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# Spontaneous modulation of alpha power during a neurofeedback session without instructions

Jacob Maaz, Véronique Paban, Laurent Waroquier, Arnaud Rey

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## To cite this version:

Jacob Maaz, Véronique Paban, Laurent Waroquier, Arnaud Rey. Spontaneous modulation of alpha power during a neurofeedback session without instructions. 2025. hal-05248701v2

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Distributed under a Creative Commons Attribution 4.0 International License
Spontaneous modulation of alpha power during a
neurofeedback session without instructions

Jacob Maaz,¹,²,³ Véronique Paban,¹ Laurent Waroquier⁴ 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

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**Abstract**

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Electroencephalographic neurofeedback (EEG-NF) enables individuals to self-regulate specific EEG features with real-time sensory feedback. Despite clinical and cognitive-enhancement applications, the mechanisms underlying the EEG modulation remain poorly understood. Particularly, alpha activity (8-12 Hz) upregulation may occur independently of the participants' volitional control. We previously reported spontaneous increases in alpha power during a passive neurofeedback-like task using pre-recorded EEG feedback. In the present study, we replicated this protocol while implementing an EEG-NF procedure using real-time alpha power. Thirty-two healthy adults observed a grey circle whose size was either fixed (control) or continuously updated at 1, 5 or 10 Hz (experimental conditions). Importantly, participants were not informed of the nature of the feedback and received no instructions for self-regulation. We evaluated the effects of (i) trial repetition, (ii) the presence (control vs. experimental conditions), (iii) the frequency (1, 5 or 10 Hz) and (iv) the source (online vs. offline alpha) of feedback update on EEG features classically targeted by EEG-NF. Importantly, we observed robust increases in alpha power over time independently of the presence, frequency and source of feedback update. The presence, frequency and source of feedback update did not influence the EEG features considered. These findings suggest that alpha EEG-NF upregulation may arise from spontaneous dynamics, such as time-on-task effects, rather than the hypothesised self-regulation mechanism. The assumption that observed alpha increases reflect successful neurofeedback learning thus remain into question. More broadly, the present study highlights the importance of including control conditions and accounting for non-specific effects when evaluating EEG-NF outcomes.

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**Keywords:** EEG; neurofeedback; spectral power; neuromodulation; alpha activity

**Running title:** PASSIVE EEG-NF

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53 **Introduction**

Electroencephalographic neurofeedback (EEG-NF) is a closed-loop procedure in which
individuals receive real-time feedback of their own EEG activity and are encouraged to self-
regulate it (Sitaram et al., 2017). EEG-NF modulates specific EEG features and is effective as
both a clinical intervention and a training method to enhance cognitive functioning (Arns et al.,
2017; Enriquez-Geppert et al., 2017; Thibault et al., 2016). For instance, EEG-NF reduces the
symptoms of attention deficit and/or hyperactivity disorders (ADHD; Enriquez-Geppert et al.,
2019), depression and anxiety disorders (Hammond, 2005), and pharmaco-resistant epilepsy
(Tan et al., 2009). Similarly, the cognitive performances of healthy individuals are improved
in tasks involving memory, attention and procedural learning (for a review, see Gruzelier,
2014). Yet, despite decades of practice, scepticism persists on the actual mechanisms
underlying the effectiveness of EEG-NF on clinical/behavioural outcomes (Kalokairinou et al.,
2022; Thibault & Raz, 2017). Recent rigorous studies brought to light that these effects may
actually be driven by non-specific, confounded factors of the EEG-NF procedure (Dessy et al.,
2020; Neurofeedback Collaborative Group, 2021, 2023; Schabus et al., 2017; Schönenberg et
al., 2017). In particular, authors identify participants' expectations and motivation, and/or
placebo effects (Dessy et al., 2018; Schönenberg et al., 2021; Thibault & Raz, 2018).

Traditionally, the clinical and behavioural effects are specifically attributed to the EEG-NF procedure and the targeted EEG modulation (Micoulaud-Franchi et al., 2019). Accordingly, thanks to a real-time sensory feedback, participants learn to regulate specific features of their EEG activity. In return, this self-regulation reflects adaptive changes in brain function, which ultimately mediate the targeted therapeutic or behavioural effects (Chiasson et al., 2023). Thus, to expect an influence on behaviour, one should first evaluate the success of the targeted EEG modulation. Classically, the EEG modulation is expressed in terms of EEG changes at three levels: within sessions (i.e., trial-by-trial), between sessions (i.e., longitudinal changes across
training days), and by comparing EEG activity at rest before and after the EEG-NF protocol (Ros et al., 2020). Yet, these forms of EEG modulation are inconsistently observed altogether in empirical studies, and there is no consensus on the most relevant outcome to evaluate it (Dempster & Vernon, 2009; Egner et al., 2004; Mirifar et al., 2022; Schabus, 2017, 2018; Witte et al., 2018). Moreover, there are substantial methodological variations between EEG-NF protocols, including differences in feedback implementation, instructions, protocol length, trial structure, and whether or not a resting baseline is used to define positive feedback (Chiasson et al., 2023; Hasslinger et al., 2022; Muñoz-Moldes & Cleeremans, 2020). These discrepancies are currently not much considered in the field, although they may impact the EEG features targeted by the trainings (Strehl, 2014). As such, it is not surprising that 15-50% of EEG-NF participants fail to modulate their EEG activity no matter the protocol (Alkoby et al., 2018; Kadosh & Staunton, 2019; Wan et al., 2014). Overall, a common explanation of these issues may lie in the persistent lack of knowledge about the mechanisms underlying the targeted EEG changes in EEG-NF protocols (Fovet et al., 2016). If one hopes to induce clinical/behavioural changes through the specific modulation of underlying EEG features, these mechanisms should be of main interest and better understood (Micoulaud-Franchi & Fovet, 2016).

Building on these current issues, we recently developed a passive EEG paradigm that mimicked the structure and environment of an EEG-NF session, without involving real-time EEG feedback nor explicit EEG self-regulation instructions (Maaz et al., 2025). In this study, healthy participants were simply instructed, during multiple trials, to fixate a grey circle that either remained unchanged (control condition) or was continuously updated by size, either at 1, 5 or 10 Hz (experimental conditions). The size variations were driven by pre-recorded alpha band (8-12 Hz) power from a different group of participants, similarly to a sham feedback (Sorger et al., 2019). Results showed a spontaneous and consistent increase in frontal, central and parietal alpha power across trials, no matter if the circle was continuously updated or not.
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Additionally, an increase in theta power at Pz was observed when comparing conditions during which the stimulus was continuously updated relatively to the control, no-update condition.

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Importantly, such spectral changes in commonly targeted EEG-NF features may, at least in part, non-specifically explain the effects observed in genuine EEG-NF sessions. Alpha, in particular, is considered as one of the most responsive spectral features to EEG-NF self-regulation (Belinskaia et al., 2020; Chikhi et al., 2023; Naas et al., 2019; Rogala et al., 2016). However, these recent findings suggest that the observed within-session alpha upregulation through EEG-NF is not solely elicited by its hypothesised self-regulation by the participants (Maaz et al., 2025). Rather, it may be due to the non-specific tendency of alpha power to increase with time-on-task (Arnau et al., 2020; Benwell et al., 2019; Kopčanová et al., 2025).

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Additionally, a very common practice to determine the positive feedback presented to the participant is to measure, prior training, a “baseline” of the targeted feature during a resting period without feedback (e.g., Agnoli et al., 2018; Gonçalves et al., 2018; Maszczyk et al., 2020). The theta increase triggered by the presentation of a continuously updated stimulus calls into question the validity of this approach. This emphasises the necessity for the field to establish common evidence-based standards to design EEG-NF protocols (Chiasson et al., 2023; Strehl, 2014).

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The present study investigated whether the EEG power of frequency bands commonly targeted by EEG-NF is influenced by: (i) the repeated presentation of the same visual stimulus over time (i.e., grey circle as pseudo visual feedback), (ii) the presence of a continuous feedback update within trials, (iii) the frequency at which the feedback is updated (1, 5 or 10 Hz), and (iv) the source of this feedback update. Particularly, the last point was evaluated by modifying the method used to generate the circle size variations in Maaz et al. (2025). Here, we implemented a visual display that matches a genuine alpha EEG-NF session. As such, the circle size was updated in real time as a visual feedback of participants' own alpha power

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recorded at Pz. Importantly, participants were not instructed to self-regulate their EEG activity, nor informed about the EEG-driven nature of the feedback, resulting in a passive covert EEG-NF paradigm (Muñoz-Moldes & Cleeremans, 2020). Accordingly, we aimed to assess whether the spontaneous EEG changes previously reported still occur in the presence of a closed-loop feedback procedure, yet in the absence of explicit instructions for self-regulation. We evaluated each effect and corresponding interactions on classically targeted EEG features in EEG-NF research, i.e., the spectral power of theta (4-8 Hz), alpha (8-12 Hz), sensorimotor rhythm (SMR; 12-15 Hz) and beta (15-30 Hz) frequency bands (Dessy et al., 2020; Micoulaud-Franchi et al., 2015, 2019; Thibault et al., 2015). We hypothesised to replicate the findings of Maaz et al. (2025): (i) alpha power should spontaneously increase over time, no matter the presence, the frequency and the source of stimulus update (Benwell et al., 2019; Maaz et al., 2025), (ii) the continuous stimulus update should elicit an increase in theta power at Pz (VanRullen, 2016), (iii) the frequency and (iv) the source of stimulus update should not influence any of the considered EEG features.

