis & Kamiya, 1970; Ros et al., 2010, 2013). Yet, these previous studies were limited by important methodological parameters such as the absence of control group or condition. A sham-controlled group was solely implemented by Ros et al. (2013) and Nan et al. (2020), who effectively concluded on a superior alpha downregulation in the experimental group (vs. sham). Nevertheless, sham-controlled studies are known to be very time- and resources-consuming (e.g., Chikhi et al., 2024). As a result, these studies suffered from very low sample sizes, a limitation that considerably enhances type I error rates (Button et al., 2013). Thus, whether individuals can volitionally down-regulate alpha power still remains to be determined in better powered studies.

Addressing these methodological limitations, the present study rigorously tests whether individuals can volitionally downregulate their alpha power within a single EEG-NF session. The alpha trajectories of one Alpha-Down group were compared to those of independent Alpha-Up and Sham groups from our previous study (Maaz, Waroquier, et al., 2025), enabling a stringent evaluation of specificity against both genuine but opposite-direction feedback and non-contingent, i.e., sham feedback (Sorger et al., 2019), respectively. Additionally, to match prior study design, the frequency of feedback update (either at 1, 5 or 10 Hz) was manipulated within participants across training blocks, thereby evaluating whether feedback update timing affects alpha modulation performance. Following training, the transfer of self-regulation performance was also assessed during without-feedback trials (Enriquez-Geppert et al., 2017; Strehl, 2014). We hypothesised that alpha power should elicit steady increases during both training and transfer phases. Furthermore, this increase should be independent of the frequency of feedback update (Maaz, Paban, et al., 2025a; Maaz, Waroquier, et al., 2025; Maaz, Paban, et al., 2025b), the targeted direction of alpha regulation (i.e., Alpha-Up vs. Alpha-Down) and
the veracity of the feedback (i.e., Alpha-Down vs. Sham; Benwell et al., 2019; Maaz, Waroquier, et al., 2025). To address neurophysiological specificity issues (Ros et al., 2020), we also evaluated each effect on the spectral power of other frequency bands frequently targeted by EEG-NF, i.e., theta (4-8 Hz), sensorimotor rhythm (SMR, 12-15 Hz) and beta (15-30 Hz).

## Method

We combined and analysed EEG data from three groups of participants who underwent very similar experimental conditions. This Method specifically applies to the Alpha-Down group of the present study. The remaining Alpha-Up and Sham groups were part of a previous double-blind sham-controlled study of our group. Further detail about this study can be found in the original paper (Maaz, Waroquier, et al., 2025).

## Participants

The sample size of the present study was determined using a Bayesian a priori power analysis for three-group comparisons in such experimental design. A sample size of 24 per group was judged sufficient to obtain enough statistical power. Thirty healthy young adults were included in the Alpha-Down group ($n = 30$; $M_{age} = 22.22$ years, $SD = 5.01$, age range = 17-38; 21 females; 27 right-handed). Adding the Alpha-Up and Sham groups (Maaz, Waroquier, et al., 2025), this resulted in ninety participants in total.

All participants reported no history of neurological or psychiatric disorders, as well as normal or corrected-to-normal vision. Recruitment was carried out via an university learning platform or internal laboratory channels. Student participants ($n = 41$) received course credits as compensation. Written informed consent was obtained from all participants in accordance with the Declaration of Helsinki. To ensure anonymity, a unique identification code was
assigned to each participant. The study was approved by the French Personal Protection Committee (CPP Sud Méditerranée V, ref. 19.09.12.44636).

Material and neurofeedback implementation

Participants completed an EEG-NF session composed of four blocks. Each block comprised eight 60-second trials. During each trial, a grey circle was presented at the centre of a screen. First, during three training blocks, the circle size was continuously updated as real-time feedback of the participant's alpha band (8-12 Hz) power at Pz (Chikhi et al., 2023). The circle size was inversely proportional to real-time alpha power. To target alpha power downregulation, participants were instructed to maximise the circle size as much as possible, therefore mirroring previous procedure of Alpha-Up and Sham groups (Maaz, Waroquier, et al., 2025). Between the three training blocks, the frequency of feedback update was manipulated at 1, 5 and 10 Hz, reflecting common update frequencies in EEG-NF protocols (Berger & Davelaar, 2018; Boe et al., 2014; Enriquez-Geppert, Huster, Figge, et al., 2014; Enriquez-Geppert, Huster, Scharfenort, et al., 2014; Hsueh et al., 2016; Kober et al., 2015; Salari et al., 2014; Studer et al., 2014; Wei et al., 2017). To counterbalance the block order across subjects, a Latin-square design was employed. All six possible triplets were generated while ensuring each frequency appears in every temporal position (123, 132, 213, 231, 312, 321) and an even distribution across participants. After the training phase, a fourth transfer block evaluated alpha downregulation transfer. In this block, the circle size remained constant (100 pixels) across trials. The aim was to assess participants' ability to self-regulate their EEG activity without the help of the real-time feedback (e.g., Strehl, 2014).

At the end of the session, participants responded to two questions: (i) “During the first three training blocks, to what extent did you feel in control of the variations of the circle?”; and (ii) “It is possible that the feedback which has been presented to you during the training was
actually random. To what extent to you believe that the feedback was random?". Participants answered through a five-point Likert scale (1 – "Not at all" – to 5 – "Completely"). These questions aimed to qualitatively assess participants' feeling of feedback control and belief of having received sham feedback, respectively. Importantly, these measures are recommended for implementation in sham-controlled EEG-NF studies to control for differences in feedback and blinding credibility between groups (Ros et al., 2020).

## Apparatus

The EEG-NF session, EEG data acquisition, as well as its online and offline processing, were conducted in Matlab Release 2023a (Mathworks, Inc.). In particular, EEG was digitalised using [Brainflow library version 5-8-1](https://www Mathworks.com/products/matlab/brainflow.html) with the OpenBCI [Cyton 8-channels board](https://www cyton.com/products/cyton-8-channels-board.html), and OpenBCI [Gold cup](https://www cyton.com/products/cyton-8-channels-board.html) and [Earclip](https://www cyton.com/products/cyton-8-channels-board.html) electrodes. Using [Psychtoolbox-3](https://www psych toolbox.ugent.be/) (Kleiner et al., 2007), feedback was displayed on a flat-screen computer monitor (screen resolution: 1920 × 1080 pixels; screen size: 52.704 × 29.646 cm; refresh rate: 60 Hz). The distance between the monitor and the back of the chair was kept constant (90-100 cm). Statistical analyses and graphical representations were performed in R version 4.3.3 (R Core Team, 2024).

## EEG recording

EEG were digitalised at a sample rate of 250 Hz, in microvolts (μV), in Matlab R2023a. Data acquisition was handled via the laptop's GPU to reduce computation time. EEG signals were recorded from six OpenBCI Gold Cup electrodes positioned according to the 10-20 International System: Fp1, Fpz, Fp2, Fz, Cz, and Pz. Two OpenBCI earclip electrodes were placed on the left and the right earlobes, serving respectively as a reference and as a noise-cancelling ground. Electrode impedance was maintained below 10 kΩ.
## Procedure

Participants were seated in front of a monitor. EEG setup was installed and impedance checked after obtaining written and informed consent. The EEG-NF session was divided into a training (three blocks) and a transfer (one block) phases. For the training phase, the following verbal instructions (in French) were given: "You will complete three blocks of neurofeedback training. Each block is composed of eight one-minute trials. During the eight trials of a block, a circle will be presented at the centre of the screen while continuously growing and decreasing in size. Depending on the block, its size will grow and decrease at different rates. At any given moment, the size of the circle reflects the state of your brainwaves in real-time, which are influenced by your thoughts. By keeping your eyes on the circle, your task is thus to try your best to increase as much as possible the size of the circle thanks to your thoughts. The goal is to find the best mental strategy that effectively renders the circle as big as possible. Over the course of the trials, you will progressively become able to self-modulate your brainwaves by intentionally modifying your thoughts and adopting the best mental strategy." Thus, participants were not informed about the specific EEG-NF target (i.e., downregulate their alpha power at Pz), nor provided with a pre-defined mental strategy. Rather, they were encouraged to find mental strategies correlating with increases in circle size as in common practices (Enriquez-Geppert et al., 2017).

Prior the transfer block, participants were also provided with verbal instructions: "Congrats for completing this training! You will now complete a final block, but without feedback. For another eight one-minute trials, a circle will be presented at the centre of the screen. However, this time, the size of the circle will not be modified. By keeping your eyes on the circle, your task will still be to try your best to self-regulate your brainwaves thanks to your thoughts. The objective is to reproduce the mental strategies you used during the training blocks to increase the circle size. Keep it up, it's almost done!". Finally, after the transfer block,
questions were presented to the participant for qualitative purposes (cf. Material and neurofeedback implementation section).

Throughout the session, participants were instructed to remain as calm and relaxed as possible to avoid introducing artifacts into the EEG signal. They had to initiate each trial by pressing the “Enter” key on a keyboard. Experimenters remained in the same room during the session but stayed out of the participant’s sight.

## EEG online processing

During the trials of the training phase, a real-time time-frequency analysis (moving-window short-FFT) were performed and followed Keil et al. (2022)'s recommendations (see Supplementary Table 1 for the corresponding completed checklist) to obtain the spectral power of the alpha frequency band (8-12 Hz) at Pz. At each step of the analysis, zero-phase filtering was first applied using a 1-20 Hz bandpass filter (4th order IIR Butterworth) with the Matlab *filtfilt* function. Then, to compute spectral power estimates in decibels (dB), a symmetric Hann window of 500 samples (2-second length) was used with the Matlab *pspectrum* function. Alpha spectral power was obtained by averaging these estimates within the 8-12 range.

Between the three training blocks, different frequencies for feedback update were used: 1, 5 or 10 Hz. To match the corresponding frequency, the overlap between two consecutive windows was determined by subtracting to window sample size (500 samples) the division of the EEG frequency sample (250 Hz) by the frequency of feedback update. This respectively resulted in window overlaps of 250 (500 – 250/1), 450 (500 – 250/5) and 475 (500 – 250/10) samples (i.e., 50%, 90% and 95%, respectively).
EEG offline processing

Offline-processing of EEG data was performed according to Keil et al. (2022)
guidelines (see Supplementary Table 2 for the completed checklist). The spectral power of
theta (4-8 Hz), alpha (8-12 Hz), SMR (12-15 Hz), and beta (15-30 Hz) frequency bands was
computed through in-house Matlab scripts and EEGLAB (Delorme & Makeig, 2004).
Neurofeedback studies typically lack of an explicit model for the generation of oscillatory
activity and the 1/f/noise (Enriquez-Geppert et al., 2017). We therefore adopted the narrowband
model implicitly assumed in the field.

First, all channel data was zero-phase filtered (Matlab [filtfilt](https://wwwmatlabcentral.com/matlabcentralmatlabfiltfilt) function) using a 0.5 *Hz* high-pass filter (6<sup>th</sup> order IIR Butterworth) and a 50 *Hz* notch filter (2<sup>nd</sup> order IIR). After importing the filtered data into EEGLAB, an extended Infomax Independent Component Analysis (ICA) was applied for each participant (Delorme et al., 2007). Through visual inspection of the component scalp topography, time series, and power spectra, eye blink and lateral eye movements components were identified and removed from the data. The number of removed components for each participant is presented in Supplementary Table 3. Artifact-corrected data were exported back in Matlab format, and only data from Fz, Cz and Pz electrodes were considered for further analysis.

Data was then analysed in the frequency domain using the Matlab [pspectrum](https://wwwmatlabcentral.com/matlabcentralmatlabpspectrum) function.
This function computes power spectra via FFT and employs Welch's method to enhance
spectral estimates reliability. By default, it segments the signal into as long sections as possible
while ensuring a number of segments as close to (but not exceeding) 8, an overlap of 50%, and
applying a Hamming window before computing the FFT. The averaged power spectra provided
the final spectral estimates. To promote normality in data distribution, power values for each
participant and trial were transformed to dB. Finally, the spectral power of theta (4-8 Hz), alpha
(8-12 Hz), SMR (12-15 Hz) and beta (15-30 Hz) frequency bands was obtained by averaging
the power estimates within the corresponding range.

Experiment of Maaz, Waroquier, et al. (2025)

Maaz, Waroquier, et al. (2025) submitted sixty participants to a double-blind sham-
controlled EEG-NF protocol. Participants were randomly allocated to either an Alpha-Up (n =
30) or a Sham (n = 30) EEG-NF session aiming at enhancing alpha power from parietal
electrode. Both sessions were highly similar to the current Alpha-Down session. The session
was also divided into training and transfer phases. During both phases, participants underwent
through the exact, same procedure, with the same material, and received identical instructions.
During the training phase, the same grey circle was continuously updated either from the
participant's real-time alpha power at Pz (Alpha-Up group) or from alpha trajectories
prerecorded during an independent study (Sham group; Maaz, Paban, et al., 2025). Neither
participants nor experimenters were informed about the veracity of the feedback and both
groups were instructed to learn to maximise the circle size during training. Importantly, unlike
the current Alpha-Down group, the circle size presented to the Alpha-Up group was positively
proportional (vs. negatively) to the participant's alpha power. Therefore, the only difference
between the current Alpha-Down group and the previous Alpha-Up and Sham groups was the
targeted direction of alpha regulation, i.e., down vs. up, respectively. Here, to generalise Maaz,
Waroquier, et al. (2025) previous results to alpha downregulation, we systematically compared
alpha trajectories from the current Alpha-Down group to the previous Alpha-Up and Sham
groups.
# Statistical analyses and hypotheses testing

## EEG data

For statistical analysis, the spectral power of theta, alpha, SMR and beta frequency bands measured at Fz, Cz and Pz electrodes were z-score standardised across participants. This was done independently for each study (i.e., the current Alpha-Down group alone and the previous Alpha-Up and Sham groups together). Alpha power at Fz, Cz and Pz was defined as main outcome of interest. Analyses of remaining variables aimed to assess the neurophysiological specificity of the current EEG-NF session, i.e., its ability to modulate specifically alpha power. For the purpose of statistical analyses, we arbitrarily hypothesised the presence of effects of interest on all variables. For analyses on training blocks, it implies that their spectral power is influenced by: (i) trial repetition, (ii) the frequency of feedback update (5 Hz vs. 1 Hz, and 10 Hz vs. 5 Hz), (iii) the targeted direction of alpha regulation (Alpha-Up vs. Alpha-Down), (iv) the veracity of the feedback (Alpha-Down vs. Sham), and (v) each corresponding interaction effects. When considering the transfer block, their spectral power would be influenced by: (i) trial repetition, (ii) the targeted direction of alpha regulation, (iii) the veracity of the feedback, and (iv) each corresponding interaction effects. The person who performed all statistical analyses (JM) remained blind to group assignments throughout.