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# Method

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## Participants

Thirty-two healthy young adults participated in the current study ($M_{age} = 20.53$ years, $SD = 2.73$, age range = 18-28; 25 females; 28 right-handed [self-reported]). Recruitment took place in November 2024 via Aix-Marseille University's educational platform and the laboratory's communication channels. All participants were different from those enrolled in Maaz et al. (2025), reported having normal or corrected-to-normal vision and no history of neurological and/or psychiatric disorders. Twenty-six participants were undergraduate students and received course credit in exchange for their involvement.
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Written and informed consent was obtained from all participants in line with the
Declaration of Helsinki. An anonymous code was assigned to each participant to ensure
confidentiality. The study protocol was approved by the French Personal Protection Committee
(CPP Sud Méditerranée V, ref. 19.09.12.44636).

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**Material and feedback implementation**

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The task consisted of an alpha EEG-NF session with no explicit instructions to self-
regulate any EEG feature. The session included four conditions, each comprising eight 60-
second trials. During each trial, a grey circle was displayed in the centre of a blank screen. In
the control condition, the circle size remained fixed throughout (radius of 100 pixels). In the
other three experimental conditions, the circle size was updated in real time according to the
spectral power of the alpha frequency band (8-12 Hz) at Pz (see the EEG online processing
section for more information). Accordingly, during the trials of the experimental conditions,
the circle size was positively proportional to the participant's alpha power at Pz computed in
real time.

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The feedback update was performed at one of three possible different frequencies, each
of which corresponding to one experimental condition: 1 *Hz*, 5 *Hz*, or 10 *Hz*. These frequencies
were selected based on their prevalence in EEG-NF protocols with healthy adults (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). Apart from the frequency at which the feedback
was updated, all other circle features (colour, maximum and minimum size) were held constant
across trials and conditions.

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To counterbalance the temporal order of conditions, a partial Latin-square design was
employed. For each participant, an in-house Matlab script randomly selected one of four

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predefined quadruplets (1342, 2413, 3124 or 4231) as the condition order, while ensuring an
even distribution across participants.

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## Apparatus

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The EEG-NF session and concurrent EEG data acquisition and online processing were
implemented in Matlab Release 2023a (Mathworks, Inc.) on a DELL Mobile 3571 computer
with an Ubuntu 22.04 OS and a NVIDIA T600 Laptop GPU. Specifically, EEG data was
acquired using the [Brainflow library version 5-8-1](https://brainflow.tbi.ac.at/) and an OpenBCI [Cyton 8-channels board](https://www.openbci.com/) with OpenBCI [Gold cup](https://www.openbci.com/products/gold-cup) and [Earclip](https://www.openbci.com/products/earclip) electrodes. Real time visual feedback was delivered via [Psychtoolbox-3](https://www.openbci.com/products/psychtoolbox-3) (Kleiner et al., 2007) and was displayed on a DELL P2419H flat-screen monitor (resolution of 1920 × 1080 pixels; 60 Hz refresh rate; screen size of 52.704 × 29.646 cm). Participants were seated at a fixed distance of between 90 and 100 cm from the screen to the back of the chair, depending on individual seating needs. EEG offline processing was conducted in Matlab Release 2023a (Mathworks, Inc.). Subsequent statistical analyses and figure generation were performed in R version 4.3.3 (R Core Team, 2024).

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## EEG recording

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EEG was digitised using the OpenBCI board at a sampling rate of 250 Hz. EEG signals
were recorded in microvolts (µV) in Matlab R2023a matrixes from six OpenBCI Gold Cup
electrodes positioned at Fp1, Fpz, Fp2, Fz, Cz, and Pz (in accordance with the 10-20
International System). Two OpenBCI earclip electrodes were positioned on the left and the
right earlobes to serve, respectively, as a reference for all electrodes and as a noise-cancelling
ground electrode. Electrode impedance was maintained under 10 kΩ.
196 **Procedure**

197 Participants were seated in front of the monitor for the duration of the experiment. After
198 obtaining written informed consent, the EEG setup was installed and impedance was checked.
199 Participants were submitted to an alpha EEG-NF session. Importantly, they were not informed
200 that the circle size was actually a feedback of their alpha power. They were also not instructed
201 to self-regulate their EEG activity thanks to this feedback. Instead, participants were repeatedly
202 asked to look at the circle presented on the monitor. Before the session began, the following
203 verbal instructions were given in French: "You will complete 4 blocks of 8 trials, each lasting
204 one minute. During each trial, a circle will be presented in the centre of the screen. During the
205 8 trials of a block, the circle can either remain the same, or its size will be changed continuously
206 at the same rate. Your only task is to keep your eyes on the circle. There are no other particular
207 instructions." Participants were also encouraged to remain still and relaxed during trials to
208 minimise artifacts in the EEG signal. Trials were initiated by pressing the "Enter" key on a
209 keyboard positioned between the participant and the monitor. Participants were allowed to rest
210 between trials at their own pace. At the end of the session, participants were invited to rate their
211 feeling of control on the circle size variations based on a 5-point Likert scale (from 1 - "Not at
212 all" to 5 - "Totally"; see Supplementary Table 1 for the repartition of responses among
213 participants). Throughout the session, the experimenter (JM) stayed in the room but positioned
214 out of the participant's view.

215 **EEG online processing**

216 During the trials of experimental conditions, the spectral power of the alpha frequency
217 band (8-12 Hz) was computed in real time from Pz electrode. Alpha power was then used to
218 continuously update the feedback presented to the participant (i.e., circle size). To this end, a
219 time-frequency analysis (moving window short-FFT) was performed following the
recommendations of Keil et al. (2022; see Supplementary Table 2 for the corresponding completed checklist). At each step, zero-phase filtering was first performed with a 1-20 Hz bandpass filter (4th order IIR Butterworth) using the Matlab `filtfilt` function. Then, spectral power estimates were computed in decibels (dB) from a 2-second (500 samples) symmetric Hann window using the Matlab `pspectrum` function. Alpha power was obtained by averaging spectral estimates within the 8-12 frequency range. Depending on the condition, the computation of alpha power have been done at different frequency rates to match the ones of the feedback update: 1 Hz, 5 Hz, or 10 Hz. For each experimental condition, the overlap between two consecutive analysing windows has been determined accordingly by subtracting the analysing window size (500 samples) by the EEG frequency sample (250 Hz) divided by the frequency of feedback update (1 Hz, 5 Hz, or 10 Hz). 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). To enable the feedback to be displayed from the start of each trial, EEG acquisition and the time-frequency analysis began three seconds before the trial onset.

## EEG offline processing

EEG data preprocessing and spectral analyses were conducted in line with the recommendations of Keil et al. (2022; see Supplementary Table 3 for the corresponding completed checklist). Custom Matlab scripts and the EEGLAB toolbox (Delorme & Makeig, 2004) were used to extract spectral power in the theta (4-8 Hz), alpha (8-12 Hz), SMR (12-15 Hz) and beta (15-30 Hz) frequency bands. As neurofeedback research typically does not rely on explicit models of the oscillatory activity and the 1/f noise (Enriquez-Geppert et al., 2017), we adopted the narrowband model for analyses (Keil et al., 2022). EEG preprocessing began with zero-phase filtering applied on data from each electrode and trial. A high-pass filter at 0.5 Hz (6th order IIR Butterworth) and a notch filter at 50 Hz (2nd order IIR) were implemented
244 using the Matlab `filtfilt` function. This function avoids phase distortion by applying the filter
245 forward and backward. Subsequently, filtered data was imported in EEGLAB, where an artifact
246 correction was performed using the extended Infomax Independent Component Analysis (ICA;
247 Delorme et al., 2007). ICA components associated with ocular artifacts (i.e., eye blinks and
248 lateral eye movements) were visually identified and removed based on their scalp topography,
249 time series, and power spectrum. One to three components were removed per participant (see
250 Supplementary Table 4 for details). The filtered and artifact-corrected EEG data was exported
251 back in Matlab format.

252 Subsequent analyses were restricted to data from Fz, Cz, and Pz electrodes. EEG signals
253 were analysed in the frequency domain using the Matlab `pspectrum` function. This function
254 computes power spectra using FFT and Welch's method to enhance the robustness of spectral
255 estimates. The function automatically determines the segment length to yield up to eight
256 overlapping segments (50% overlap), which are each windowed with a Hamming function
257 prior to FFT computation. The final spectral estimates reflect the average across these
258 segments. For the current analyses, the frequency resolution applied was approximately 0.305
259 Hz. The spectral analyses were performed separately for each trial and participant and then
260 converted to dB to favour a normal distribution of spectral power estimates. Finally, power
261 estimates within each frequency band of interest (4-8 Hz, 8-12 Hz, 12-15 Hz, and 15-30 Hz)
262 were averaged to obtain the spectral power of theta, alpha, SMR and beta frequency bands,
263 respectively.

## 264 Experiment of Maaz et al. (2025)

265 In Maaz et al. (2025), 32 healthy young adults underwent a highly similar experiment
266 to that of the current study. Particularly, EEG acquisition and processing methods were
267 identical. The full description of the rational, method, and results can be found in the original
paper. The only difference between the two experiments lies in the source used for the
continuous update of circle size during the experimental conditions. Compared to the alpha
EEG-NF procedure used in the current experiment, in Maaz et al. (2025)'s experiment, the
circle size was continuously updated based on alpha power from previously recorded EEG data
(i.e., similarly to a "sham" procedure; Thibault & Raz, 2017). By comparing the present study
to the one of Maaz et al. (2025), we hereby manipulate the source of feedback update (i.e.,
circle variations, either from 'Online alpha' for the current study, or 'Offline alpha' for Maaz
et al., 2025).

276 **Statistical analyses and hypotheses testing**

The resulting twelve dependent variables (i.e., spectral power of theta, alpha, SMR and beta frequency bands measured by Fz, Cz, and Pz electrodes) were z-score standardised across subjects of each experiment and used for statistical analyses. For the purpose of this study, we arbitrary hypothesised the presence of each effect of interest on these variables, i.e., that their spectral power is influenced by (i) trial repetition, (ii) the continuous update of the feedback, (iii) the frequency of feedback update, and (iv) the source of feedback update.

To evaluate these effects, Bayesian linear multilevel modelling was used with the *brms*
and *rstan* R packages (Bürkner, 2017; Stan Development Team, 2024). Bayesian methods offer
several advantages over traditional frequentist approaches (Schad et al., 2021, 2023), including
increased robustness to low-power situations (Schönbrodt & Wagenmakers, 2018), facilitated
development and fit of multilevel models (Gelman et al., 2014), the possibility to distinguish
sensitive from insensitive evidence for an absence of effect (i.e., the null hypothesis *H*<sub>0</sub>, Dienes
& Mclatchie, 2018), and the robustness to multiple comparison issues (Gelman et al., 2012).
Each of the above mentioned dependent variables was considered as continuous in one model.
Each model included the maximal varying effect structure to account for the individual
variability of subjects (Barr et al., 2013). The Trial number continuous predictor (i.e., integers from 1 to 8), the Condition categorical predictor (i.e., Control, 1 Hz, 5 Hz, and 10 Hz), the Source categorical predictor (i.e., Online alpha, and Offline alpha), as well as their interaction were included in each model as fixed effects. For the Trial continuous predictor, we defined the first trial as a reference for comparison. For the Condition and Source categorical predictors, we followed Schad et al. (2020) guidelines to determine custom contrast matrixes for hypotheses testing via the generalised inverse (respectively presented in Supplementary Table 5 and Supplementary Table 6). In particular, assigning a custom contrast matrix to the ‘Condition’ variable enabled us to test for an effect of both the continuous feedback update (i.e., Experimental conditions vs. Control) and the frequency of the feedback update (i.e., 5 Hz vs. 1 Hz, and 10 Hz vs. 5 Hz). To constrain the models to plausible values and to avoid overfitting issues, we placed regularising priors of N(0, 1) on each parameter (Schad et al., 2021). Here is the full model equation (brms package syntax):

Power ~ 1 + Trial * Condition * Source + (1 + Trial * Condition|Subject) (1)

For each effect of interest, we computed Bayes Factors (BFs) to quantify the strength of evidence for an hypothesis over another (Dienes & Mclatchie, 2018). To ensure we used enough Markov chain Monte Carlo (MCMC) draws to estimate stable BFs, we performed all reported statistical analyses three times (Schad et al., 2023). For each effect of interest, we report the mean of the three obtained posterior distributions, along with the largest limits of the 95% credible interval (CrI). We also report the mean of the obtained BF10 quantifying evidence for the presence of an effect (alternative hypothesis) over its absence (null hypothesis). As defined by Jeffrey (1939), we consider that a BF10 of above 3 indicates substantial evidence for the alternative over the null hypothesis, and that a BF10 of below 1/3 substantial evidence for the null over the alternative hypothesis. A BF10 between 1/3 and 3 indicates data insensitivity to distinguish null and alternative hypotheses (Dienes, 2014). When an effect was confirmed (i.e.,
317 BF<sub>10</sub> > 3), we also reported the BF<sub>10+</sub> quantifying the amount of evidence for a positive- 318 directional (i.e., one-sided) effect. Estimates (standardised units) and BFs from each of the 319 models regarding the predictors of interest are presented in Supplementary Table 7.

320 Finally, as we previously found an increase in alpha power over the whole course of 321 the task of Maaz et al. (2025; no matter the conditions), we similarly evaluated in the longer 322 run the time evolution of each frequency bands of interest. We reproduced the same statistical 323 procedure and computed the same models (Equation 1) by excluding the Condition predictor 324 and corresponding interactions. The Trial continuous predictor resulted in integers from 1 to 325 326 and the Source predictor remained the same. Estimates and BFs of these models are 327 presented in Supplementary Table 8. Here is the corresponding model equation (brms package 328 syntax):

$$329 \qquad Power \sim 1 + Trial * Source + (1 + Trial|Subject) \qquad (2)$$

## 329 Results

### 330 Alpha power increases over time

331 Fig. 1 shows how each frequency band (i.e., theta, alpha, SMR, beta) spectral power 332 evolved over the eight trials of the control condition (in which the circle size remain constant) 333 in both experiments (i.e., offline vs. online alpha). Within the control condition, we observed 334 extreme evidence for a positive effect of trial repetition on the alpha spectral power at Fz ($\beta$ = 335 0.016, 95% CrI [0.01, 0.022], BF<sub>10</sub> > 100, BF<sub>10+</sub> > 100), Cz ($\beta$ = 0.017, 95% CrI [0.011, 0.023], 336 BF<sub>10</sub> > 100, BF<sub>10+</sub> > 100) and Pz ($\beta$ = 0.018, 95% CrI [0.011, 0.024], BF<sub>10</sub> > 100, BF<sub>10+</sub> > 100). 337 These findings indicate that alpha power at Fz, Cz and Pz increases at each trial of the control 338 condition (Fig. 1D-F). For the other frequency bands of interest, we found substantial to 339 extreme evidence for the absence of an effect (Fig. 1A-C,G-I,K-L), except for the beta spectral
power at Fz for which the BF<sub>10</sub> was insensitive (β = 0.013, 95% CrI [0.005, 0.022], BF<sub>10</sub> = 0.68; see Fig. 1J). Table 1 reports the model estimates for the trial repetition parameter.

<table><caption>Table 1 Estimates of the trial repetition effect within the control condition.</caption><thead><tr><th>Frequency Band</th><th>Electrode</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>0,01</td><td>0,004</td><td>0,017</td><td>0.329</td><td>&gt; 100</td></tr><tr><td><strong>Alpha</strong></td><td><strong>Fz</strong></td><td><strong>0,016</strong></td><td><strong>0,01</strong></td><td><strong>0,022</strong></td><td><strong>&gt; 100</strong></td><td><strong>&gt; 100</strong></td></tr><tr><td>SMR</td><td>Fz</td><td>0,006</td><td>0</td><td>0,012</td><td>0.02</td><td>36.418</td></tr><tr><td>Beta</td><td>Fz</td><td>0,013</td><td>0,005</td><td>0,022</td><td>0.68</td><td>&gt; 100</td></tr><tr><td>Theta</td><td>Cz</td><td>0,008</td><td>0,001</td><td>0,015</td><td>0.045</td><td>80.367</td></tr><tr><td><strong>Alpha</strong></td><td><strong>Cz</strong></td><td><strong>0,017</strong></td><td><strong>0,011</strong></td><td><strong>0,023</strong></td><td><strong>&gt; 100</strong></td><td><strong>&gt; 100</strong></td></tr><tr><td>SMR</td><td>Cz</td><td>0,003</td><td>-0,002</td><td>0,009</td><td>0.006</td><td>8.98</td></tr><tr><td>Beta</td><td>Cz</td><td>0</td><td>-0,007</td><td>0,006</td><td>0.003</td><td>0.846</td></tr><tr><td>Theta</td><td>Pz</td><td>0,01</td><td>0,003</td><td>0,018</td><td>0.128</td><td>&gt; 100</td></tr><tr><td><strong>Alpha</strong></td><td><strong>Pz</strong></td><td><strong>0,018</strong></td><td><strong>0,011</strong></td><td><strong>0,024</strong></td><td><strong>&gt; 100</strong></td><td><strong>&gt; 100</strong></td></tr><tr><td>SMR</td><td>Pz</td><td>0,004</td><td>-0,001</td><td>0,009</td><td>0.007</td><td>12.163</td></tr><tr><td>Beta</td><td>Pz</td><td>-0,006</td><td>-0,014</td><td>0,001</td><td>0.014</td><td>0.058</td></tr></tbody></table>

Each model reported has been computed three times to ensure the stability of the BFs. If not specified, each numerical value corresponds to the average of the values obtained across these three model computations. The ‘Estimate’ column stands for the estimated group-level effect (slope) of the ‘Trial’ predictor considered in a model (in z-score standardised units). The ‘Lower’ and ‘Upper’ columns correspond to the minimal lower and maximal upper bounds of the three 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 bold 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).
A

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

B

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

Figure 1 Trial repetition effect on EEG spectral power depending on the source of feedback update. (A) BF₁₀ quantifying the evidence in favour of the presence, over the absence, of the trial repetition effect on theta (4-8 Hz), alpha (8-12 Hz), SMR (12-15 Hz) and beta (15-30 Hz) spectral power, respectively. On each plot, the dashed horizontal line at y = 3 indicates the level of substantial evidence in favour of an effect, whereas the dashed horizontal line at y = 1/3 indicates substantial evidence for no effect. (B) Evolution of theta, alpha, SMR and beta spectral power (respectively across columns) at Fz, Cz and Pz (respectively across rows) across the trials of the control condition. Each line point represents the EEG spectral

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power averaged at the group-level in function of the source of feedback update (light pink: online alpha; dark blue: offline alpha). Error bars indicate 95% confidence intervals.