Statistical analyses were conducted using Bayesian linear multilevel models implemented via the brms (Bürkner, 2017) and rstan (Stan Development Team, 2024) packages in R. Bayesians methods offer several advantages over traditional frequentist approaches (see Schad et al., 2021, 2023 for extended tutorials). This includes robustness in low-power contexts (Schönbrodt & Wagenmakers, 2018), straightforward implementation and computation of multilevel structures (Gelman et al., 2014), the ability to quantify evidence for both the presence and absence of effects (Dienes & Mclatchie, 2018), and reduced susceptibility to multiple comparison issues (Gelman et al., 2012). Each model treated one of the dependent
variables as continuous and included the maximal random effect structure to account for interindividual variability (Barr et al., 2013). When analysing training blocks, the models included as fixed effects: the Trial number continuous within-subject predictor (i.e., integers from 1 to 8), the Frequency categorical within-subject predictor (i.e., 1 Hz, 5 Hz, and 10 Hz), and one Task categorical between-subject predictor (i.e., Alpha-Up, Alpha-Down and Sham EEG-NF groups), as well as their interactions. The first trial served as reference level for the Trial predictor. Concerning Frequency and Task predictors, we applied repeated contrast matrixes following the guidelines of Schad et al. (2020; respectively presented in Supplementary Tables 4-5). Specifically, assigning a repeated contrast matrix to Task predictor enabled to evaluate both effects of the targeted direction of alpha modulation (Alpha-Up vs. Alpha-Down) and of the veracity of feedback (Alpha-Down vs. Sham). To prevent overfitting and constrain parameter estimates to plausible values, regularising priors of N(0, 1) were used for all model parameters (Schad et al., 2021). The full model equation (in brms syntax) was the following:

$$Power \sim Trial * Frequency * Task + (Trial * Frequency | Subject) \quad (1)$$

To analyse the transfer block, the same procedure was used while solely including the Trial and Task predictors, as well as their interaction as fixed effects. The corresponding model equation was defined as:

$$Power \sim Trial * Task + (Trial | Subject) \quad (2)$$

For all effects, Bayes Factors (BFs) quantified the strength of evidence in favour of one hypothesis over another (Dienes & Mclatchie, 2018). To ensure the stability of BFs, all reported analyses were performed twice (Schad et al., 2023). For each effect, the mean of the two obtained posterior distributions, as well as the largest limits of the 95% Credible Intervals (CrI) are reported. Additionally, we report the mean of the BF10 indicating the strength of evidence
for the alternative hypothesis (presence of an effect) relative to the null (absence of effect).

Following Jeffreys (1939), we interpret *BF*<sub>10</sub> as follows: values above 3 indicate substantial evidence for the alternative hypothesis, values below 1/3 quantify substantial evidence for the null hypothesis, and values between 1/3 and 3 suggest data insensitivity to distinguish between the two hypotheses. When the presence of an effect is supported (i.e., *BF*<sub>10</sub> > 3), the *BF*<sub>10+</sub>, quantifying the amount of evidence for a positive-directional (i.e., one-sided) effect, is also reported. Supplementary Tables 6-9 present the model estimates (in standardised units), corresponding 95% CrI and BFs from all reported analyses.

Feeling of feedback control and feedback credibility

To examine group differences in subjective experience of control and perceived
feedback credibility, we conducted two Bayesian between-subject ANOVAs. The first test
evaluated participants’ ratings of perceived control over the feedback variations, and the second
assessed the belief that the feedback updates were random (corresponding, respectively, to
questions (i) and (ii) in <u>Material and neurofeedback implementation</u>). Both variables were
treated as continuous based on responses to 5-point Likert scale items ranging from 1 – “Not
at all” – to 5 – “Completely”. For each test, we report the estimated group difference along
with the 95% CrI limits. In addition, we report the <i>BF</i><sub>01</sub> which quantifies the strength of
evidence in favour of the null (no group difference) over the alternative hypothesis (presence
of group difference).

Data availability

Supplementary Table 10 presents the checklist of the Consensus on the reporting and
experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf).
All materials, data and analysis codes are available via the Open Science Framework:
https://osf.io/6bn4v/?view_only=d50855dd681349a593a35a6b0c7f10c4.
# Results

## Alpha increases no matter what during training

Fig. 1 shows alpha power evolution at frontal, central and parietal electrodes during training depending on the Task participants was submitted to (Alpha-Down EEG-NF, Alpha-Up EEG-NF, or Sham EEG-NF). Globally, alpha power exhibits an upward trend during training independently of the task in hand. Concerning the trial repetition effect, extreme evidence was quantified in favour of a positive effect on alpha power (Fz: $\beta$ = 0.018, 95% CrI [0.012, 0.024], $BF_{10} > 100$, $BF_{10+} > 100$; Cz: $\beta$ = 0.019, 95% CrI [0.013, 0.025], $BF_{10} > 100$, $BF_{10+} > 100$; Pz: $\beta$ = 0.02, 95% CrI [0.012, 0.027], $BF_{10} > 100$, $BF_{10+} > 100$). Regarding absolute power difference between groups, moderate evidence supported the absence of effect at Fz (Alpha-Up vs. Alpha-Down: $\beta$ = 0.192, 95% CrI [-0.273, 0.662], $BF_{10}$ = 0.328; Alpha-Down vs. Sham: $\beta$ = 0.156, 95% CrI [-0.319, 0.626], $BF_{10}$ = 0.296), while $BF_{10}$ were insensitive towards neither the null nor the alternative hypothesis at Cz (Alpha-Up vs. Alpha-Down: $\beta$ = 0.232, 95% CrI [-0.226, 0.699], $BF_{10}$ = 0.384; Alpha-Down vs. Sham: $\beta$ = 0.224, 95% CrI [-0.234, 0.680], $BF_{10}$ = 0.376) and Pz (Alpha-Up vs. Alpha-Down: $\beta$ = 0.241, 95% CrI [-0.196, 0.678], $BF_{10}$ = 0.396; Alpha-Down vs. Sham: $\beta$ = 0.29, 95% CrI [-0.148, 0.727], $BF_{10}$ = 0.518). Importantly, very strong to extreme evidence was found for the absence of interaction between trial repetition and both the targeted direction of alpha modulation (up vs. down; Fz: $\beta$ = -0.002, 95% CrI [-0.017, 0.012], $BF_{10}$ = 0.008; Cz: $\beta$ = 0.002, 95% CrI [-0.013, 0.018], $BF_{10}$ = 0.008; Pz: $\beta$ = 0.007, 95% CrI [-0.011, 0.026], $BF_{10}$ = 0.012) and feedback veracity (down vs. sham; Fz: $\beta$ = 0.001, 95% CrI [-0.013, 0.016], $BF_{10}$ = 0.007; Cz: $\beta$ = 0.002, 95% CrI [-0.014, 0.017], $BF_{10}$ = 0.008; Pz: $\beta$ = -0.005, 95% CrI [-0.024, 0.013], $BF_{10}$ = 0.011). Finally, regarding the effect of the frequency of feedback update and remaining interactions, we mostly found moderate to extreme evidence supporting no effect on alpha power. The only exception, for which we found insensitive evidence, was the interaction between the frequency of feedback
update (5 Hz vs. 1 Hz) and the targeted direction of alpha modulation (up vs. down) on alpha power at Cz and Pz. All model estimates, 95% CrI and corresponding BFs are reported in Supplementary Table 6. Overall, these results suggest that alpha power tends to increase within an EEG-NF session, yet independently of feedback update frequency, alpha possibility to be trained or the targeted direction of alpha modulation.

<figure><img src="image_2.png" /></figure>

**Figure 1 Alpha power evolution within the 1 Hz training block.** (A) *BF*<sub>10</sub> quantifying evidence in favour of the alternative hypothesis over the null for trial repetition effect (green) and corresponding interactions (blue: targeted direction of alpha modulation; purple: feedback veracity) on alpha (8-12 *Hz*) spectral power at Fz, Cz and Pz during the 1 *Hz* training block. Dashed horizontal lines at *y* = 3 indicate substantial evidence for the presence of an effect, whereas dashed horizontal lines at *y* = 1/3 substantial evidence for no effect. (B) Alpha power
evolution across the trials of the 1 Hz training block depending on electrode position (left: Fz; middle: Cz; right: Pz) and on the Task participants were submitted to (pink red: Alpha-Down EEG-NF; cream: Alpha-Up EEG-NF; dark blue: Sham EEG-NF). Each line point corresponds to EEG power averaged at the group-level. Error bars indicate 95% confidence intervals.

**Alpha non-stationarity is preserved during the transfer phase**

The evolution of alpha power during the transfer block is presented on Fig. 2 in function of participants' group (Alpha-Down, Alpha-Up, Sham). Extreme evidence supported a positive effect of trial repetition on alpha power (Fz: $\beta$ = 0.037, 95% CrI [0.028, 0.047], $BF_{10} > 100$, $BF_{10+} > 100$; Cz: $\beta$ = 0.037, 95% CrI [0.027, 0.047], $BF_{10} > 100$, $BF_{10+} > 100$; Pz: $\beta$ = 0.033, 95% CrI [0.022, 0.043], $BF_{10} > 100$, $BF_{10+} > 100$). Concerning absolute group difference in alpha power between the Alpha-Down and Alpha-up groups, moderate evidence supported the absence of group difference at Fz ($\beta$ = 0.168, 95% CrI [-0.330, 0.664], $BF_{10}$ = 0.32), yet insensitive evidence was quantified at Cz ($\beta$ = 0.261, 95% CrI [-0.233, 0.764], $BF_{10}$ = 0.43) and Pz ($\beta$ = 0.298, 95% CrI [-0.189, 0.788], $BF_{10}$ = 0.52), and similarly when comparing the Alpha-Down and the Sham groups (Fz: $\beta$ = 0.186, 95% CrI [-0.310, 0.686], $BF_{10}$ = 0.332; Cz: $\beta$ = 0.23, 95% CrI [-0.273, 0.725], $BF_{10}$ = 0.387; Pz: $\beta$ = 0.28, 95% CrI [-0.212, 0.765], $BF_{10}$ = 0.473). Finally, strong to very strong evidence was found in favour of the absence of interaction between trial repetition and both the targeted direction of alpha modulation (Fz: $\beta$ = -0.005, 95% CrI [-0.028, 0.018], $BF_{10}$ = 0.013; Cz: $\beta$ = -0.007, 95% CrI [-0.031, 0.018], $BF_{10}$ = 0.014; Pz: $\beta$ = 0.007, 95% CrI [-0.018, 0.033], $BF_{10}$ = 0.015) and the veracity of feedback (Fz: $\beta$ = 0.017, 95% CrI [-0.006, 0.040], $BF_{10}$ = 0.034; Cz: $\beta$ = 0.02, 95% CrI [-0.005, 0.045], $BF_{10}$ = 0.047; Pz: $\beta$ = 0.006, 95% CrI [-0.019, 0.032], $BF_{10}$ = 0.014). The model estimates, corresponding 95% CrI and BFs are reported in Supplementary Table 7. These results suggest that alpha power continues to increase during the transfer phase regardless of the targeted direction of alpha modulation and of the veracity of feedback.
<figure><img src="image_3.png" /></figure>

**Figure 2 Alpha power evolution during the transfer phase.** (A) *BF*<sub>10</sub> quantifying evidence in favour of the alternative hypothesis over the null for trial repetition effect (green) and corresponding interactions (blue: targeted direction of alpha modulation; purple: feedback veracity) on alpha (8-12 Hz) spectral power at Fz, Cz and Pz during the transfer block. Dashed horizontal lines at *y* = 3 indicate substantial evidence for the presence of an effect, whereas dashed horizontal lines at *y* = 1/3 substantial evidence for no effect. (B) Alpha power evolution across the trials of the transfer block depending on electrode position (left: Fz; middle: Cz; right: Pz) and on the Task participants were submitted to (pink red: Alpha-Down EEG-NF; cream: Alpha-Up EEG-NF; dark blue: Sham EEG-NF). Each line point corresponds to EEG power averaged at the group-level. Error bars indicate 95% confidence intervals.
Neurophysiological specificity: side effects on theta and SMR

To evaluate the neurophysiological specificity of the current alpha EEG-NF protocol
(i.e., its ability to specifically down-regulate alpha power solely), we reproduced the above-
mentioned analyses on the spectral power of theta (4-8 Hz), SMR (12-15 Hz) and beta (15-30
Hz) frequency bands. Depending on participants' group (Alpha-Down, Alpha-Up, or Sham),
Fig. 3 displays power evolution within each considered band throughout the EEG-NF session.
For simplicity, all model estimates and corresponding 95% CrI and BFs are reported in
Supplementary Tables 8 and 9, respectively regarding the analyses of the training and the
transfer phases.

Concerning training blocks, we found moderate to very strong evidence for an effect of
the frequency of feedback update (10 Hz vs. 5 Hz with a superiority in power for the 5 Hz
frequency) on theta power (Fz: β = -0.107, 95% CrI [-0.173, -0.040], BF<sub>10</sub> = 4.457, BF<sub>10+</sub> =
0.001; Cz: β = -0.125, 95% CrI [-0.196, -0.055], BF<sub>10</sub> = 12.826, BF<sub>10+</sub> ≈ 0; Pz: β = -0.195, 95%
CrI [-0.294, -0.095], BF<sub>10</sub> = 93.367, BF<sub>10+</sub> ≈ 0). For all remaining considered effects,
insensitive or moderate to extreme evidence for an absence of effect was quantified. This
indicates that visual updating of the feedback at 5 Hz elicits a constant increase in theta power
over time no matter the task in hand.