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No effect of the continuous update of feedback, nor of the frequency and of
the source of feedback update

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Fig. 2 shows, depending on the source of feedback update, the spectral power of the considered frequency bands (i.e., theta, alpha, SMR, beta) during the first trial of each condition (i.e., Control, 1 Hz, 5 Hz and 10 Hz). When comparing the experimental conditions to the control (i.e., effect of the presence of feedback update), we found substantial to strong evidence supporting an absence of effect on theta power at Fz (Fig. 2A), as well as on alpha (Fig. 2D-F) and beta power (Fig. 2J-L). Insensitive $BF_{10}$ were obtained for the effect on SMR power (Fig. 2G-I) at Fz ($\beta$ = 0.091, 95% CrI [0.031, 0.152], $BF_{10}$ = 2.338), Cz ($\beta$ = 0.077, 95% CrI [0.017, 0.138], $BF_{10}$ = 0.707) and Pz ($\beta$ = 0.068, 95% CrI [0.007, 0.128], $BF_{10}$ = 0.354), and on theta power (Fig. 2B-C) at Cz ($\beta$ = 0.074, 95% CrI [0.018, 0.131], $BF_{10}$ = 0.755) and Pz ($\beta$ = 0.081, 95% CrI [0.021, 0.141], $BF_{10}$ = 1.059).

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Concerning the effect of both the frequency and the source of feedback update, substantial to strong evidence supported the absence of an effect on most of the considered frequency bands. Exceptions concern the effect of the frequency of feedback update, in particular when comparing 10 Hz to 5 Hz conditions for which insensitive evidence was obtained at Cz for SMR ($\beta$ = 0.076, 95% CrI [0.01, 0.142], $BF_{10}$ = 0.432) and beta power ($\beta$ = 0.105, 95% CrI [0.022, 0.187], $BF_{10}$ = 0.95).

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Similarly, for all interactions effects, we found substantial to extreme evidence for no effect on most of the considered frequency bands. The only exceptions concern the interaction between the feedback update and the source of feedback update, quantifying insensitive evidence for the effect on theta at Cz ($\beta$ = 0.074, 95% CrI [0.017, 0.131], $BF_{10}$ = 0.713) and Pz ($\beta$ = 0.085, 95% CrI [0.025, 0.145], $BF_{10}$ = 1.457). Overall, these findings suggest that alpha

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power increases over time, regardless of whether feedback is updated (Experimental conditions
vs. Control), how frequently it is updated (1, 5, or 10 Hz), or its source (online vs. offline alpha).

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All the model estimates and BFs of the parameters reported in this section are presented in Supplementary Table 7.

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<figure><img src="image_6.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_7.png" /></figure>

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**Figure 2 EEG spectral power at the first trial of each condition in both experiments. (A)**

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BF₁₀ quantifying the evidence in favour of the presence, over the absence, of the feedback update effect on theta (4-8 Hz), alpha (8-12 Hz), SMR (12-15 Hz) and beta (15-30 Hz) spectral power, respectively. On each plot, the dashed horizontal line at y = 3 indicates the level of substantial evidence in favour of an effect, whereas the dashed horizontal line at y = 1/3 indicates substantial evidence for no effect. (B) Spectral power of theta, alpha, SMR and beta spectral power (respectively across columns) at Fz, Cz and Pz (respectively across rows) at the first trial of each condition. Each line point represents the EEG spectral power averaged at the
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group-level in function of the source of feedback update (light pink: online alpha; dark blue: offline alpha). Error bars indicate 95% confidence intervals.

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**Alpha increase is maintained throughout the session no matter the source of feedback update**

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In this section, we report the results of the models taking into account the trial repetition effect over the entire session, no matter the condition (Equation 2). Fig. 3 shows the evolution, depending on the source of feedback update, of the considered frequency bands (i.e., theta, alpha, SMR and beta) over the 32 trials of the experiment. We found extreme evidence that the alpha power at Fz ($\beta$ = 0.007, 95% CrI [0.004, 0.01], $BF_{10} > 100$, $BF_{10+} > 100$), Cz ($\beta$ = 0.007, 95% CrI [0.004, 0.01], $BF_{10} > 100$, $BF_{10+} > 100$), and Pz ($\beta$ = 0.009, 95% CrI [0.006, 0.012], $BF_{10} > 100$, $BF_{10+} > 100$) increased throughout the entire session in both experiments (Fig. 3D-F). Substantial to extreme evidence supported the absence of trial repetition effect on the remaining frequency bands (Fig. 3A-B,G-L), except for the theta power at Pz for which the amount of evidence was insensitive ($\beta$ = 0.006, 95% CrI [0.003, 0.009], $BF_{10} = 0.792$; see Fig. 3C). Model estimates are presented in Table 2.

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Concerning the effect of the source of feedback update, as well as the interaction with trial repetition, we obtained substantial to extreme evidence of no effect on all considered frequency bands, except for the interaction effect on alpha power at Pz ($\beta$ = -0.005, 95% CrI [-0.007, -0.002], $BF_{10} = 0.473$). Overall, these results indicate that, throughout the entire session, the source of feedback update does not influence the power of theta, alpha, SMR and beta frequency bands. More importantly, the increase in alpha power over time is observed no matter the source of feedback update.
<figure><img src="image_8.png" /></figure>

**Figure 3 Evolution of EEG spectral power throughout the session.** (A) *BF*<sub>10</sub> quantifying the evidence in favour of the presence, over the absence, of the trial repetition effect (throughout the session) on theta (4-8 *Hz*), alpha (8-12 *Hz*), SMR (12-15 *Hz*) and beta (15-30 *Hz*) spectral power, respectively. On each plot, the dashed horizontal line at *y* = 3 indicates the level of substantial evidence in favour of an effect, whereas the dashed horizontal line at *y* = 1/3 indicates substantial evidence for no effect. (B) Evolution of theta, alpha, SMR and beta spectral power (respectively across columns) at Fz, Cz and Pz (respectively across rows) throughout the session. Each line point represents the EEG spectral power averaged at the group-level in function of the source of feedback update (light pink: online alpha; dark blue: offline alpha). Error bars indicate 95% confidence intervals. Vertical dashed lines mark the first trial of each condition.

<table><caption>**Table 2 Estimates of the trial repetition effect throughout the entire session.**</caption><thead><tr><th>Frequency Band</th><th>Electrode</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>0,006</td><td>0,003</td><td>0,009</td><td>0.792</td><td>&gt; 100</td></tr></tbody></table>
<table><tbody><tr><td><strong>Alpha</strong></td><td><strong>Fz</strong></td><td><strong>0,007</strong></td><td><strong>0,004</strong></td><td><strong>0,01</strong></td><td><strong>&gt; 100</strong></td><td><strong>&gt; 100</strong></td></tr><tr><td>SMR</td><td>Fz</td><td>0,003</td><td>0,001</td><td>0,005</td><td>0,026</td><td>&gt; 100</td></tr><tr><td>Beta</td><td>Fz</td><td>0,005</td><td>0,002</td><td>0,008</td><td>0,121</td><td>&gt; 100</td></tr><tr><td>Theta</td><td>Cz</td><td>0,003</td><td>0</td><td>0,006</td><td>0,011</td><td>34.99</td></tr><tr><td><strong>Alpha</strong></td><td><strong>Cz</strong></td><td><strong>0,007</strong></td><td><strong>0,004</strong></td><td><strong>0,01</strong></td><td><strong>&gt; 100</strong></td><td><strong>&gt; 100</strong></td></tr><tr><td>SMR</td><td>Cz</td><td>0,002</td><td>0</td><td>0,004</td><td>0,005</td><td>24.922</td></tr><tr><td>Beta</td><td>Cz</td><td>0,001</td><td>-0,001</td><td>0,004</td><td>0,002</td><td>6.62</td></tr><tr><td>Theta</td><td>Pz</td><td>0,006</td><td>0,002</td><td>0,009</td><td>0,253</td><td>&gt; 100</td></tr><tr><td><strong>Alpha</strong></td><td><strong>Pz</strong></td><td><strong>0,009</strong></td><td><strong>0,006</strong></td><td><strong>0,012</strong></td><td><strong>&gt; 100</strong></td><td><strong>&gt; 100</strong></td></tr><tr><td>SMR</td><td>Pz</td><td>0,002</td><td>0</td><td>0,004</td><td>0,014</td><td>80.223</td></tr><tr><td>Beta</td><td>Pz</td><td>0</td><td>-0,003</td><td>0,003</td><td>0,001</td><td>0.874</td></tr></tbody></table>

Each model reported has been computed three times to ensure the stability of the BFs. If not specified, each numerical value corresponds to the average of the values obtained across these three model computations. The ‘Estimate’ column stands for the estimated group-level effect (slope) of the ‘Trial’ predictor considered in a model (in z-score standardised units). The ‘Lower’ and ‘Upper’ columns correspond to the minimal lower and maximal upper bounds of the three 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 bold 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).