Regarding the transfer phase, we found strong to extreme evidence for a positive effect
of trial repetition on theta power at all electrodes (Fz: β = 0.03, 95% CrI [0.019, 0.040], BF₁₀
> 100, BF₁₀⁺ > 100; Cz: β = 0.029, 95% CrI [0.018, 0.040], BF₁₀ > 100, BF₁₀⁺ > 100; Pz: β =
0.034, 95% CrI [0.022, 0.047], BF₁₀ > 100, BF₁₀⁺ > 100) and on SMR power at Fz (β = 0.022,
95% CrI [0.012, 0.033], BF₁₀ = 44.844, BF₁₀⁺ > 100) and Cz (β = 0.017, 95% CrI [0.009,
0.025], BF₁₀ = 12.796, BF₁₀⁺ > 100). For all remaining considered effects, either insensitive or
moderate to extreme evidence for no effect was quantified. These results suggest that alpha
neighbouring bands, i.e., theta and SMR, may also exhibits a positive non-stationary over time
no matter the group.

<figure><img src="image_4.png" /></figure>

**Figure 3 Evolution of EEG spectral power throughout a single EEG-NF session.** Theta (4-8 Hz; **A-C**), alpha (8-12 Hz; **D-F**), SMR (12-15 Hz; **G-I**) and beta (15-30 Hz; **J-L**) power evolution over the entire EEG-NF session, including training and transfer phases. Each line point represents the EEG spectral power at one trial. Each was averaged at the group-level in function of the Task participants were submitted to (pink red: Alpha-Down EEG-NF; cream: Alpha-Up EEG-NF; dark blue: Sham EEG-NF). Error bars indicate 95% confidence intervals. Vertical dashed lines mark the first trial of each new block.

**Neurofeedback is perceived similarly between groups**

Finally, we evaluated whether participants underwent through a similar experience of
the feedback in all three groups (Alpha-Down, Alpha-Up and Sham). Fig. 4 presents the
distribution of the strength of participants' feeling of control on feedback variations (Fig. 4A),
and of participants' belief that feedback variations were actually random and not based on their
EEG activity (i.e., sham; Fig. 4B). Bayesian independent ANOVAs quantified moderate
evidence for an absence of group difference in both responses (Feeling of control: $\beta$ = -0.082,
95% CrI [-0.308, 0.137], $BF_{01}$ = 7.370; Belief in random feedback variations: $\beta$ = 0.181, 95%
CrI [-0.055, 0.435], BF<sub>01</sub> = 3.282). Importantly, these suggest that participants experienced similar control on feedback variations and that feedback veracity (i.e., genuine vs. sham) could not have been guessed.

<figure><img src="image_5.png" /></figure>

**Figure 4** Distributions of responses on participants' feeling of control and belief in random feedback between groups. Distribution of ratings (5-point Likert scale) of participants' feeling of control over feedback variations (**A**) and belief in the random (i.e., sham) nature of feedback variations (**B**), depending on whether participants were submitted to an Alpha-Down (pink red), Alpha-Up (cream) or Sham (dark blue) EEG-NF session.

Discussion

This study rigorously evaluated whether alpha power could be downregulated during a single EEG-NF session. One group of healthy individuals was submitted to an EEG-NF training aiming to downregulate their alpha power at parietal electrode. Importantly, to control
for non-specific influences, alpha trajectories from the Alpha-Down group were compared to independent Alpha-Up and Sham groups, respectively submitted to genuine EEG-NF aiming alpha upregulation (vs. current downregulation) and to sham EEG-NF. During training, alpha power showed robust within-session increases at frontal, central, and parietal electrodes. Critically, these upward trajectories occurred irrespective of the targeted regulation direction (Alpha-Down vs. Alpha-Up), the veracity of the feedback (genuine vs. sham), and the frequency of feedback update. Additionally, the same pattern of results was observed during a transfer phase, following EEG-NF training, aiming to evaluate participant self-regulation skills. Theta and SMR bands also exhibited positive drifts, and subjective ratings indicated comparable perceived control and successful blinding across groups. Collectively, these findings argue against volitional alpha self-regulation under single-session EEG-NF settings and instead support a dominant role of non-specific, time-dependent factors.

Historically, EEG modulation through EEG-NF is considered to be underlined by an active self-regulation mechanism (e.g., Micoulaud-Franchi & Fovet, 2018). This mechanism is hypothesised to elicit, through volitional control, significant EEG changes within and between training sessions, as well as between EEG resting-states measured before and after training (Ros et al., 2020). In particular, the alpha band (8-12 Hz) spectral densities have been identified as a privileged and easy-to-modulate EEG outcome (Belinskaia et al., 2020; Chikhi et al., 2023; Escolano et al., 2014; Kluetsch et al., 2014; Ros et al., 2010). Specifically, alpha downregulation has been targeted and achieved in single EEG-NF sessions (Nan et al., 2020; Ros et al., 2013). However, by contrast, this study did not achieve alpha downregulation through a single EEG-NF session. Rather, we observed extreme Bayesian evidence for an increase in alpha power during both training and transfer phases (Fig. 1-2).

These results call for a careful reappraisal of earlier reports of alpha down-regulation in single EEG-NF sessions (Kluetsch et al., 2014; Nan et al., 2020; Ros et al., 2010, 2013).
Crucially, the current contrasting results may be explained by several methodological limitations present in previous designs. For example, Ros et al. (2013) and Kluetsch et al. (2014) interpreted successful alpha downregulation based on comparisons of average power during the entire training session with resting-state baselines taken beforehand. However, such a comparison does not reveal whether alpha decreased as training progressed. The observe decrease could be explained by the simple engagement in the EEG-NF task, which may tend to suppress alpha relative to rest because of attentional demands (Jensen, 2024). More importantly, these previous studies did not systematically include controls for non-specific EEG changes. In particular, sham EEG-NF represents the most common control condition for non-specific influences on EEG-NF outcomes (Sorger et al., 2019). Participants are engaged into a similar genuine EEG-NF procedure, yet they received feedback disconnected from their EEG activity. Sham was implemented twice in alpha downregulation studies (Nan et al., 2020; Ros et al., 2013). In those cases, authors confirmed superiority in alpha modulation for genuine EEG-NF over sham. However, these results were limited by very low sample sizes which are known to inflate the rate of type I errors (Button et al., 2013). Consequently, the possibility to downregulate alpha activity during single EEG-NF sessions remained to be determined by further studies with proper controls and sufficient sample sizes.

In addition to sham, bidirectional control groups have emerged as particularly powerful alternatives (Sorger et al., 2019). While accounting for non-specific influences, participants are trained to either increase or decrease the same EEG feature, evaluating the specificity of the targeted direction of EEG modulation. Building on previous methodological issues, the current study implemented both sham and bidirectional controls by systematically comparing alpha trajectories of the Alpha-Down group with two independent groups from a previous study (Maaz, Waroquier, et al., 2025). Importantly, we reproduced identical material, procedure and instructions from previous settings. The previous double-blind sham-controlled study
investigated the mechanisms of alpha upregulation during a single EEG-NF session. One group underwent an Alpha-Up session aiming alpha upregulation, whereas the other a Sham session. In previous work, alpha showed clear increases in power throughout the session independently of the veracity of feedback (i.e., Alpha-Up vs. Sham). These findings suggested that single-session alpha upregulation was not actually driven by volitional EEG control claimed by previous studies (e.g., Agnoli et al., 2018; Chikhi et al., 2023).

Here, we aimed to extend previous conclusions on alpha power upregulation to its downregulation in single-session designs. Comparing the current Alpha-Down with previous Alpha-Up and Sham groups enabled to robustly evaluate whether alpha downregulation is affected by the targeted direction of alpha modulation (Alpha-Down vs. Alpha-Up) and by feedback veracity (Alpha-Down vs. Sham; Sorger et al., 2019). Critically, during both training and transfer phases, Bayesian evidence strongly supported identical alpha increases between all three groups (Fig. 1-2). The absence of interaction with the targeted direction of alpha modulation and feedback veracity indicates that alpha activity may not be effectively downregulated through EEG-NF, but is rather rising due to non-specific influences. In addition, when evaluating subjective experience of control over feedback and its credibility, results confirmed no difference between all three groups (Fig. 4). Together, these results do not support the historical claim of volitional EEG control (e.g., Strehl, 2014).

Rather, these findings are consistent with prior EEG-NF studies documenting alpha increases without alpha possibility to be modulated (Belinskaia et al., 2020; Chikhi et al., 2024; Dessy et al., 2020; Jiang et al., 2021; Naas et al., 2019). For instance, alpha power increases were similarly present in a sham and a genuine EEG-NF protocols (Naas et al., 2019), during a theta (4-8 Hz) EEG-NF session (Chikhi et al., 2024), and within and between the sessions of a biofeedback protocol targeting heart rate variability (Dessy et al., 2020). In addition, recent evidence demonstrated that alpha similarly increases when participants are exposed to a passive
visualisation task mimicking the environment of an EEG-NF session (Maaz, Paban, et al., 2025a; Maaz, Waroquier, et al., 2025; Maaz, Paban, et al., 2025b). Overall, it seems that alpha power tends to increase over time independently of participant engagement in EEG-NF.

Outside of EEG-NF settings, it is already well-documented that alpha activity is exposed to significant non-stationarities occurring over milliseconds to hours (e.g., Benwell et al., 2019; Cohen, 2014). Namely, irrespective of any experimental manipulation, alpha power tends to increase over time, whereas alpha peak frequency tends to decrease. These non-stationarities can be explained by time-on-tasks documented effects on variables such as fatigue, arousal and mind-wandering, which are known to influence alpha activity (Arnau et al., 2020; Käthner et al., 2014; Maaz, Waroquier, et al., 2025). Thus, the current findings reinforce that, no matter what, alpha power exhibits positive non-stationarities due to general time-on-task effects (see Fig. 3D-F showing alpha rising throughout the experimental session). They enable to generalise previous conclusions on alpha upregulation to its downregulation, and call for caution in interpreting alpha EEG-NF results without proper systematic controls.

Furthermore, we would like to stress the relevance of the observed results during the transfer phase. Typically, transfer phases are recommended to evaluate the assumption that participants, after sufficient training, should become able to self-regulate their EEG activity without the help of the real-time feedback (Ros et al., 2020; Strehl, 2014). However, their implementation remains scarce in existing studies (for exceptions, see Paban et al., 2024; Strehl et al., 2006). Here, alpha power continued to rise during the no-feedback transfer phase irrespectively of participant group (Fig. 2), suggesting that no learned EEG modulation occur during training. Additionally, we manipulated the frequency of feedback update during training (1, 5 or 10 Hz) to evaluate whether feedback update timing would influence learning of alpha modulation. However, as no successful EEG modulation was achieved, we congruently observed no effect (nor corresponding interactions) of feedback update frequency on alpha
power. Thus, all observed results on alpha power converge towards a failure to its volitional
modulation in the present single-session settings.

Another assumption in EEG-NF research is the neurophysiological specificity of
training, i.e., whether the EEG-NF protocol solely modulates the targeted EEG feature (Sorger
et al., 2019). However, again, this assumption has still not been systematically addressed in
previous studies. Here, we reproduced the analyses on alpha on the spectral power of theta,
SMR and beta frequency bands. The results of the training and transfer phases are presented,
respectively, in Supplementary Tables 8-9. During training, we only obtained substantial
evidence for a superior theta power when the feedback was updated at 5 Hz (vs. 10 Hz), which
is probably due to an entrainment effect of the 5 Hz frequency (see Supplementary Fig. 1).
During transfer, we interestingly observed an effect of trial repetition, with the absence of any
corresponding interactions, on theta power at Fz, Cz and Pz, as well as on SMR power at Fz
and Cz (Fig. 3. A-C,G-I). This suggests that the spontaneous increases in alpha power may
extend to its neighbouring theta and SMR bands. These findings are consistent with previous
work documenting spontaneous theta and SMR increases over time in EEG-NF settings (Dessy
et al., 2020; Grosselin et al., 2021; Maaz, Waroquier, et al., 2025; Schabus et al., 2017). The
present multiband analyses therefore suggest that the observed changes may extend to global,
broadband non-specific dynamics rather than band-specific, feedback-driven learning.

More broadly, EEG-NF research mostly focused on establishing effective clinical and
behavioural interventions (Chiasson et al., 2023; Micoulaud-Franchi et al., 2015; Thibault et
al., 2015). To this end, EEG-NF studies implemented single- or multiple-session trainings
targeting the modulation of specific EEG features supposed to underly clinical and behavioural
outcomes. However, despite the fact that EEG modulation remains the first objective of EEG-
NF, EEG outcomes (within-, between-session changes and pre- to post-training changes during
rest) have for long be ignored and implicitly assumed (Thibault & Raz, 2018). As a result, the
core assumption that EEG changes occur through volitional control (i.e., active self-regulation mechanism) has been poorly evaluated (Micoulaud-Franchi & Fovet, 2018). Specifically, the present work rigorously addressed the case of claimed alpha modulation during single EEG-NF sessions (Agnoli et al., 2018; Chikhi et al., 2023; Nan et al., 2020; Ros et al., 2013). The results suggest that neither the up- or downregulation of alpha power occurs through the hypothesised volitional control. Rather, they are congruent with documented spontaneous alpha non-stationarities over time (e.g., Benwell et al., 2019). This emphasises the importance for further research to implement systematic control of non-specific influences on EEG-NF outcomes.

Lastly, as the present study evaluated alpha modulation within a single session, one could argue that its conclusions only apply to within-session EEG changes of alpha EEG-NF. Yet, previous studies also reported non-specific alpha increases between sessions (Dessy et al., 2020; Naas et al., 2019), as well as between resting-state measures (Chikhi et al., 2024; Ros et al., 2013). Similarly, the current increases in theta and SMR power highlight that non-specific broadband changes may occur during EEG-NF. As current recommendations on brain and behavioural outcomes, this study emphasises the necessity to properly assess the specificity of targeted EEG modulation (Ros et al., 2020). Guidelines on the justification and the choice of control conditions for neurofeedback already exist (Sorger et al., 2019). We thus recommend further studies to refer to these guidelines when conceptualising EEG-NF protocols.

## Conclusion

Under matched procedures and rigorous controls, single-session EEG-NF did not confer volitional control over alpha power. Instead, alpha power exhibited robust non-stationary increases over time that were insensitive to feedback veracity, targeted regulation direction, and feedback update frequency. These results, together with prior work, argue that
the within-session modulation of classic EEG rhythms such as the alpha band cannot be
attribute to feedback-specific learning in the absence of well-controlled evidence.

Funding

JM was supported by a doctoral fellowship of the French Ministry of Higher Education, Research, and Innovation. VP was supported by the Fond de dotation JANSSEN HORIZON (grant numbers CNRS SPV/MB/214535). This research was supported by the Convergence Institute ILCB (ANR-16-CONV-0002), the NeuroMarseille Institute and the NeuroSchool PhD program, the Centre for Research in Education Ampiric, and the HEBBIAN ANR project (#ANR-23-CE28-0008). For the purpose of Open Access, a [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/) public copyright licence has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission.

Competing interests

The authors report no competing interests.

Author contributions

**Jacob Maaz:** Conceptualisation; Formal analysis; Investigation; Methodology; Resources; Visualisation; Supervision; Writing – Original draft; Writing – Review and Editing.

**Alexandra Dia:** Conceptualisation; Data curation; Investigation. **Laurent Waroquier:** Conceptualisation; Methodology; Supervision; Writing – Review & Editing. **Véronique Paban:** Conceptualisation; Methodology; Project administration; Resources; Supervision; Writing – Review & Editing.

**Arnaud Rey:** Conceptualisation; Methodology; Project administration; Resources; Supervision; Writing – Review & Editing.
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**Supplementary Material**

**for:**

**Alpha power increases no matter what during a neurofeedback session**

Jacob Maaz,¹,²,³ Alexandra Dia,¹,² Laurent Waroquier,⁴ Véronique Paban,¹,² and Arnaud Rey¹,³

**Author affiliations:**

1 Aix-Marseille Université, CNRS, CRPN, 13331 Marseille, France

2 Institute Neuro-Marseille, Aix-Marseille Université, France

3 Institute of Language Communication and the Brain, Aix-Marseille Université, France

4 Aix-Marseille Université, PSYCLE, 13621 Aix-en-Provence, France

**Correspondence to:** Jacob Maaz

Centre for Research in Psychology and Neuroscience (CRPN) – UMR 7077

CNRS – Aix-Marseille Université

3, place Victor Hugo – Case D

13331 Marseille Cedex 3 – France
jacob.maaz@univ-amu.fr
<table><caption>**Supplementary Table 1 Checklist guidelines for time-frequency analyses (adapted from Keil et al., 2022).**</caption><thead><tr><th>#</th><th>Information to be included in the manuscript</th><th>Completed?</th></tr></thead><tbody><tr><td>1</td><td>The specific stage of processing in which time-frequency analysis was applied (e.g., single-trials, after trial averaging, etc.). This clarifies which aspect(s) of oscillatory activity (e.g., spontaneous and/or induced, evoked, etc.) are being observed. If averaged potentials of each trial were subtracted prior to conducting time-frequency analyses on single trials, this step should be stated along with figures depicting the averaged potential in both time and frequency domains.</td><td>YES</td></tr><tr><td>2</td><td>For authors using Fourier-based time-frequency analyses (spectrograms), the following recommendations are provided for each specific approach: (1) If using spectrograms, or moving-window DFT/FFT analyses, report the specific window size and step size. Additional within-window averaging achieved via algorithms (e.g., Welch periodogram method) should also be reported. (2) If using multitaper analyses, the type of tapering windows used, total number used, their center frequencies, whether any smoothing factors are applied, and the specific algorithms used to form their shapes should be reported. (3) If using complex demodulation, the frequencies examined, and the specific properties of the low-pass filter used (i.e., filter type, order, and cutoff frequency) should be reported.</td><td>YES</td></tr><tr><td>3</td><td>For authors conducting time-frequency analyses based on time domain filtering methods (i.e., Filter-Hilbert or similar approaches), the software and version number of the Hilbert transform used to identify the phase-shifted version of the empirical signal. In addition, authors should state the specific properties of band-pass filter (i.e., filter types, order, and cutoff frequencies).</td><td>Not applicable</td></tr><tr><td>4</td><td>If using wavelet-based methods for time-frequency analyses, include the smoothing/ smearing for the minimum and maximum frequency of interest and indicate the maximal temporal and frequency smoothing for a specific wavelet family. In addition, using Morlet wavelets, include the Morlet parameter (m) indicating the trade-off between time and frequency smoothing and smoothing values in the time (σt) and frequency (σf) domains.</td><td>Not applicable</td></tr><tr><td>5</td><td>As for frequency domain analyses, specify the duration of analytical time segments used, with pre- and post-event onset duration. In addition, include the number of time segments for each condition/group.</td><td>YES</td></tr><tr><td>6</td><td>Descriptions of any nonlinear transformations and/or baseline adjustment that were used prior to statistical analyses, accompanied by a rationale for these decisions. Specifically, include the duration used as a baseline and the type of algorithm (e.g., division, subtraction, etc.) used for this adjustment.</td><td>Not applicable</td></tr></tbody></table>
<table><caption>Supplementary Table 2 Checklist guidelines for spectral analyses (adapted from Keil et al., 2022).</caption><thead><tr><th>#</th><th>Information to be included in the manuscript</th><th>Completed?</th></tr></thead><tbody><tr><td>1</td><td>Specifying the inputs and outputs of all algorithms used in the processing pipeline</td><td>YES</td></tr><tr><td>2</td><td>A discussion of how oscillatory activity was conceptualised relative to 1/f noise and/or other broadband phenomena (underlying model)</td><td>YES</td></tr><tr><td>3</td><td>A rationale for the choice of measurement of power in a specific frequency band, including how nonperiodic (1/f) contributions to the spectrum were addressed</td><td>YES</td></tr><tr><td>4</td><td>A statement describing the specific type of Fourier- or non-Fourier-based algorithm used for transformation from the time domain to the frequency domain</td><td>YES</td></tr><tr><td>5</td><td>The exact duration of time segment used for transformation into the frequency domain for each condition of interest. In addition, the total number of segments (e.g., trials per condition) entering an averaged spectrum, along with how data epochs were combined within and across recordings (e.g., overlapping windows)</td><td>YES</td></tr><tr><td>6</td><td>The type, total number of, overlap between, and duration of any taper window functions, along with their ramp-on and ramp-off duration. If alternative and/or additional steps were taken to address edge artifacts, these should be stated. If applicable, the choice of taper window function should be specified as being guided by computational principles and/or by aiming to replicate current methods (e.g., Hann or Hamming window)</td><td>YES</td></tr><tr><td>7</td><td>If zero-padding is applied, the number and location of added zeros (e.g., before the time series, after the time series, or both before and after the time series)</td><td>Not applicable</td></tr><tr><td>8</td><td>All normalisation steps (e.g., by length of time, multiplication of the lower half of the spectrum, or by complex conjugate, etc.) applied to the spectral power or power density calculation</td><td>YES</td></tr><tr><td>9</td><td>The native frequency resolution of the spectrum (e.g., 1/(epoch duration in seconds)). In addition, the number of frequency bins extracted for a specific band of interest, and the range of these bins (e.g., 7.98 Hz to 11.97 Hz)</td><td>YES</td></tr><tr><td>10</td><td>Whether analyses were conducted using single trials or the average across trials</td><td>YES</td></tr><tr><td>11</td><td>How band power was measured from a spectrum</td><td>YES</td></tr></tbody></table>
<table><caption>Supplementary Table 3 Number of Independent Components removed from the data of each participant in the Alpha-Down group.</caption><thead><tr><td>Participant's number</td><td>Number of Independent Components removed</td></tr></thead><tbody><tr><td>1</td><td>2</td></tr><tr><td>2</td><td>3</td></tr><tr><td>3</td><td>2</td></tr><tr><td>4</td><td>3</td></tr><tr><td>5</td><td>3</td></tr><tr><td>6</td><td>2</td></tr><tr><td>7</td><td>2</td></tr><tr><td>8</td><td>2</td></tr><tr><td>9</td><td>2</td></tr><tr><td>10</td><td>2</td></tr><tr><td>11</td><td>2</td></tr><tr><td>12</td><td>3</td></tr><tr><td>13</td><td>3</td></tr><tr><td>14</td><td>2</td></tr><tr><td>15</td><td>3</td></tr><tr><td>16</td><td>3</td></tr><tr><td>17</td><td>3</td></tr><tr><td>18</td><td>2</td></tr><tr><td>19</td><td>3</td></tr><tr><td>20</td><td>3</td></tr><tr><td>21</td><td>3</td></tr><tr><td>22</td><td>2</td></tr><tr><td>23</td><td>2</td></tr><tr><td>24</td><td>3</td></tr><tr><td>25</td><td>3</td></tr><tr><td>26</td><td>2</td></tr><tr><td>27</td><td>2</td></tr><tr><td>28</td><td>3</td></tr><tr><td>29</td><td>2</td></tr><tr><td>30</td><td>2</td></tr></tbody></table>

All components were identified using the EEGLAB extended Infomax Independent Component Analysis (ICA) algorithm (Delorme et al., 2007). Independent Components for eye blinks and lateral eye movements were identified for rejection and subtracted from the data by visual inspection of the component scalp topography, time series, and power spectrum distributions. Note that, unusually, three components were removed from the data of 14 participants. This was done because the ICA algorithm split one of the two typical eye artifact components (one for eye blink and one for lateral eye movements) into two different components. For example, ICA on participant 4's data resulted in a duplication of the typical eye blink component. This high-number of participants with three removed components is probably explained by an unusual data lost in frontal electrodes (Fp1, Fpz and/or Fp2) due material deterioration.
<table><caption>**Supplementary Table 4** Repeated contrast matrix assigned to the Frequency predictor of each model computed on training blocks.</caption><thead><tr><th>Condition labels</th><th>Intercept</th><th>5 Hz vs. 1 Hz<br>(1<sup>st</sup> contrast)</th><th>10 Hz vs. 5 Hz<br>(2<sup>nd</sup> contrast)</th></tr></thead><tbody><tr><td>1 Hz</td><td>1</td><td>-2/3</td><td>-1/3</td></tr><tr><td>5 Hz</td><td>1</td><td>1/3</td><td>-1/3</td></tr><tr><td>10 Hz</td><td>1</td><td>1/3</td><td>2/3</td></tr></tbody></table>

Between the three training blocks, we manipulated the Frequency of feedback update at 1, 5 or 10 Hz. To relate to our hypotheses testing, we applied the present repeated-contrast matrix to the categorical predictor 'Frequency'. This matrix was obtained by applying the generalised inverse to a *Hypothesis* matrix referring to our hypotheses (Schad et al., 2020). The '5 Hz vs. 1 Hz' (1st contrast) column relates to the hypothesis that there is a difference in spectral power when participants are presented a feedback updated at 5 Hz relative to 1 Hz. The '10 Hz vs. 5 Hz' (2nd contrast) column relates to the hypothesis that there is a difference in spectral power when participants are presented a feedback updated at 10 Hz relative to 5 Hz.

<table><caption>**Supplementary Table 5** Repeated contrast matrix assigned to the Task predictor of each model.</caption><thead><tr><th>Level labels</th><th>Intercept</th><th>Alpha-up vs.<br>Alpha-Down<br>(1<sup>st</sup> contrast)</th><th>Alpha-Down vs.<br>Sham<br>(2<sup>nd</sup> contrast)</th></tr></thead><tbody><tr><td>Alpha-Up</td><td>1</td><td>-2/3</td><td>-1/3</td></tr><tr><td>Alpha-Down</td><td>1</td><td>1/3</td><td>-1/3</td></tr><tr><td>Sham</td><td>1</td><td>1/3</td><td>2/3</td></tr></tbody></table>