## Discussion

The present study aimed to assess whether spontaneous EEG changes observed in a previous pseudo-neurofeedback task (Maaz et al., 2025) would replicate in a genuine EEG-NF session using real-time alpha feedback, but without explicitly engaging participants into its self-regulation. We investigated whether the spectral power of commonly targeted frequency bands in EEG-NF (i.e., theta, alpha, SMR, and beta) was influenced by the repeated presentation of a visual stimulus, the presence of its continuous update, the frequency and the source of this update (online vs. offline alpha power). In line with our hypotheses and previous results, we found that alpha power increased across trials at all electrode sites, regardless of the presence, frequency, or source of the feedback update. However, we did not replicate the previous finding of increased theta power at Pz relative to the presence of a continuously updated visual stimulus. No other effects were found for SMR or beta power.
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When assessing the influence of the source of the visual feedback update (online alpha
in the current experiment vs. offline alpha in Maaz et al., 2025) on the alpha power increases,
results revealed no effect of the source of feedback update, as well as an absence of interaction
with trial repetition. Importantly, the same conclusions were drawn when analysing the alpha
evolution over the whole session, without taking into account the Condition predictor (Equation
2; see Fig. 2D-F). Thus, in EEG-NF settings, alpha power tends to exhibit a positive non-
stationarity which cannot be attributed to the visual stimulus presented, its continuous updated
over time, the frequency of this update nor whether it is EEG-driven or not.

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The alpha band is one of the EEG features most frequently targeted in EEG-NF history
(Belinskaia et al., 2020; Micoulaud-Franchi et al., 2019; Nowlis & Kamiya, 1970). Alpha
activity has been widely linked with relaxation and meditation (Bing-Canar et al., 2016;
Magosso et al., 2019). Alpha upregulation through EEG-NF has been observed within and
between EEG-NF sessions (Belinskaia et al., 2020; Chikhi et al., 2023, 2024; Dempster &
Vernon, 2009; Escolano et al., 2014; Grosselin et al., 2021; Naas et al., 2019; Nowlis &
Kamiya, 1970; Su et al., 2021; Zoefel et al., 2011), as well as during resting-state EEG before
and after EEG-NF training (Dekker et al., 2014; Lee et al., 2019). Consequently, this effective
modulation is considered as responsible for alpha EEG-NF effectiveness as a non-
pharmacological treatment of post-traumatic stress disorders (PTSD; Kluetsch et al., 2014),
depression and anxiety (Hardt & Kamiya, 1978; Peeters et al., 2014), headaches (Stokes &
Lappin, 2010) and even tinnitus (Jensen et al., 2023). In healthy individuals, alpha modulation
is also targeted to enhance memory (Naas et al., 2019; Yeh et al., 2021), attention (Bagherzadeh
et al., 2020; Berger & Davelaar, 2018) and creative (Agnoli et al., 2018) performances.

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In the current study, the alpha power increased in a situation where participants were
not explicitly engaged in any EEG self-regulation (i.e., passive feedback visualisation). Still,
they were provided with genuine sensory feedback of their real-time alpha power. These results
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are particularly congruent with previous alpha upregulation in the studies of Dekker et al.

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(2014) and van Boxtel et al. (2012). During fifteen consecutive EEG-NF daily-sessions, healthy

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participants were provided with an alpha auditory feedback. Importantly, participants were not

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explicitly instructed to self-regulate their alpha thanks to the feedback, as in the current study.

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Yet, alpha increases were observed within and between EEG-NF sessions, as well as at rest

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before and after training. These results were interpreted as successful alpha upregulation

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through EEG-NF, suggesting that the mere presentation of a sensory feedback is sufficient to

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drive the targeted EEG modulation. In that case, instructing participants to self-regulate their

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EEG activity would not be necessary.

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However, alpha power is known to elicit upward non-stationarities over time,

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independently of any experimental manipulation (Benwell et al., 2019; Kopčanová et al., 2025;

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Maaz et al., 2025). As a result, without properly controlling for these non-specific influences,

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one cannot confidently conclude that the observed alpha evolution during genuine EEG-NF

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alone reflects successful EEG modulation. Here, we systematically compared the alpha

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trajectories in the present genuine EEG-NF settings with an independent group (Maaz et al.,

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2025). This group underwent through almost identical experimental conditions to those of the

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present group. They were repeatedly presented with either a fixed or a continuously modified

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circle, without explicit instructions to self-modulate their EEG activity thanks to the circle

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variations. The only between-group difference was the source of feedback update, i.e., real-

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time alpha power at Pz for the current study and prerecorded alpha trajectories for Maaz et al.

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(2025). Critically, Bayesian analyses confirmed the absence of any effect of the source of

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feedback update on alpha power, as well as an absence of interaction with trial repetition. These

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findings suggest that the present and previously observed alpha power increases during EEG-

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NF (Dekker et al., 2014; van Boxtel et al., 2012) may occur independently of a genuine closed-
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loop feedback procedure. Rather, previously acknowledged alpha non-stationarities most likely
drove, non-specifically, the targeted alpha upregulation (Maaz et al., 2025).

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Namely, to control non-specific influences on both EEG and clinical/behavioural
outcomes, sham EEG-NF protocols are considered as the gold standard control condition/group
(Ros et al., 2020; Thibault & Raz, 2017; although see Sorger et al., 2019 for a comprehensive
review highlighting alternative controls). Everything else being equal, the only difference
between sham and genuine EEG-NF protocols is the presence or absence of a link between the
sensory feedback presented to the participants and the targeted EEG feature(s). In sham
protocols, without informing the participant, the sensory feedback is disconnected from the
targeted EEG feature(s), which rules out the possibility of self-regulation (Sorger et al., 2019).

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Therefore, if EEG modulation occurs through the hypothesised self-regulation mechanism,
genuine EEG-NF protocols should produce superior EEG changes (Thibault & Raz, 2018).

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However, as the entire field, alpha EEG-NF studies do not systematically implement a sham
(or even an active control) condition/group (Chikhi et al., 2023; Dekker et al., 2014; Lee et al.,
2019; Yeh et al., 2021; Zoefel et al., 2011). While some exceptions exist (Escolano et al., 2014;
Grosselin et al., 2021), alpha increases has also been observed independently of its possibility
to be trained (Belinskaia et al., 2020; Dessy et al., 2020; Jiang et al., 2021; Naas et al., 2019).

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These observations align with the present results. By manipulating the source of feedback
update, this study ensured that the targeted alpha upregulation was effectively due to the
presence of a closed-loop feedback procedure. To this end, the sample from Maaz et al. (2025)
was used as a sham-like control group. Overall, these findings highlight the necessity for EEG-
NF to control non-specific influences which can mascaraed as successful EEG modulation.

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Furtherly, while sham EEG-NF is considered as a gold-standard control, both genuine
and sham protocols comprise a variety of factors which can influence EEG activity. Typically,
sham protocols are designed to control for the influence of non-specific cognitive (engagement

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in self-regulation), psychosocial (motivation, interaction with the practitioner, expectations, general neurotechnological context) or general repetition-related factors confounded in the genuine EEG-NF procedure (Thibault & Raz, 2018). By comparing genuine and sham protocols, one can consequently infer whether the observed effects are due to EEG self-regulation or to non-specific cognitive, psychosocial and/or repetition-related factors. As such, EEG changes have been reported to occur in sham protocols (e.g., Belinskaia et al., 2020; Naas et al., 2019; Schabus et al., 2017). However, when using sham as a stand-alone control, the cognitive, psychosocial or repetition-related nature of these non-specific influences remain to be determined. By additionally using a similar control group to the one of the present study, one can distinguish between non-specific influences related to time-on-task to higher-level cognitive and psychosocial factors confound in the sham procedure.