Here, we evaluated the effects of both the targeted direction of alpha modulation (i.e., up vs. down) and the veracity of the feedback (i.e., genuine vs. sham). To this end, we systematically compared the current Alpha-Down group to an Alpha-Up group and to a Sham group, respectively. These two groups were considered from a previous independent double-blind sham-controlled study (Maaz, Waroquier, et al., 2025). To relate to hypotheses testing for both effects, we applied the present matrix to the 'Task' predictor of all models. This matrix was obtained by applying the generalised inverse to the *Hypothesis* matrix referring to the null hypotheses of corresponding effects (Schad et al., 2020). The column of the first contrast ('Alpha-up vs. Alpha-Down') relates to the hypothesis of a difference in spectral power when participants are engaged in the up- vs. the downregulation of alpha power. The column of the second contrast ('Alpha-Down vs. Sham') relates to the hypothesis on a difference in spectral power when submitting participants to a genuine compared to a sham session.
<table><caption>Supplementary Table 6 Estimates from models computed on alpha spectral power during training blocks.</caption><thead><tr><th>Electrode</th><th>Parameter</th><th>Estimate</th><th>Lower</th><th>Upper</th><th>BF<sub>10</sub></th><th>BF<sub>10+</sub></th></tr></thead><tbody><tr><td>Fz</td><td><strong>Trial</strong></td><td><strong>0,018</strong></td><td><strong>0,012</strong></td><td><strong>0,024</strong></td><td><strong>&gt; 100</strong></td><td><strong>&gt; 100</strong></td></tr><tr><td>Fz</td><td>Frequency - 5 Hz vs. 1 Hz</td><td>0,005</td><td>-0,068</td><td>0,078</td><td>0.037</td><td>1.26</td></tr><tr><td>Fz</td><td>Frequency - 10 Hz vs. 5 Hz</td><td>0,005</td><td>-0,062</td><td>0,071</td><td>0.034</td><td>1.242</td></tr><tr><td>Fz</td><td>Task - Up vs. Down</td><td>0,192</td><td>-0,273</td><td>0,662</td><td>0.328</td><td>3.776</td></tr><tr><td>Fz</td><td>Task - Down vs. Sham</td><td>0,156</td><td>-0,319</td><td>0,626</td><td>0.296</td><td>2.906</td></tr><tr><td>Fz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz</td><td>-0,003</td><td>-0,015</td><td>0,009</td><td>0.007</td><td>0.45</td></tr><tr><td>Fz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz</td><td>0,009</td><td>-0,003</td><td>0,021</td><td>0.017</td><td>12.61</td></tr><tr><td>Fz</td><td>Trial:Task - Up vs. Down</td><td>-0,002</td><td>-0,017</td><td>0,012</td><td>0.008</td><td>0.598</td></tr><tr><td>Fz</td><td>Trial:Task - Down vs. Sham</td><td>0,001</td><td>-0,013</td><td>0,016</td><td>0.007</td><td>1.325</td></tr><tr><td>Fz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>-0,12</td><td>-0,297</td><td>0,057</td><td>0.218</td><td>0.1</td></tr><tr><td>Fz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>-0,077</td><td>-0,238</td><td>0,084</td><td>0.126</td><td>0.209</td></tr><tr><td>Fz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>0,122</td><td>-0,056</td><td>0,299</td><td>0.231</td><td>10.413</td></tr><tr><td>Fz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>0,011</td><td>-0,15</td><td>0,173</td><td>0.082</td><td>1.239</td></tr><tr><td>Fz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>0,008</td><td>-0,021</td><td>0,037</td><td>0.017</td><td>2.396</td></tr><tr><td>Fz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>0,023</td><td>-0,006</td><td>0,052</td><td>0.048</td><td>15.269</td></tr><tr><td>Fz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>-0,004</td><td>-0,033</td><td>0,025</td><td>0.015</td><td>0.625</td></tr><tr><td>Fz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>-0,025</td><td>-0,054</td><td>0,004</td><td>0.06</td><td>0.05</td></tr><tr><td>Cz</td><td><strong>Trial</strong></td><td><strong>0,019</strong></td><td><strong>0,013</strong></td><td><strong>0,025</strong></td><td><strong>&gt; 100</strong></td><td><strong>&gt; 100</strong></td></tr><tr><td>Cz</td><td>Frequency - 5 Hz vs. 1 Hz</td><td>0,005</td><td>-0,066</td><td>0,078</td><td>0.036</td><td>1.266</td></tr><tr><td>Cz</td><td>Frequency - 10 Hz vs. 5 Hz</td><td>-0,014</td><td>-0,088</td><td>0,06</td><td>0.04</td><td>0.548</td></tr><tr><td>Cz</td><td>Task - Up vs. Down</td><td>0,232</td><td>-0,226</td><td>0,699</td><td>0.384</td><td>5.301</td></tr><tr><td>Cz</td><td>Task - Down vs. Sham</td><td>0,224</td><td>-0,234</td><td>0,68</td><td>0.376</td><td>5.009</td></tr><tr><td>Cz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz</td><td>-0,002</td><td>-0,014</td><td>0,009</td><td>0.006</td><td>0.494</td></tr><tr><td>Cz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz</td><td>0,007</td><td>-0,006</td><td>0,02</td><td>0.012</td><td>6.331</td></tr><tr><td>Cz</td><td>Trial:Task - Up vs. Down</td><td>0,002</td><td>-0,013</td><td>0,018</td><td>0.008</td><td>1.603</td></tr><tr><td>Cz</td><td>Trial:Task - Down vs. Sham</td><td>0,002</td><td>-0,014</td><td>0,017</td><td>0.008</td><td>1.363</td></tr><tr><td>Cz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>-0,168</td><td>-0,344</td><td>0,006</td><td>0.53</td><td>0.03</td></tr><tr><td>Cz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>-0,08</td><td>-0,262</td><td>0,101</td><td>0.135</td><td>0.238</td></tr><tr><td>Cz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>0,114</td><td>-0,061</td><td>0,29</td><td>0.204</td><td>8.953</td></tr><tr><td>Cz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>-0,005</td><td>-0,187</td><td>0,176</td><td>0.091</td><td>0.92</td></tr><tr><td>Cz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>0,013</td><td>-0,015</td><td>0,04</td><td>0.021</td><td>4.514</td></tr><tr><td>Cz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>0,014</td><td>-0,017</td><td>0,046</td><td>0.024</td><td>4.403</td></tr><tr><td>Cz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>-0,007</td><td>-0,035</td><td>0,02</td><td>0.016</td><td>0.424</td></tr><tr><td>Cz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>-0,02</td><td>-0,052</td><td>0,011</td><td>0.036</td><td>0.11</td></tr><tr><td>Pz</td><td><strong>Trial</strong></td><td><strong>0,02</strong></td><td><strong>0,012</strong></td><td><strong>0,027</strong></td><td><strong>&gt; 100</strong></td><td><strong>&gt; 100</strong></td></tr></tbody></table>
<table><tbody><tr><td>Pz</td><td>Frequency - 5 Hz vs. 1 Hz</td><td>-0,053</td><td>-0,148</td><td>0,041</td><td>0.09</td><td>0.154</td></tr><tr><td>Pz</td><td>Frequency - 10 Hz vs. 5 Hz</td><td>-0,071</td><td>-0,149</td><td>0,007</td><td>0.198</td><td>0.038</td></tr><tr><td>Pz</td><td>Task - Up vs. Down</td><td>0,241</td><td>-0,196</td><td>0,678</td><td>0.396</td><td>6.338</td></tr><tr><td>Pz</td><td>Task - Down vs. Sham</td><td>0,29</td><td>-0,148</td><td>0,727</td><td>0.518</td><td>9.486</td></tr><tr><td>Pz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz</td><td>0,003</td><td>-0,012</td><td>0,019</td><td>0.009</td><td>1.887</td></tr><tr><td>Pz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz</td><td>0,012</td><td>-0,002</td><td>0,026</td><td>0.03</td><td>20.262</td></tr><tr><td>Pz</td><td>Trial:Task - Up vs. Down</td><td>0,007</td><td>-0,011</td><td>0,026</td><td>0.012</td><td>3.475</td></tr><tr><td>Pz</td><td>Trial:Task - Down vs. Sham</td><td>-0,005</td><td>-0,024</td><td>0,013</td><td>0.011</td><td>0.383</td></tr><tr><td>Pz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>-0,274</td><td>-0,501</td><td>-0,043</td><td>1.756</td><td>0.01</td></tr><tr><td>Pz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>-0,026</td><td>-0,216</td><td>0,164</td><td>0.099</td><td>0.646</td></tr><tr><td>Pz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>0,15</td><td>-0,078</td><td>0,379</td><td>0.264</td><td>9.286</td></tr><tr><td>Pz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>0,016</td><td>-0,174</td><td>0,206</td><td>0.098</td><td>1.312</td></tr><tr><td>Pz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>0,036</td><td>-0,002</td><td>0,073</td><td>0.106</td><td>30.264</td></tr><tr><td>Pz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>0,009</td><td>-0,025</td><td>0,044</td><td>0.02</td><td>2.381</td></tr><tr><td>Pz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>-0,022</td><td>-0,06</td><td>0,015</td><td>0.038</td><td>0.136</td></tr><tr><td>Pz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>-0,024</td><td>-0,059</td><td>0,01</td><td>0.046</td><td>0.091</td></tr></tbody></table>

Each model reported has been computed twice in order to ensure the stability of the BFs. If not specified, each numerical value corresponds to the average of the values obtained across these two model computations. The 'Estimate' column stands for the estimated group-level effects (slopes) of each model 'Parameter' (in z-score standardised units). For the 'Trial' predictor, the estimate corresponds to the group-level effect of one trial of the 1 Hz training block (defined as reference for subsequent comparisons for the Frequency predictor) of the Alpha-Up group (defined as reference for subsequent comparisons for the Task predictor). For the 'Frequency' predictor, each comparison (i.e., '5 Hz vs. 1 Hz' and '10 Hz vs. 5 Hz') estimate refers to the group-level effect during each training block first trial (modality of Trial predictor defined as reference for subsequent comparisons) of the Alpha-Up group. For the 'Task' predictor, the estimate of both comparisons ('Up vs. Down' and 'Down vs. Sham') refers to the between-group effect within the first trial of the 1 Hz training block. The 'Lower' and 'Upper' columns correspond to the minimal lower and maximal upper bounds of the two 95% CrI computed. The 'BF<sub>10</sub>' and 'BF<sub>10+</sub>' columns correspond to the BF in favour of the alternative hypothesis (relative to the null) and the directional (i.e., one-sided) BF, respectively.

Lines in gold highlight corresponding electrode and parameter for which BFs quantify sufficient evidence in favour of the alternative hypothesis over the null (i.e., presence of an effect) on alpha power.

<table><caption>**Supplementary Table 7 Estimates from models computed on alpha spectral power during the transfer block.**</caption><thead><tr><th>Electrode</th><th>Parameter</th><th>Estimate</th><th>Lower</th><th>Upper</th><th>BF<sub>10</sub></th><th>BF<sub>10+</sub></th></tr></thead><tbody><tr><td>Fz</td><td>Trial</td><td><strong>0,037</strong></td><td><strong>0,028</strong></td><td><strong>0,047</strong></td><td><strong>&gt; 100</strong></td><td><strong>&gt; 100</strong></td></tr><tr><td>Fz</td><td>Task - Up vs. Down</td><td>0,168</td><td>-0,33</td><td>0,664</td><td>0.32</td><td>2.993</td></tr><tr><td>Fz</td><td>Task - Down vs. Sham</td><td>0,186</td><td>-0,31</td><td>0,686</td><td>0.332</td><td>3.344</td></tr><tr><td>Fz</td><td>Trial:Task - Up vs. Down</td><td>-0,005</td><td>-0,028</td><td>0,018</td><td>0.013</td><td>0.5</td></tr><tr><td>Fz</td><td>Trial:Task - Down vs. Sham</td><td>0,017</td><td>-0,006</td><td>0,04</td><td>0.034</td><td>12.418</td></tr><tr><td>Cz</td><td>Trial</td><td><strong>0,037</strong></td><td><strong>0,027</strong></td><td><strong>0,047</strong></td><td><strong>&gt; 100</strong></td><td><strong>&gt; 100</strong></td></tr><tr><td>Cz</td><td>Task - Up vs. Down</td><td>0,261</td><td>-0,233</td><td>0,764</td><td>0.43</td><td>5.729</td></tr><tr><td>Cz</td><td>Task - Down vs. Sham</td><td>0,23</td><td>-0,273</td><td>0,725</td><td>0.387</td><td>4.643</td></tr><tr><td>Cz</td><td>Trial:Task - Up vs. Down</td><td>-0,007</td><td>-0,031</td><td>0,018</td><td>0.014</td><td>0.416</td></tr><tr><td>Cz</td><td>Trial:Task - Down vs. Sham</td><td>0,02</td><td>-0,005</td><td>0,045</td><td>0.047</td><td>17.748</td></tr></tbody></table>
<table><thead><tr><th>Pz</th><th>Trial</th><th>0,033</th><th>0,022</th><th>0,043</th><th>&gt; 100</th><th>&gt; 100</th></tr></thead><tbody><tr><td>Pz</td><td>Task - Up vs. Down</td><td>0,298</td><td>-0,189</td><td>0,788</td><td>0,52</td><td>7,763</td></tr><tr><td>Pz</td><td>Task - Down vs. Sham</td><td>0,28</td><td>-0,212</td><td>0,765</td><td>0,473</td><td>6,74</td></tr><tr><td>Pz</td><td>Trial:Task - Up vs. Down</td><td>0,007</td><td>-0,018</td><td>0,033</td><td>0,015</td><td>2,476</td></tr><tr><td>Pz</td><td>Trial:Task - Down vs. Sham</td><td>0,006</td><td>-0,019</td><td>0,032</td><td>0,014</td><td>2,244</td></tr></tbody></table>

Each model reported has been computed twice in order to ensure the stability of the BFs. If not specified, each numerical value corresponds to the average of the values obtained across these two model computations. The 'Estimate' column stands for the estimated group-level effects (slopes) of each model 'Parameter' (in z-score standardised units). For the 'Trial' predictor, the estimate corresponds to the group-level effect of one trial within the Alpha-Up group (defined as reference for subsequent comparisons for the Task predictor). For the 'Task' predictor, the estimate of both comparisons ('Up vs. Down' and 'Down vs. Sham') refers to the between-group effect within the first trial of the transfer block (defined as reference for subsequent comparisons for the Trial predictor). The 'Lower' and 'Upper' columns correspond to the minimal lower and maximal upper bounds of the two 95% CrI computed. The 'BF<sub>10</sub>' and 'BF<sub>10+</sub>' columns correspond to the BF in favour of the alternative hypothesis (relative to the null) and the directional (i.e., one-sided) BF, respectively.

Lines in gold highlight corresponding electrode and parameter for which BFs quantify sufficient evidence in favour of the alternative hypothesis over the null (i.e., presence of an effect) on alpha power.