Importantly, the question of non-specific repetition-related influences is particularly relevant for alpha EEG-NF. Outside of EEG-NF context, alpha activity is considered as an active mechanism for inhibiting or gating sensory input, playing a central role in attention and memory processes (Klimesch, 1999, 2012; Klimesch et al., 2007). More broadly, alpha is known as exhibited vs. inhibited depending on the internal (i.e., self-focused) vs. external (i.e., environment-focused) focus of attention, respectively (Hanslmayr et al., 2011; Sauseng et al., 2005; Wang et al., 2016). Unsurprisingly, alpha activity is enhanced during episodes of mind wandering, defined as a switch of attention towards internal thoughts unrelated to the task in hand (Arnau et al., 2020; Compton et al., 2019). In addition, the occurrence of mind wandering increases with time-on-task (Compton et al., 2024; Gouraud et al., 2018). Thus, the present increases in alpha power over time, as in broader cognitive tasks (Arnau et al., 2020, 2021; Benwell et al., 2019), might be elicited by an increasing internal focus of attention during mind wandering episodes. As a result, this phenomenon may, at least in part, explain the EEG
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changes in terms of enhanced alpha power in both sham and genuine EEG-NF protocols (e.g., Belinskaia et al., 2020).

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Moreover, this study investigated whether presenting a continuously updated visual stimulus (vs. presenting the same unmodified stimulus over time) has an influence on the spectral power of frequency bands classically targeted by EEG-NF (i.e., theta, alpha, SMR and beta). This question is highly relevant given the current lack of methodological standards in the field when designing EEG-NF protocols (Chiasson et al., 2023; Gruzelier, 2014b; Hasslinger et al., 2022; Strehl, 2014). In particular, to determine when to provide positive feedback to participants (i.e., indication of success in self-modulation at a given time point), a common practice is first to measure the targeted EEG feature during a resting-state “baseline”, during which participants usually fixate passively on a cross. During training, if the real-time computed EEG feature exceeds a threshold determined by its baseline value, positive feedback is given to the participant (e.g., Agnoli et al., 2018; Enriquez-Geppert et al., 2017; Gonçalves et al., 2018; Maszczyk et al., 2020). However, this approach can be problematic, since EEG-NF training involves active participant engagement in self-regulation, unlike resting-state baseline measures. This engagement can influence the targeted EEG feature through the involvement of higher cognitive processes (Gaume et al., 2016; Micoulaud Franchi et al., 2020). Additionally, the perception of a continuously modified stimulus can alter EEG spectral densities, in particular in the theta and alpha frequency bands (e.g., Spaak et al., 2014; VanRullen, 2016). If so, the determination of positive feedback during training may be biased, resulting in an unclear evaluation of the subsequent EEG modulation. In our previous work, we found that the presentation of a continuously updated visual stimulus, compared to no modification, elicits an increase in theta power at Pz (Maaz et al., 2025). We therefore highlighted the need to change the current method to determine positive feedback in threshold-based approaches, in particular for protocols focusing on theta upregulation at Pz (e.g., Egner
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& Gruzelier, 2004; Rozengurt et al., 2016). Surprisingly, we did not replicate this finding in
the current study and we cannot actually conclude about the presence or absence of an effect.

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Indeed, the *BF*<sub>10</sub> evaluating the effect of the continuous stimulus update and the corresponding
interaction with the source of feedback update (i.e., the between-group predictor comparing the
two studies) quantified insensitive amount of evidence (see Supplementary Table 7; Dienes &
Mclatchie, 2018; Jeffreys, 1939).

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The failure to replicate this finding could be explained by the main difference between
the two studies, i.e., the source of feedback update. In the present study, the feedback update
was based on the online-computed alpha power of the participant, i.e., similarly to a genuine
EEG-NF procedure. By contrast, Maaz et al. (2025) used pre-recorded alpha power from a pilot
study. Specifically, for each trial during which the circle size was updated, each circle size
variation was determined in real time by sampling at random one possible variation of alpha
power pre-recorded during the pilot. Yet, while successfully providing circle size variations
disconnected from participant EEG activity, this method yielded to suboptimal representation
of alpha temporal dynamics. Therefore, this mismatch in alpha temporal dynamics between
studies might explain the divergent effects on theta, which may be result of distinct visual
information processing.

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Here, in addition to the non-replication of the effect of the continuous feedback update,
we mostly confirmed the absence of influence from either the frequency and the source of
feedback update on EEG features classically targeted by EEG-NF (for few exceptions, for
which we found insensitive evidence, see Supplementary Table 7 and Supplementary Table 8).
We would like to emphasise the reassuring nature of these findings for the field. Indeed, these
effects addressed important methodological issues which did not receive enough consideration
so far (Chiasson et al., 2023; Hasslinger et al., 2022; Witte et al., 2018). In particular, the
frequency of feedback update is not currently standardised in the field. Whether at 1, 5 or 10

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Hz, this frequency may influence the timing of the learning process occurring during EEG-NF sessions (Belinskaia et al., 2020; Fingelkurts & Fingelkurts, 2006; Michel & Koenig, 2018).

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Similarly, whether genuine or sham, the source of feedback update influences *per se* the learning process (Pigott et al., 2017, 2018). The present results suggest that, at least perceptually, the update, the frequency and the source of feedback do not influence EEG features typically targeted. However, contrary to sham and genuine EEG-NF protocols, the present experiment lacked explicit instructions for participants to engage in EEG self-regulation. As such, the extent to which each of the manipulated factors influences EEG features during actual EEG-NF settings (i.e., with explicit instructions for self-regulation) remains to be determined by further studies.

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611

612

613

614

615

Overall, the present study provides valuable methodological considerations for EEG-NF protocols, being reassuring especially concerning the feedback generation procedure (no effect of the presence, the frequency nor the source of feedback update). More importantly, however, the current increase in alpha power over time, which was also reported in a similar context (Maaz et al., 2025), contributes to the broader discussion about the mechanisms underlying EEG-NF (Micoulaud-Franchi & Fovet, 2018; Thibault & Raz, 2017, 2018).

616

617

618

619

620

621

622

623

624

625

626

627

628

629

Specifically, alpha increases occurred independently of the continuous update of the feedback (control vs. experimental conditions), its frequency rate (whether at 1, 5 or 10 Hz) and its EEG-driven nature (i.e., source of feedback update). As such, the present increases in alpha power match the two criteria to reflect spontaneous activity, i.e., not being influenced by the feedback display in a situation during which participants are not activity engaged in the self-regulation task (Ramot & Martin, 2022). While the targeted EEG modulation is traditionally framed as resulting from a self-regulation mechanism (Gaume et al., 2016; Mirifar et al., 2022), the current study suggests that spontaneous EEG dynamics can mimic the patterns expected from successful training in the absence of intentional modulation. This aligns with critiques
630 emphasising the need to distinguish true self-regulation from time-related EEG changes,
631 placebo effects, and protocol artefacts (Schabus, 2017, 2018; Witte et al., 2018). More
632 generally, the present data call for caution in interpreting EEG-NF outcomes without rigorous
633 control conditions, in particular concerning protocols targeting alpha upregulation. Further
634 empirical studies aiming to characterise the EEG modulation underlying mechanisms are
635 encouraged.
636

636 Specifically, a direct comparison of the current alpha increase with the alpha evolution
637 during both genuine EEG-NF and a proper control protocol would help to isolate the
638 mechanisms responsible for alpha modulation (i.e., specific self-regulation, non-specific
639 cognitive and/or psychosocial factors, and/or general non-specific effects such as time-on-
640 task). In terms of control protocols, several possibilities have been previously acknowledged
641 (Sorger et al., 2019). In particular, above-mentioned sham EEG-NF would enable to conclude
642 whether specific self-regulation, acquired through the repeated exposure to genuine feedback,
643 can overcome or equals non-specific influences in terms of EEG changes. Furthermore,
644 comparing an active and a passive sham protocols would enable to dissociate between
645 repetition-related and more higher cognitive/psychosocial non-specific influences (Maaz et al.,
646 2025). Additionally, alternative protocols such as bidirectional control (i.e., manipulating the
647 direction of targeted EEG modulation) can powerfully account for non-specific influences
648 while also enabling a direct evaluation of self-regulation occurrence.
649

## Conclusion
***

650 This study shows that alpha power increases spontaneously over time during an EEG-
651 NF session without instructions and engagement in self-regulation. These increases are similar
652 to those targeted and observed in genuine and sham EEG-NF contexts, supporting the view
653 that spontaneous EEG dynamics and non-specific contextual factors can influence the EEG
654
675

Resources; Supervision; Writing – Review & Editing. **Laurent Waroquier:**

676

Conceptualisation; Methodology; Supervision; Writing – Review & Editing. **Arnaud Rey:**

677

Conceptualisation; Methodology; Project administration; Resources; Supervision; Writing –

678

Review & Editing.

679

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Supplementary Material

for:

Spontaneous modulation of alpha power during a
neurofeedback session without instructions

Jacob Maaz,¹,²,³ Véronique Paban,¹ Laurent Waroquier⁴ and Arnaud Rey¹,³

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Author affiliations:

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

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

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

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

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Correspondence to: Jacob Maaz

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

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CNRS – Aix-Marseille Université

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3, place Victor Hugo – Case D

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13331 Marseille Cedex 3 – France

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jacob.maaz@univ-amu.fr
1090 Supplementary Table 1 Participants' subjective feeling of control on the circle variations
1091 (only the group with online alpha-driven variations).