<table><caption>Supplementary Table 8 Estimates from models computed on theta, SMR and beta spectral power during training blocks.</caption><thead><tr><th>Frequency Band</th><th>Electrode</th><th>Predictor</th><th>Estimate</th><th>Lower</th><th>Upper</th><th>BF<sub>10</sub></th><th>BF<sub>10+</sub></th></tr></thead><tbody><tr><td>Theta</td><td>Fz</td><td>Trial</td><td>0,013</td><td>0,006</td><td>0,02</td><td>1.166</td><td>&gt; 100</td></tr><tr><td>Theta</td><td>Fz</td><td>Frequency - 5 Hz vs. 1 Hz</td><td>0,078</td><td>0,004</td><td>0,152</td><td>0.312</td><td>48.64</td></tr><tr><td>Theta</td><td>Fz</td><td>Frequency - 10 Hz vs. 5 Hz</td><td>-0,107</td><td>-0,173</td><td>-0,04</td><td>4.457</td><td>0.001</td></tr><tr><td>Theta</td><td>Fz</td><td>Task - Up vs. Down</td><td>0,148</td><td>-0,327</td><td>0,619</td><td>0.292</td><td>2.742</td></tr><tr><td>Theta</td><td>Fz</td><td>Task - Down vs. Sham</td><td>0,046</td><td>-0,431</td><td>0,521</td><td>0.242</td><td>1.368</td></tr><tr><td>Theta</td><td>Fz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz</td><td>-0,014</td><td>-0,027</td><td>-0,002</td><td>0.1</td><td>0.009</td></tr><tr><td>Theta</td><td>Fz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz</td><td>0,015</td><td>0,003</td><td>0,027</td><td>0.126</td><td>&gt; 100</td></tr><tr><td>Theta</td><td>Fz</td><td>Trial:Task - Up vs. Down</td><td>-0,004</td><td>-0,022</td><td>0,014</td><td>0.01</td><td>0.509</td></tr><tr><td>Theta</td><td>Fz</td><td>Trial:Task - Down vs. Sham</td><td>0,019</td><td>0,001</td><td>0,037</td><td>0.072</td><td>45.93</td></tr><tr><td>Theta</td><td>Fz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>0,038</td><td>-0,143</td><td>0,22</td><td>0.1</td><td>1.953</td></tr><tr><td>Theta</td><td>Fz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>-0,103</td><td>-0,266</td><td>0,059</td><td>0.177</td><td>0.118</td></tr><tr><td>Theta</td><td>Fz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>0,06</td><td>-0,121</td><td>0,241</td><td>0.114</td><td>2.859</td></tr><tr><td>Theta</td><td>Fz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>-0,026</td><td>-0,189</td><td>0,136</td><td>0.088</td><td>0.601</td></tr><tr><td>Theta</td><td>Fz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>-0,011</td><td>-0,04</td><td>0,019</td><td>0.019</td><td>0.304</td></tr><tr><td>Theta</td><td>Fz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>0,02</td><td>-0,009</td><td>0,05</td><td>0.037</td><td>10.166</td></tr><tr><td>Theta</td><td>Fz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>-0,006</td><td>-0,035</td><td>0,024</td><td>0.016</td><td>0.538</td></tr><tr><td>Theta</td><td>Fz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>0</td><td>-0,03</td><td>0,029</td><td>0.015</td><td>0.956</td></tr><tr><td>SMR</td><td>Fz</td><td>Trial</td><td>0,013</td><td>0,006</td><td>0,02</td><td>1.178</td><td>&gt; 100</td></tr><tr><td>SMR</td><td>Fz</td><td>Frequency - 5 Hz vs. 1 Hz</td><td>0,036</td><td>-0,041</td><td>0,112</td><td>0.06</td><td>4.663</td></tr><tr><td>SMR</td><td>Fz</td><td>Frequency - 10 Hz vs. 5 Hz</td><td>0,039</td><td>-0,033</td><td>0,111</td><td>0.066</td><td>6.01</td></tr><tr><td>SMR</td><td>Fz</td><td>Task - Up vs. Down</td><td>0,036</td><td>-0,453</td><td>0,524</td><td>0.244</td><td>1.25</td></tr><tr><td>SMR</td><td>Fz</td><td>Task - Down vs. Sham</td><td>0,045</td><td>-0,443</td><td>0,531</td><td>0.253</td><td>1.34</td></tr></tbody></table>
<table><tbody><tr><td>SMR</td><td>Fz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz</td><td>-0,006</td><td>-0,019</td><td>0,008</td><td>0.01</td><td>0.264</td></tr><tr><td>SMR</td><td>Fz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz</td><td>0,005</td><td>-0,009</td><td>0,018</td><td>0.009</td><td>3.114</td></tr><tr><td>SMR</td><td>Fz</td><td>Trial:Task - Up vs. Down</td><td>0,002</td><td>-0,015</td><td>0,02</td><td>0.009</td><td>1.577</td></tr><tr><td>SMR</td><td>Fz</td><td>Trial:Task - Down vs. Sham</td><td>-0,004</td><td>-0,021</td><td>0,014</td><td>0.01</td><td>0.514</td></tr><tr><td>SMR</td><td>Fz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>-0,01</td><td>-0,194</td><td>0,175</td><td>0.094</td><td>0.841</td></tr><tr><td>SMR</td><td>Fz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>-0,158</td><td>-0,334</td><td>0,017</td><td>0.434</td><td>0.04</td></tr><tr><td>SMR</td><td>Fz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>0,148</td><td>-0,038</td><td>0,332</td><td>0.318</td><td>16.235</td></tr><tr><td>SMR</td><td>Fz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>0,088</td><td>-0,088</td><td>0,265</td><td>0.144</td><td>5.128</td></tr><tr><td>SMR</td><td>Fz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>-0,006</td><td>-0,04</td><td>0,027</td><td>0.018</td><td>0.552</td></tr><tr><td>SMR</td><td>Fz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>0,036</td><td>0,003</td><td>0,07</td><td>0.166</td><td>63.056</td></tr><tr><td>SMR</td><td>Fz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>-0,002</td><td>-0,035</td><td>0,032</td><td>0.017</td><td>0.855</td></tr><tr><td>SMR</td><td>Fz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>-0,034</td><td>-0,068</td><td>-0,001</td><td>0.136</td><td>0.022</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial</td><td>0,008</td><td>0</td><td>0,016</td><td>0.028</td><td>36.626</td></tr><tr><td>Beta</td><td>Fz</td><td>Frequency - 5 Hz vs. 1 Hz</td><td>0,023</td><td>-0,075</td><td>0,121</td><td>0.054</td><td>2.091</td></tr><tr><td>Beta</td><td>Fz</td><td>Frequency - 10 Hz vs. 5 Hz</td><td>-0,081</td><td>-0,163</td><td>0</td><td>0.288</td><td>0.026</td></tr><tr><td>Beta</td><td>Fz</td><td>Task - Up vs. Down</td><td>-0,008</td><td>-0,509</td><td>0,499</td><td>0.254</td><td>0.95</td></tr><tr><td>Beta</td><td>Fz</td><td>Task - Down vs. Sham</td><td>0,026</td><td>-0,488</td><td>0,522</td><td>0.258</td><td>1.19</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz</td><td>0</td><td>-0,021</td><td>0,021</td><td>0.01</td><td>1.006</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz</td><td>0,021</td><td>0,003</td><td>0,038</td><td>0.119</td><td>86.308</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial:Task - Up vs. Down</td><td>0,001</td><td>-0,019</td><td>0,021</td><td>0.01</td><td>1.159</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial:Task - Down vs. Sham</td><td>-0,002</td><td>-0,022</td><td>0,018</td><td>0.01</td><td>0.722</td></tr><tr><td>Beta</td><td>Fz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>-0,003</td><td>-0,24</td><td>0,235</td><td>0.122</td><td>0.964</td></tr><tr><td>Beta</td><td>Fz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>-0,018</td><td>-0,216</td><td>0,18</td><td>0.102</td><td>0.742</td></tr><tr><td>Beta</td><td>Fz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>0,08</td><td>-0,159</td><td>0,319</td><td>0.151</td><td>2.922</td></tr><tr><td>Beta</td><td>Fz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>0,009</td><td>-0,191</td><td>0,205</td><td>0.098</td><td>1.144</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>0,011</td><td>-0,039</td><td>0,061</td><td>0.028</td><td>2.035</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>0,02</td><td>-0,023</td><td>0,063</td><td>0.034</td><td>4.642</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>-0,02</td><td>-0,071</td><td>0,03</td><td>0.035</td><td>0.268</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>-0,013</td><td>-0,056</td><td>0,03</td><td>0.026</td><td>0.384</td></tr><tr><td>Theta</td><td>Cz</td><td>Trial</td><td>0,01</td><td>0,003</td><td>0,018</td><td>0.121</td><td>&gt; 100</td></tr><tr><td>Theta</td><td>Cz</td><td>Frequency - 5 Hz vs. 1 Hz</td><td>0,089</td><td>0,015</td><td>0,164</td><td>0.58</td><td>&gt; 100</td></tr><tr><td><strong>Theta</strong></td><td><strong>Cz</strong></td><td><strong>Frequency - 10 Hz vs. 5 Hz</strong></td><td><strong>-0,125</strong></td><td><strong>-0,196</strong></td><td><strong>-0,055</strong></td><td><strong>12.826</strong></td><td><strong>0</strong></td></tr><tr><td>Theta</td><td>Cz</td><td>Task - Up vs. Down</td><td>0,177</td><td>-0,292</td><td>0,644</td><td>0.312</td><td>3.429</td></tr><tr><td>Theta</td><td>Cz</td><td>Task - Down vs. Sham</td><td>0,066</td><td>-0,402</td><td>0,535</td><td>0.242</td><td>1.57</td></tr><tr><td>Theta</td><td>Cz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz</td><td>-0,018</td><td>-0,03</td><td>-0,007</td><td>0.678</td><td>0.001</td></tr></tbody></table>
<table><tbody><tr><td>Theta</td><td>Cz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz</td><td>0,018</td><td>0,006</td><td>0,03</td><td>0.384</td><td>&gt; 100</td></tr><tr><td>Theta</td><td>Cz</td><td>Trial:Task - Up vs. Down</td><td>-0,007</td><td>-0,025</td><td>0,012</td><td>0.012</td><td>0.328</td></tr><tr><td>Theta</td><td>Cz</td><td>Trial:Task - Down vs. Sham</td><td>0,024</td><td>0,005</td><td>0,043</td><td>0.214</td><td>&gt; 100</td></tr><tr><td>Theta</td><td>Cz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>-0,011</td><td>-0,191</td><td>0,17</td><td>0.092</td><td>0.824</td></tr><tr><td>Theta</td><td>Cz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>-0,124</td><td>-0,296</td><td>0,047</td><td>0.242</td><td>0.083</td></tr><tr><td>Theta</td><td>Cz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>0,081</td><td>-0,1</td><td>0,262</td><td>0.135</td><td>4.262</td></tr><tr><td>Theta</td><td>Cz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>0</td><td>-0,173</td><td>0,171</td><td>0.086</td><td>0.987</td></tr><tr><td>Theta</td><td>Cz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>-0,008</td><td>-0,035</td><td>0,02</td><td>0.016</td><td>0.417</td></tr><tr><td>Theta</td><td>Cz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>0,019</td><td>-0,011</td><td>0,049</td><td>0.034</td><td>8.678</td></tr><tr><td>Theta</td><td>Cz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>-0,015</td><td>-0,042</td><td>0,013</td><td>0.024</td><td>0.176</td></tr><tr><td>Theta</td><td>Cz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>0,001</td><td>-0,029</td><td>0,031</td><td>0.015</td><td>1.103</td></tr><tr><td>SMR</td><td>Cz</td><td>Trial</td><td>0,01</td><td>0,004</td><td>0,016</td><td>0.438</td><td>&gt; 100</td></tr><tr><td>SMR</td><td>Cz</td><td>Frequency - 5 Hz vs. 1 Hz</td><td>0,026</td><td>-0,038</td><td>0,09</td><td>0.045</td><td>3.812</td></tr><tr><td>SMR</td><td>Cz</td><td>Frequency - 10 Hz vs. 5 Hz</td><td>0,054</td><td>-0,009</td><td>0,117</td><td>0.133</td><td>20.811</td></tr><tr><td>SMR</td><td>Cz</td><td>Task - Up vs. Down</td><td>0,048</td><td>-0,455</td><td>0,543</td><td>0.251</td><td>1.357</td></tr><tr><td>SMR</td><td>Cz</td><td>Task - Down vs. Sham</td><td>0,089</td><td>-0,409</td><td>0,587</td><td>0.266</td><td>1.781</td></tr><tr><td>SMR</td><td>Cz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz</td><td>-0,004</td><td>-0,016</td><td>0,007</td><td>0.008</td><td>0.295</td></tr><tr><td>SMR</td><td>Cz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz</td><td>0,004</td><td>-0,008</td><td>0,015</td><td>0.007</td><td>2.843</td></tr><tr><td>SMR</td><td>Cz</td><td>Trial:Task - Up vs. Down</td><td>0,004</td><td>-0,011</td><td>0,019</td><td>0.009</td><td>2.245</td></tr><tr><td>SMR</td><td>Cz</td><td>Trial:Task - Down vs. Sham</td><td>-0,004</td><td>-0,02</td><td>0,011</td><td>0.009</td><td>0.39</td></tr><tr><td>SMR</td><td>Cz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>-0,082</td><td>-0,239</td><td>0,075</td><td>0.136</td><td>0.176</td></tr><tr><td>SMR</td><td>Cz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>-0,168</td><td>-0,322</td><td>-0,016</td><td>0.795</td><td>0.016</td></tr><tr><td>SMR</td><td>Cz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>0,188</td><td>0,031</td><td>0,345</td><td>1.206</td><td>&gt; 100</td></tr><tr><td>SMR</td><td>Cz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>0,096</td><td>-0,057</td><td>0,25</td><td>0.166</td><td>8.285</td></tr><tr><td>SMR</td><td>Cz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>0,001</td><td>-0,028</td><td>0,03</td><td>0.015</td><td>1.127</td></tr><tr><td>SMR</td><td>Cz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>0,029</td><td>0,001</td><td>0,057</td><td>0.106</td><td>43.951</td></tr><tr><td>SMR</td><td>Cz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>-0,013</td><td>-0,043</td><td>0,016</td><td>0.022</td><td>0.222</td></tr><tr><td>SMR</td><td>Cz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>-0,024</td><td>-0,052</td><td>0,004</td><td>0.058</td><td>0.05</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial</td><td>0,002</td><td>-0,005</td><td>0,008</td><td>0.004</td><td>2.531</td></tr><tr><td>Beta</td><td>Cz</td><td>Frequency - 5 Hz vs. 1 Hz</td><td>0,042</td><td>-0,037</td><td>0,121</td><td>0.068</td><td>5.81</td></tr><tr><td>Beta</td><td>Cz</td><td>Frequency - 10 Hz vs. 5 Hz</td><td>-0,04</td><td>-0,109</td><td>0,028</td><td>0.068</td><td>0.14</td></tr><tr><td>Beta</td><td>Cz</td><td>Task - Up vs. Down</td><td>-0,002</td><td>-0,51</td><td>0,504</td><td>0.256</td><td>0.992</td></tr><tr><td>Beta</td><td>Cz</td><td>Task - Down vs. Sham</td><td>0,037</td><td>-0,465</td><td>0,549</td><td>0.257</td><td>1.251</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz</td><td>-0,008</td><td>-0,024</td><td>0,009</td><td>0.013</td><td>0.218</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz</td><td>0,015</td><td>0,002</td><td>0,029</td><td>0.087</td><td>78.309</td></tr></tbody></table>
<table><tbody><tr><td>Beta</td><td>Cz</td><td>Trial:Task - Up vs. Down</td><td>0,004</td><td>-0,012</td><td>0,02</td><td>0.009</td><td>2.261</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial:Task - Down vs. Sham</td><td>-0,002</td><td>-0,018</td><td>0,013</td><td>0.008</td><td>0.619</td></tr><tr><td>Beta</td><td>Cz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>-0,071</td><td>-0,263</td><td>0,12</td><td>0.126</td><td>0.299</td></tr><tr><td>Beta</td><td>Cz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>-0,034</td><td>-0,201</td><td>0,134</td><td>0.092</td><td>0.526</td></tr><tr><td>Beta</td><td>Cz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>0,109</td><td>-0,084</td><td>0,301</td><td>0.186</td><td>6.566</td></tr><tr><td>Beta</td><td>Cz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>0,124</td><td>-0,043</td><td>0,292</td><td>0.247</td><td>13.052</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>0,003</td><td>-0,037</td><td>0,043</td><td>0.02</td><td>1.301</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>0,015</td><td>-0,018</td><td>0,048</td><td>0.025</td><td>4.4</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>-0,022</td><td>-0,062</td><td>0,018</td><td>0.036</td><td>0.162</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>-0,016</td><td>-0,049</td><td>0,016</td><td>0.028</td><td>0.192</td></tr><tr><td>Theta</td><td>Pz</td><td>Trial</td><td>0,009</td><td>-0,003</td><td>0,021</td><td>0.018</td><td>13.621</td></tr><tr><td>Theta</td><td>Pz</td><td>Frequency - 5 Hz vs. 1 Hz</td><td>0,056</td><td>-0,091</td><td>0,2</td><td>0.096</td><td>3.392</td></tr><tr><td><strong>Theta</strong></td><td><strong>Pz</strong></td><td><strong>Frequency - 10 Hz vs. 5 Hz</strong></td><td><strong>-0,195</strong></td><td><strong>-0,294</strong></td><td><strong>-0,095</strong></td><td><strong>83.367</strong></td><td><strong>0</strong></td></tr><tr><td>Theta</td><td>Pz</td><td>Task - Up vs. Down</td><td>0,226</td><td>-0,198</td><td>0,658</td><td>0.374</td><td>5.736</td></tr><tr><td>Theta</td><td>Pz</td><td>Task - Down vs. Sham</td><td>0,027</td><td>-0,401</td><td>0,455</td><td>0.218</td><td>1.228</td></tr><tr><td>Theta</td><td>Pz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz</td><td>-0,012</td><td>-0,039</td><td>0,015</td><td>0.021</td><td>0.224</td></tr><tr><td>Theta</td><td>Pz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz</td><td>0,027</td><td>0,005</td><td>0,049</td><td>0.224</td><td>&gt; 100</td></tr><tr><td>Theta</td><td>Pz</td><td>Trial:Task - Up vs. Down</td><td>-0,018</td><td>-0,047</td><td>0,011</td><td>0.032</td><td>0.12</td></tr><tr><td>Theta</td><td>Pz</td><td>Trial:Task - Down vs. Sham</td><td>0,029</td><td>0</td><td>0,058</td><td>0.109</td><td>42.567</td></tr><tr><td>Theta</td><td>Pz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>-0,144</td><td>-0,493</td><td>0,207</td><td>0.242</td><td>0.263</td></tr><tr><td>Theta</td><td>Pz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>-0,252</td><td>-0,492</td><td>-0,013</td><td>1.03</td><td>0.02</td></tr><tr><td>Theta</td><td>Pz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>0,15</td><td>-0,2</td><td>0,502</td><td>0.252</td><td>4.074</td></tr><tr><td>Theta</td><td>Pz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>0,146</td><td>-0,093</td><td>0,387</td><td>0.254</td><td>7.681</td></tr><tr><td>Theta</td><td>Pz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>0,022</td><td>-0,042</td><td>0,086</td><td>0.04</td><td>3.007</td></tr><tr><td>Theta</td><td>Pz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>0,041</td><td>-0,011</td><td>0,094</td><td>0.085</td><td>14.981</td></tr><tr><td>Theta</td><td>Pz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>-0,042</td><td>-0,106</td><td>0,023</td><td>0.074</td><td>0.114</td></tr><tr><td>Theta</td><td>Pz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>-0,021</td><td>-0,074</td><td>0,031</td><td>0.036</td><td>0.27</td></tr><tr><td>SMR</td><td>Pz</td><td>Trial</td><td>0,007</td><td>-0,003</td><td>0,018</td><td>0.014</td><td>11.725</td></tr><tr><td>SMR</td><td>Pz</td><td>Frequency - 5 Hz vs. 1 Hz</td><td>-0,04</td><td>-0,161</td><td>0,081</td><td>0.075</td><td>0.346</td></tr><tr><td>SMR</td><td>Pz</td><td>Frequency - 10 Hz vs. 5 Hz</td><td>0,051</td><td>-0,033</td><td>0,135</td><td>0.086</td><td>7.676</td></tr><tr><td>SMR</td><td>Pz</td><td>Task - Up vs. Down</td><td>0,102</td><td>-0,382</td><td>0,585</td><td>0.265</td><td>1.962</td></tr><tr><td>SMR</td><td>Pz</td><td>Task - Down vs. Sham</td><td>0,133</td><td>-0,349</td><td>0,614</td><td>0.282</td><td>2.412</td></tr><tr><td>SMR</td><td>Pz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz</td><td>0,004</td><td>-0,02</td><td>0,027</td><td>0.013</td><td>1.618</td></tr><tr><td>SMR</td><td>Pz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz</td><td>0,008</td><td>-0,011</td><td>0,026</td><td>0.013</td><td>3.892</td></tr><tr><td>SMR</td><td>Pz</td><td>Trial:Task - Up vs. Down</td><td>-0,001</td><td>-0,027</td><td>0,024</td><td>0.013</td><td>0.849</td></tr><tr><td>SMR</td><td>Pz</td><td>Trial:Task - Down vs. Sham</td><td>-0,006</td><td>-0,031</td><td>0,019</td><td>0.014</td><td>0.469</td></tr></tbody></table>
<table><tbody><tr><td>SMR</td><td>Pz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>-0,256</td><td>-0,546</td><td>0,036</td><td>0.665</td><td>0.044</td></tr><tr><td>SMR</td><td>Pz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>-0,123</td><td>-0,326</td><td>0,081</td><td>0.21</td><td>0.132</td></tr><tr><td>SMR</td><td>Pz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>0,348</td><td>0,055</td><td>0,638</td><td>2.224</td><td>97.172</td></tr><tr><td>SMR</td><td>Pz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>0,111</td><td>-0,092</td><td>0,314</td><td>0.182</td><td>6.072</td></tr><tr><td>SMR</td><td>Pz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>0,027</td><td>-0,03</td><td>0,085</td><td>0.046</td><td>4.736</td></tr><tr><td>SMR</td><td>Pz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>0,025</td><td>-0,02</td><td>0,07</td><td>0.041</td><td>6.428</td></tr><tr><td>SMR</td><td>Pz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>-0,047</td><td>-0,105</td><td>0,011</td><td>0.106</td><td>0.056</td></tr><tr><td>SMR</td><td>Pz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>-0,029</td><td>-0,074</td><td>0,017</td><td>0.051</td><td>0.118</td></tr><tr><td>Beta</td><td>Pz</td><td>Trial</td><td>-0,007</td><td>-0,017</td><td>0,003</td><td>0.013</td><td>0.093</td></tr><tr><td>Beta</td><td>Pz</td><td>Frequency - 5 Hz vs. 1 Hz</td><td>-0,016</td><td>-0,147</td><td>0,115</td><td>0.068</td><td>0.673</td></tr><tr><td>Beta</td><td>Pz</td><td>Frequency - 10 Hz vs. 5 Hz</td><td>-0,034</td><td>-0,127</td><td>0,058</td><td>0.062</td><td>0.304</td></tr><tr><td>Beta</td><td>Pz</td><td>Task - Up vs. Down</td><td>-0,024</td><td>-0,511</td><td>0,459</td><td>0.242</td><td>0.861</td></tr><tr><td>Beta</td><td>Pz</td><td>Task - Down vs. Sham</td><td>-0,01</td><td>-0,496</td><td>0,474</td><td>0.248</td><td>0.945</td></tr><tr><td>Beta</td><td>Pz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz</td><td>0,009</td><td>-0,017</td><td>0,035</td><td>0.017</td><td>3.08</td></tr><tr><td>Beta</td><td>Pz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz</td><td>0,015</td><td>-0,005</td><td>0,036</td><td>0.03</td><td>12.579</td></tr><tr><td>Beta</td><td>Pz</td><td>Trial:Task - Up vs. Down</td><td>0,001</td><td>-0,024</td><td>0,026</td><td>0.013</td><td>1.166</td></tr><tr><td>Beta</td><td>Pz</td><td>Trial:Task - Down vs. Sham</td><td>-0,001</td><td>-0,026</td><td>0,024</td><td>0.013</td><td>0.863</td></tr><tr><td>Beta</td><td>Pz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>-0,328</td><td>-0,64</td><td>-0,013</td><td>1.295</td><td>0.021</td></tr><tr><td>Beta</td><td>Pz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>0,016</td><td>-0,21</td><td>0,241</td><td>0.114</td><td>1.24</td></tr><tr><td>Beta</td><td>Pz</td><td>Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>0,294</td><td>-0,02</td><td>0,607</td><td>0.853</td><td>29.14</td></tr><tr><td>Beta</td><td>Pz</td><td>Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>0,112</td><td>-0,113</td><td>0,334</td><td>0.184</td><td>5.144</td></tr><tr><td>Beta</td><td>Pz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Up vs. Down</td><td>0,049</td><td>-0,014</td><td>0,112</td><td>0.101</td><td>14.761</td></tr><tr><td>Beta</td><td>Pz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Up vs. Down</td><td>0,004</td><td>-0,047</td><td>0,054</td><td>0.026</td><td>1.247</td></tr><tr><td>Beta</td><td>Pz</td><td>Trial:Frequency - 5 Hz vs. 1 Hz:Task - Down vs. Sham</td><td>-0,06</td><td>-0,123</td><td>0,003</td><td>0.189</td><td>0.032</td></tr><tr><td>Beta</td><td>Pz</td><td>Trial:Frequency - 10 Hz vs. 5 Hz:Task - Down vs. Sham</td><td>-0,013</td><td>-0,063</td><td>0,037</td><td>0.029</td><td>0.432</td></tr></tbody></table>

Each model reported has been computed twice in order to ensure the stability of the BFs. If not specified, each numerical value corresponds to the average of the values obtained across these two model computations. The 'Estimate' column stands for the estimated group-level effects (slopes) of each model 'Parameter' (in z-score standardised units). For the 'Trial' predictor, the estimate corresponds to the group-level effect of one trial of the 1 Hz training block (defined as reference for subsequent comparisons for the Frequency predictor) of the Alpha-Up group (defined as reference for subsequent comparisons for the Task predictor). For the 'Frequency' predictor, each comparison (i.e., '5 Hz vs. 1 Hz' and '10 Hz vs. 5 Hz') estimate refers to the group-level effect during each training block first trial (modality of Trial predictor defined as reference for subsequent comparisons) of the Alpha-Up group. For the 'Task' predictor, the estimate of both comparisons ('Alpha-Up vs. Alpha-Down' and 'Alpha-Down vs. Sham') refers to the between-group effect within the first trial of the 1 Hz training block. The 'Lower' and 'Upper' columns correspond to the minimal lower and maximal upper bounds of the two 95% CrI computed. The 'BF<sub>10</sub>' and 'BF<sub>10+</sub>' columns correspond to the BF in favour of the alternative hypothesis (relative to the null) and the directional (i.e., one-sided) BF, respectively.