<table><thead><tr><td>Responses</td><td>Number of answers</td></tr></thead><tbody><tr><td>1 – “Not at all”</td><td>17</td></tr><tr><td>2 – “A little”</td><td>9</td></tr><tr><td>3 – “Mildly”</td><td>6</td></tr><tr><td>4 – “A lot”</td><td>0</td></tr><tr><td>5 – “Totally”</td><td>0</td></tr></tbody></table>

1092 At the end of the session, participants were asked to answer on a 5-point Likert scale to the question: “During the trials in which the circle was modified, to what extent did you feel you had control over the variations of the circle?”. Most of the participants reported no subjective level of control on the circle variations (n = 17), suggesting little explicit knowledge on the link between their EEG activity and the visual feedback. Some also reported subjective feelings of little (n = 9) and moderate (n = 6) control. Yet, this measure was not implemented in the control group from Maaz et al. (2025), in which the feedback was disconnected from participant EEG activity. Thus, without the possibility for a meaningful between-group comparison, little inference can be sustainably made about these results.
<table><caption>1101 Supplementary Table 2 Checklist guidelines for time-frequency analyses (adapted from
1102 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>1103 Supplementary Table 3 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>
1107 Supplementary Table 4 Number of Independent Components removed from the data of
1108 each participant.

<table><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>2</td></tr><tr><td>3</td><td>2</td></tr><tr><td>4</td><td>2</td></tr><tr><td>5</td><td>2</td></tr><tr><td>6</td><td>3</td></tr><tr><td>7</td><td>2</td></tr><tr><td>8</td><td>1</td></tr><tr><td>9</td><td>3</td></tr><tr><td>10</td><td>1</td></tr><tr><td>11</td><td>2</td></tr><tr><td>12</td><td>3</td></tr><tr><td>13</td><td>2</td></tr><tr><td>14</td><td>3</td></tr><tr><td>15</td><td>3</td></tr><tr><td>16</td><td>2</td></tr><tr><td>17</td><td>2</td></tr><tr><td>18</td><td>2</td></tr><tr><td>19</td><td>2</td></tr><tr><td>20</td><td>2</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>2</td></tr><tr><td>25</td><td>2</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>3</td></tr><tr><td>30</td><td>3</td></tr><tr><td>31</td><td>2</td></tr><tr><td>32</td><td>2</td></tr></tbody></table>

1109 All components were identified using the EEGLAB extended Infomax Independent
1110 Component Analysis (ICA) algorithm (Delorme et al., 2007). Independent Components for eye
1111 blinks and lateral eye movements were identified for rejection and subtracted from the data by
1112 visual inspection of the component scalp topography, time series, and power spectrum
1113 distributions. Note that, unusually, three components were removed from the data of nine
1114 participants (participants 6, 9, 12, 14, 15, 21, 28, 29 and 30). This was done because the ICA
1115 algorithm split one of the two typical eye artifact components (one for eye blink and one for
1116 lateral eye movements) into two different components. For example, ICA on participant 9's
1117 data resulted in a duplication of the typical eye blink component.
<table><caption>Supplementary Table 5 Custom-coded contrast matrix assigned to the Condition predictor of each model.</caption><thead><tr><th>Condition labels</th><th>Intercept</th><th>Exp vs. Control<br>(1<sup>st</sup> contrast)</th><th>5 Hz vs. 1 Hz<br>(2<sup>nd</sup> contrast)</th><th>10 Hz vs. 5 Hz<br>(3<sup>rd</sup> contrast)</th></tr></thead><tbody><tr><td>Control</td><td>1</td><td>-3/4</td><td>0</td><td>0</td></tr><tr><td>1 Hz</td><td>1</td><td>1/4</td><td>-2/3</td><td>-1/3</td></tr><tr><td>5 Hz</td><td>1</td><td>1/4</td><td>1/3</td><td>-1/3</td></tr><tr><td>10 Hz</td><td>1</td><td>1/4</td><td>1/3</td><td>2/3</td></tr></tbody></table>

Within the four conditions composing our task, we manipulate the presence (three Exp) or absence (Control) of the continuous modification of the circle size. We also manipulate, depending on the experimental condition, the frequency rate of this continuous modification: 1 Hz, 5 Hz, or 10 Hz. To include both predictors (i.e., the continuous modification of the circle size, and the frequency at which the circle was modified) in our models and relate to our hypotheses testing, we applied the present custom contrast matrix to the categorical predictor 'Condition'. This matrix was obtained by applying the generalised inverse to a *Hypothesis* matrix referring to our hypotheses. The 'Exp vs. Control (1st contrast)' column refers to the hypothesis that there is a difference in spectral power when participants are presented with a continuously modified circle (Exp), relative to when the circle remains the same (Control). The '5 Hz vs. 1 Hz (2nd contrast)' column relates to the hypothesis that there is a difference in spectral power when participants are presented a circle modified at 5 Hz relative to 1 Hz. The '10 Hz vs. 5 Hz (3rd contrast)' column relates to the hypothesis that there is a difference in spectral power when participants are presented a circle modified at 10 Hz relative to 5 Hz.

<table><caption>Supplementary Table 6 Custom-coded contrast matrix assigned to the Source predictor of each model.</caption><thead><tr><th>Source labels</th><th>Intercept</th><th>Contrast</th></tr></thead><tbody><tr><td>Online alpha</td><td>1/2</td><td>-0.5</td></tr><tr><td>Offline alpha</td><td>1/2</td><td>0.5</td></tr></tbody></table>