Lines in gold highlight the EEG features for which BFs quantify sufficient evidence in favour of the alternative hypothesis over the null (i.e., presence of an effect).
<table><caption>Supplementary Table 9 Estimates from models computed on theta, SMR and beta spectral power during the transfer block.</caption><thead><tr><th>Frequency Band</th><th>Electrode</th><th>Predictor</th><th>Estimate</th><th>Lower</th><th>Upper</th><th>BF<sub>10</sub></th><th>BF<sub>10+</sub></th></tr></thead><tbody><tr><td rowspan="4">Theta</td><td>Fz</td><td><strong>Trial</strong></td><td><strong>0,03</strong></td><td><strong>0,019</strong></td><td><strong>0,04</strong></td><td><strong>&gt; 100</strong></td><td><strong>&gt; 100</strong></td></tr><tr><td>Fz</td><td>Task - Up vs. Down</td><td>0,127</td><td>-0,379</td><td>0,639</td><td>0,284</td><td>2,256</td></tr><tr><td>Fz</td><td>Task - Down vs. Sham</td><td>0,108</td><td>-0,398</td><td>0,608</td><td>0,278</td><td>1,987</td></tr><tr><td>Fz</td><td>Trial:Task - Up vs. Down</td><td>-0,002</td><td>-0,027</td><td>0,024</td><td>0,013</td><td>0,821</td></tr><tr><td>Fz</td><td>Trial:Task - Down vs. Sham</td><td>0,03</td><td>0,004</td><td>0,056</td><td>0,19</td><td>92,495</td></tr><tr><td rowspan="4">SMR</td><td>Fz</td><td><strong>Trial</strong></td><td><strong>0,022</strong></td><td><strong>0,012</strong></td><td><strong>0,033</strong></td><td><strong>44.844</strong></td><td><strong>&gt; 100</strong></td></tr><tr><td>Fz</td><td>Task - Up vs. Down</td><td>0,08</td><td>-0,4</td><td>0,556</td><td>0,252</td><td>1,704</td></tr><tr><td>Fz</td><td>Task - Down vs. Sham</td><td>-0,048</td><td>-0,525</td><td>0,428</td><td>0,244</td><td>0,73</td></tr><tr><td>Fz</td><td>Trial:Task - Up vs. Down</td><td>-0,007</td><td>-0,034</td><td>0,019</td><td>0,015</td><td>0,396</td></tr><tr><td>Fz</td><td>Trial:Task - Down vs. Sham</td><td>0,04</td><td>0,014</td><td>0,066</td><td>1,108</td><td>&gt; 100</td></tr><tr><td>Beta</td><td>Fz</td><td><strong>Trial</strong></td><td>0,018</td><td>0</td><td>0,037</td><td>0,062</td><td>38,326</td></tr><tr><td>Beta</td><td>Fz</td><td>Task - Up vs. Down</td><td>0,066</td><td>-0,383</td><td>0,512</td><td>0,238</td><td>1,595</td></tr><tr><td>Beta</td><td>Fz</td><td>Task - Down vs. Sham</td><td>-0,108</td><td>-0,558</td><td>0,346</td><td>0,255</td><td>0,468</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial:Task - Up vs. Down</td><td>-0,003</td><td>-0,048</td><td>0,042</td><td>0,023</td><td>0,808</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial:Task - Down vs. Sham</td><td>0,026</td><td>-0,019</td><td>0,071</td><td>0,044</td><td>7,185</td></tr><tr><td rowspan="4">Theta</td><td>Cz</td><td><strong>Trial</strong></td><td><strong>0,029</strong></td><td><strong>0,018</strong></td><td><strong>0,04</strong></td><td><strong>&gt; 100</strong></td><td><strong>&gt; 100</strong></td></tr><tr><td>Cz</td><td>Task - Up vs. Down</td><td>0,181</td><td>-0,335</td><td>0,696</td><td>0,33</td><td>3,141</td></tr><tr><td>Cz</td><td>Task - Down vs. Sham</td><td>0,111</td><td>-0,407</td><td>0,629</td><td>0,287</td><td>1,979</td></tr><tr><td>Cz</td><td>Trial:Task - Up vs. Down</td><td>-0,002</td><td>-0,029</td><td>0,025</td><td>0,014</td><td>0,756</td></tr><tr><td>Cz</td><td>Trial:Task - Down vs. Sham</td><td>0,03</td><td>0,003</td><td>0,056</td><td>0,154</td><td>68,163</td></tr><tr><td rowspan="4">SMR</td><td>Cz</td><td><strong>Trial</strong></td><td><strong>0,017</strong></td><td><strong>0,009</strong></td><td><strong>0,025</strong></td><td><strong>12.796</strong></td><td><strong>&gt; 100</strong></td></tr><tr><td>Cz</td><td>Task - Up vs. Down</td><td>0,13</td><td>-0,346</td><td>0,604</td><td>0,282</td><td>2,428</td></tr><tr><td>Cz</td><td>Task - Down vs. Sham</td><td>-0,014</td><td>-0,494</td><td>0,463</td><td>0,239</td><td>0,905</td></tr><tr><td>Cz</td><td>Trial:Task - Up vs. Down</td><td>-0,008</td><td>-0,028</td><td>0,012</td><td>0,014</td><td>0,279</td></tr><tr><td>Cz</td><td>Trial:Task - Down vs. Sham</td><td>0,027</td><td>0,007</td><td>0,047</td><td>0,37</td><td>&gt; 100</td></tr><tr><td>Beta</td><td>Cz</td><td><strong>Trial</strong></td><td>0,009</td><td>-0,001</td><td>0,019</td><td>0,024</td><td>26,131</td></tr><tr><td>Beta</td><td>Cz</td><td>Task - Up vs. Down</td><td>0,013</td><td>-0,445</td><td>0,464</td><td>0,226</td><td>1,092</td></tr><tr><td>Beta</td><td>Cz</td><td>Task - Down vs. Sham</td><td>-0,053</td><td>-0,505</td><td>0,401</td><td>0,236</td><td>0,683</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial:Task - Up vs. Down</td><td>0,013</td><td>-0,011</td><td>0,038</td><td>0,022</td><td>5,988</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial:Task - Down vs. Sham</td><td>0,019</td><td>-0,006</td><td>0,044</td><td>0,04</td><td>14,017</td></tr><tr><td rowspan="4">Theta</td><td>Pz</td><td><strong>Trial</strong></td><td><strong>0,034</strong></td><td><strong>0,022</strong></td><td><strong>0,047</strong></td><td><strong>&gt; 100</strong></td><td><strong>&gt; 100</strong></td></tr><tr><td>Pz</td><td>Task - Up vs. Down</td><td>0,041</td><td>-0,452</td><td>0,534</td><td>0,252</td><td>1,3</td></tr><tr><td>Pz</td><td>Task - Down vs. Sham</td><td>0,233</td><td>-0,264</td><td>0,725</td><td>0,387</td><td>4,701</td></tr><tr><td>Pz</td><td>Trial:Task - Up vs. Down</td><td>0,013</td><td>-0,018</td><td>0,043</td><td>0,021</td><td>3,929</td></tr><tr><td>Pz</td><td>Trial:Task - Down vs. Sham</td><td>0</td><td>-0,031</td><td>0,03</td><td>0,015</td><td>0,958</td></tr><tr><td>SMR</td><td>Pz</td><td><strong>Trial</strong></td><td>0,015</td><td>0,006</td><td>0,024</td><td>0,963</td><td>&gt; 100</td></tr><tr><td>SMR</td><td>Pz</td><td>Task - Up vs. Down</td><td>0,176</td><td>-0,275</td><td>0,63</td><td>0,312</td><td>3,562</td></tr><tr><td>SMR</td><td>Pz</td><td>Task - Down vs. Sham</td><td>0,002</td><td>-0,45</td><td>0,456</td><td>0,23</td><td>1,018</td></tr><tr><td>SMR</td><td>Pz</td><td>Trial:Task - Up vs. Down</td><td>-0,013</td><td>-0,036</td><td>0,009</td><td>0,023</td><td>0,136</td></tr><tr><td>SMR</td><td>Pz</td><td>Trial:Task - Down vs. Sham</td><td>0,03</td><td>0,007</td><td>0,052</td><td>0,344</td><td>&gt; 100</td></tr><tr><td>Beta</td><td>Pz</td><td><strong>Trial</strong></td><td>0,006</td><td>-0,005</td><td>0,016</td><td>0,01</td><td>6,351</td></tr><tr><td>Beta</td><td>Pz</td><td>Task - Up vs. Down</td><td>-0,027</td><td>-0,464</td><td>0,411</td><td>0,218</td><td>0,818</td></tr><tr><td>Beta</td><td>Pz</td><td>Task - Down vs. Sham</td><td>-0,038</td><td>-0,472</td><td>0,391</td><td>0,223</td><td>0,761</td></tr></tbody></table>
<table><tr><td>Beta</td><td>Pz</td><td>Trial:Task - Up vs. Down</td><td>0,005</td><td>-0,02</td><td>0,031</td><td>0,014</td><td>1,914</td></tr><tr><td>Beta</td><td>Pz</td><td>Trial:Task - Down vs. Sham</td><td>0,009</td><td>-0,017</td><td>0,034</td><td>0,016</td><td>3,03</td></tr></table>

Each model reported has been computed twice in order to ensure the stability of the BFs. If not specified, each numerical value corresponds to the average of the values obtained across these two model computations. The 'Estimate' column stands for the estimated group-level effects (slopes) of each model 'Parameter' (in z-score standardised units). For the 'Trial' predictor, the estimate corresponds to the group-level effect of one trial within the Alpha-Up group (defined as reference for subsequent comparisons for the Task predictor). For the 'Task' predictor, the estimate of both comparisons ('Alpha-Up vs. Alpha-Down' and 'Alpha-Down vs. Sham') refers to the between-group effect within the first trial of the transfer block (defined as reference for subsequent comparisons for the Trial predictor). The 'Lower' and 'Upper' columns correspond to the minimal lower and maximal upper bounds of the two 95% CrI computed. The 'BF<sub>10</sub>' and 'BF<sub>10+</sub>' columns correspond to the BF in favour of the alternative hypothesis (relative to the null) and the directional (i.e., one-sided) BF, respectively. Lines in gold highlight the EEG features for which BFs quantify sufficient evidence in favour of the alternative hypothesis over the null (i.e., presence of an effect).

**Supplementary Table 10 Completed CRED-nf checklist from Ros et al. (2020).**

**1. Pre-experiment**

a. This experiment was not preregistered.

b. The sample size of the present study was determined using a Bayesian a priori power analysis for three-group comparisons in such experimental design. A sample size of 24 per group was judged sufficient to obtain enough statistical power.

**2. Control groups**

a. The alpha trajectories of this Alpha-Down group were compared to those of independent Alpha-Up and Sham groups from our previous study (Maaz et al., 2025), enabling a stringent evaluation of specificity against both genuine but opposite-direction feedback and non-contingent, i.e., sham feedback (Sorger et al., 2019), respectively.

b. The experiment did not directly include a double-blind (only one Alpha-Down group, yet compared to two groups which are included in a double-blind study).

c. Blinding of those who rate the outcome and those who analyse the data:

- NA: There was only one participant group (data collection).

- The person who performed all statistical analyses (JM) remained blind to group assignments throughout.

d. These questions aimed to qualitatively assess participants' feeling of feedback control and belief of having received sham feedback, respectively. Importantly, these measures are recommended for implementation in sham-controlled EEG-NF studies to control for differences in feedback and blinding credibility between groups (Ros et al., 2020).

e. NA: This is not a clinical efficacy study

**3. Control measures**

a. Psychosocial factors were not measured

b. Participants were not informed about the specific EEG-NF target (i.e., downregulate their alpha power at Pz), nor provided with a pre-defined mental strategy. Rather, they were encouraged to find mental strategies correlating with increases in circle size as in common practices (Enriquez-Geppert et al., 2017).

c. The strategies participants used were not recorded or not reported in the manuscript

d. The manuscript does not report the methods used for online-data processing and artifact correction

e. Condition and group effects for artifacts were not measured, or not reported in the manuscript

**4. Feedback specifications**

a. See the EEG online processing section.

b. See Material and neurofeedback implementation, as well as Procedure sections.

c. During each trial, a grey circle was presented at the centre of a screen. First, during three training blocks, the circle size was continuously updated as real-time feedback
of the participant's alpha band (8-12 Hz) power at Pz (Chikhi et al., 2023). The circle size was inversely proportional to real-time alpha power.

d. During three training blocks, the circle size was continuously updated as real-time feedback of the participant's alpha band (8-12 Hz) power at Pz (Chikhi et al., 2023).

e. See Apparatus section.

**5. Outcome measures – Brain**

a. Concerning the trial repetition effect, extreme evidence was quantified in favour of a positive effect on alpha power (Fz: $\beta$ = 0.018, 95% CrI [0.012, 0.024], BF10 > 100, BF10+ > 100; Cz: $\beta$ = 0.019, 95% CrI [0.013, 0.025], BF10 > 100, BF10+ > 100; Pz: $\beta$ = 0.02, 95% CrI [0.012, 0.027], BF10 > 100, BF10+ > 100).

b. Fig. 1 shows alpha power evolution at frontal, central and parietal electrodes during training depending on the Task participants was submitted to (Alpha-Down EEG-NF, Alpha-Up EEG-NF, or Sham EEG-NF). Fig. 3 displays power evolution within each considered band throughout the EEG-NF session.

c. Regarding absolute power difference between groups, moderate evidence supported the absence of effect at Fz (Alpha-Up vs. Alpha-Down: $\beta$ = 0.192, 95% CrI [-0.273, 0.662], BF10 = 0.328; Alpha-Down vs. Sham: $\beta$ = 0.156, 95% CrI [-0.319, 0.626], BF10 = 0.296), while BF10 were insensitive towards neither the null nor the alternative hypothesis at Cz (Alpha-Up vs. Alpha-Down: $\beta$ = 0.232, 95% CrI [-0.226, 0.699], BF10 = 0.384; Alpha-Down vs. Sham: $\beta$ = 0.224, 95% CrI [-0.234, 0.680], BF10 = 0.376) and Pz (Alpha-Up vs. Alpha-Down: $\beta$ = 0.241, 95% CrI [-0.196, 0.678], BF10 = 0.396; Alpha-Down vs. Sham: $\beta$ = 0.29, 95% CrI [-0.148, 0.727], BF10 = 0.518). Importantly, very strong to extreme evidence was found for the absence of interaction between trial repetition and both the targeted direction of alpha modulation (up vs. down; Fz: $\beta$ = -0.002, 95% CrI [-0.017, 0.012], BF10 = 0.008; Cz: $\beta$ = 0.002, 95% CrI [-0.013, 0.018], BF10 = 0.008; Pz: $\beta$ = 0.007, 95% CrI [-0.011, 0.026], BF10 = 0.012) and feedback veracity (down vs. sham; Fz: $\beta$ = 0.001, 95% CrI [-0.013, 0.016], BF10 = 0.007; Cz: $\beta$ = 0.002, 95% CrI [-0.014, 0.017], BF10 = 0.008; Pz: $\beta$ = -0.005, 95% CrI [-0.024, 0.013], BF10 = 0.011).

**6. Outcome measures – Behaviour**

a. NA: the study does not take cognitive or behavioural measures.

b. NA: the study does not take cognitive or behavioural measures.

**7. Data storage**

a. All materials, data and analysis codes are available via the Open Science Framework.
<figure><img src="image_6.png" /><figcaption>**Supplementary Figure 1** Averaged power spectra for each frequency of feedback update.</figcaption></figure>

Power spectra averaged across groups and trials depending on the feedback update frequency (cream: 1 Hz; dark blue: 5 Hz; pink red: 10 Hz) and on electrode position (A: Fz; B: Cz; C: Pz). On each panel, vertical dashed lines represent each of the feedback update frequencies. All spectra have a frequency resolution of ~0.305 Hz.

For each electrode, there is a clear peak at 5 Hz (i.e., within the theta range of 4-8 Hz) when the feedback was update at this frequency, compared to the others. This suggest that updating the visual feedback at 5 Hz entertains an increased in theta spectral power. One can also notice a smaller peak at 10 Hz when using this frequency for feedback update.