When comparing our present study to the one of Maaz et al. (2024), we manipulate the source of the continuous modification of the circle size. The source was, in the current study, the Pz alpha power of the participant computed in real time (Online alpha), and, in Maaz et al. (2024), alpha power computed on prerecorded EEG data. The present matrix was obtained by applying the generalised inverse to a *Hypothesis* matrix referring to the null hypothesis ($H_0$) regarding the effect of the source of feedback update (i.e., on average, the sum of the dependent variable within each experiment is null).
<table><caption>Supplementary Table 7 Estimates from statistical models computed with Equation 1.</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>0,01</td><td>0,004</td><td>0,017</td><td>0,329</td><td>&gt; 100</td></tr><tr><td>Theta</td><td>Fz</td><td>Exp vs. Control</td><td>0,038</td><td>-0,026</td><td>0,102</td><td>0,065</td><td>7.315</td></tr><tr><td>Theta</td><td>Fz</td><td>Condition - 5 Hz vs. 1 Hz</td><td>0,02</td><td>-0,054</td><td>0,095</td><td>0,043</td><td>2.409</td></tr><tr><td>Theta</td><td>Fz</td><td>Condition - 10 Hz vs. 5 Hz</td><td>0,037</td><td>-0,038</td><td>0,112</td><td>0,062</td><td>5.147</td></tr><tr><td>Theta</td><td>Fz</td><td>Source</td><td>0</td><td>-0,24</td><td>0,244</td><td>0,121</td><td>1</td></tr><tr><td>Theta</td><td>Fz</td><td>Trial:Exp vs. Control</td><td>-0,002</td><td>-0,014</td><td>0,01</td><td>0,007</td><td>0.587</td></tr><tr><td>Theta</td><td>Fz</td><td>Trial:Condition - 5 Hz vs. 1 Hz</td><td>0,001</td><td>-0,014</td><td>0,016</td><td>0,008</td><td>1.263</td></tr><tr><td>Theta</td><td>Fz</td><td>Trial:Condition - 10 Hz vs. 5 Hz</td><td>-0,011</td><td>-0,025</td><td>0,003</td><td>0,024</td><td>0.063</td></tr><tr><td>Theta</td><td>Fz</td><td>Trial:Source</td><td>0</td><td>-0,007</td><td>0,007</td><td>0,003</td><td>0.996</td></tr><tr><td>Theta</td><td>Fz</td><td>Exp vs. Control:Source</td><td>0,065</td><td>0,001</td><td>0,13</td><td>0,238</td><td>42.214</td></tr><tr><td>Theta</td><td>Fz</td><td>Condition - 5 Hz vs. 1 Hz:Source</td><td>-0,025</td><td>-0,099</td><td>0,05</td><td>0,047</td><td>0.344</td></tr><tr><td>Theta</td><td>Fz</td><td>Condition - 10 Hz vs. 5 Hz:Source</td><td>0,003</td><td>-0,072</td><td>0,079</td><td>0,039</td><td>1.15</td></tr><tr><td>Theta</td><td>Fz</td><td>Trial:Exp vs. Control:Source</td><td>-0,008</td><td>-0,02</td><td>0,004</td><td>0,015</td><td>0.102</td></tr><tr><td>Theta</td><td>Fz</td><td>Trial:Condition - 5 Hz vs. 1 Hz:Source</td><td>0,006</td><td>-0,009</td><td>0,02</td><td>0,01</td><td>3.501</td></tr><tr><td>Theta</td><td>Fz</td><td>Trial:Condition - 10 Hz vs. 5 Hz:Source</td><td>-0,007</td><td>-0,021</td><td>0,007</td><td>0,012</td><td>0.182</td></tr><tr><td>Alpha</td><td>Fz</td><td>Trial</td><td><strong>0,016</strong></td><td><strong>0,01</strong></td><td><strong>0,022</strong></td><td><strong>&gt; 100</strong></td><td><strong>&gt; 100</strong></td></tr><tr><td>Alpha</td><td>Fz</td><td>Exp vs. Control</td><td>0,03</td><td>-0,031</td><td>0,09</td><td>0,049</td><td>5.059</td></tr><tr><td>Alpha</td><td>Fz</td><td>Condition - 5 Hz vs. 1 Hz</td><td>-0,005</td><td>-0,072</td><td>0,062</td><td>0,034</td><td>0.797</td></tr><tr><td>Alpha</td><td>Fz</td><td>Condition - 10 Hz vs. 5 Hz</td><td>0,053</td><td>-0,025</td><td>0,132</td><td>0,098</td><td>10.126</td></tr><tr><td>Alpha</td><td>Fz</td><td>Source</td><td>-0,011</td><td>-0,259</td><td>0,236</td><td>0,125</td><td>0.869</td></tr><tr><td>Alpha</td><td>Fz</td><td>Trial:Exp vs. Control</td><td>-0,001</td><td>-0,012</td><td>0,01</td><td>0,006</td><td>0.778</td></tr><tr><td>Alpha</td><td>Fz</td><td>Trial:Condition - 5 Hz vs. 1 Hz</td><td>-0,007</td><td>-0,02</td><td>0,007</td><td>0,011</td><td>0.191</td></tr><tr><td>Alpha</td><td>Fz</td><td>Trial:Condition - 10 Hz vs. 5 Hz</td><td>0,004</td><td>-0,009</td><td>0,018</td><td>0,008</td><td>2.787</td></tr><tr><td>Alpha</td><td>Fz</td><td>Trial:Source</td><td>0,003</td><td>-0,002</td><td>0,009</td><td>0,006</td><td>7.259</td></tr><tr><td>Alpha</td><td>Fz</td><td>Exp vs. Control:Source</td><td>-0,012</td><td>-0,073</td><td>0,049</td><td>0,033</td><td>0.536</td></tr><tr><td>Alpha</td><td>Fz</td><td>Condition - 5 Hz vs. 1 Hz:Source</td><td>-0,001</td><td>-0,067</td><td>0,066</td><td>0,033</td><td>0.958</td></tr><tr><td>Alpha</td><td>Fz</td><td>Condition - 10 Hz vs. 5 Hz:Source</td><td>-0,04</td><td>-0,119</td><td>0,039</td><td>0,067</td><td>0.188</td></tr><tr><td>Alpha</td><td>Fz</td><td>Trial:Exp vs. Control:Source</td><td>0,006</td><td>-0,005</td><td>0,017</td><td>0,01</td><td>5.817</td></tr><tr><td>Alpha</td><td>Fz</td><td>Trial:Condition - 5 Hz vs. 1 Hz:Source</td><td>-0,003</td><td>-0,016</td><td>0,011</td><td>0,007</td><td>0.529</td></tr><tr><td>Alpha</td><td>Fz</td><td>Trial:Condition - 10 Hz vs. 5 Hz:Source</td><td>0,001</td><td>-0,013</td><td>0,014</td><td>0,007</td><td>1.18</td></tr><tr><td>SMR</td><td>Fz</td><td>Trial</td><td>0,006</td><td>0</td><td>0,012</td><td>0,02</td><td>36.418</td></tr><tr><td>SMR</td><td>Fz</td><td>Exp vs. Control</td><td>0,091</td><td>0,031</td><td>0,152</td><td>2.338</td><td>&gt; 100</td></tr><tr><td>SMR</td><td>Fz</td><td>Condition - 5 Hz vs. 1 Hz</td><td>-0,065</td><td>-0,134</td><td>0,003</td><td>0,206</td><td>0.03</td></tr><tr><td>SMR</td><td>Fz</td><td>Condition - 10 Hz vs. 5 Hz</td><td>0,073</td><td>-0,001</td><td>0,146</td><td>0,249</td><td>37.479</td></tr><tr><td>SMR</td><td>Fz</td><td>Source</td><td>-0,014</td><td>-0,265</td><td>0,24</td><td>0,128</td><td>0.834</td></tr><tr><td>SMR</td><td>Fz</td><td>Trial:Exp vs. Control</td><td>-0,006</td><td>-0,018</td><td>0,007</td><td>0,009</td><td>0.218</td></tr><tr><td>SMR</td><td>Fz</td><td>Trial:Condition - 5 Hz vs. 1 Hz</td><td>0,01</td><td>-0,006</td><td>0,025</td><td>0,017</td><td>8.348</td></tr><tr><td>SMR</td><td>Fz</td><td>Trial:Condition - 10 Hz vs. 5 Hz</td><td>-0,001</td><td>-0,016</td><td>0,014</td><td>0,008</td><td>0.854</td></tr><tr><td>SMR</td><td>Fz</td><td>Trial:Source</td><td>0,004</td><td>-0,002</td><td>0,01</td><td>0,007</td><td>10.216</td></tr><tr><td>SMR</td><td>Fz</td><td>Exp vs. Control:Source</td><td>0,012</td><td>-0,048</td><td>0,072</td><td>0,032</td><td>1.855</td></tr><tr><td>SMR</td><td>Fz</td><td>Condition - 5 Hz vs. 1 Hz:Source</td><td>-0,043</td><td>-0,111</td><td>0,025</td><td>0,075</td><td>0.117</td></tr><tr><td>SMR</td><td>Fz</td><td>Condition - 10 Hz vs. 5 Hz:Source</td><td>-0,033</td><td>-0,107</td><td>0,04</td><td>0,056</td><td>0.225</td></tr><tr><td>SMR</td><td>Fz</td><td>Trial:Exp vs. Control:Source</td><td>0,001</td><td>-0,011</td><td>0,013</td><td>0,006</td><td>1.238</td></tr><tr><td>SMR</td><td>Fz</td><td>Trial:Condition - 5 Hz vs. 1 Hz:Source</td><td>0,007</td><td>-0,008</td><td>0,023</td><td>0,012</td><td>4.708</td></tr><tr><td>SMR</td><td>Fz</td><td>Trial:Condition - 10 Hz vs. 5 Hz:Source</td><td>-0,003</td><td>-0,018</td><td>0,012</td><td>0,008</td><td>0.487</td></tr></tbody></table>
PASSIVE EEG-NF

<table><tbody><tr><td>Beta</td><td>Fz</td><td>Trial</td><td>0,013</td><td>0,005</td><td>0,022</td><td>0.68</td><td>&gt; 100</td></tr><tr><td>Beta</td><td>Fz</td><td>Exp vs. Control</td><td>0,085</td><td>-0,006</td><td>0,176</td><td>0.258</td><td>29.654</td></tr><tr><td>Beta</td><td>Fz</td><td>Condition - 5 Hz vs. 1 Hz</td><td>-0,031</td><td>-0,139</td><td>0,077</td><td>0.064</td><td>0.398</td></tr><tr><td>Beta</td><td>Fz</td><td>Condition - 10 Hz vs. 5 Hz</td><td>0,098</td><td>-0,007</td><td>0,203</td><td>0.292</td><td>29.473</td></tr><tr><td>Beta</td><td>Fz</td><td>Source</td><td>-0,017</td><td>-0,258</td><td>0,229</td><td>0.123</td><td>0.8</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial:Exp vs. Control</td><td>-0,003</td><td>-0,019</td><td>0,013</td><td>0.009</td><td>0.533</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial:Condition - 5 Hz vs. 1 Hz</td><td>-0,002</td><td>-0,024</td><td>0,021</td><td>0.011</td><td>0.787</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial:Condition - 10 Hz vs. 5 Hz</td><td>0,004</td><td>-0,016</td><td>0,024</td><td>0.011</td><td>1.846</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial:Source</td><td>0,005</td><td>-0,003</td><td>0,013</td><td>0.008</td><td>6.894</td></tr><tr><td>Beta</td><td>Fz</td><td>Exp vs. Control:Source</td><td>0,002</td><td>-0,089</td><td>0,092</td><td>0.045</td><td>1.061</td></tr><tr><td>Beta</td><td>Fz</td><td>Condition - 5 Hz vs. 1 Hz:Source</td><td>-0,023</td><td>-0,13</td><td>0,086</td><td>0.059</td><td>0.509</td></tr><tr><td>Beta</td><td>Fz</td><td>Condition - 10 Hz vs. 5 Hz:Source</td><td>-0,019</td><td>-0,123</td><td>0,086</td><td>0.056</td><td>0.567</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial:Exp vs. Control:Source</td><td>0,002</td><td>-0,014</td><td>0,018</td><td>0.008</td><td>1.585</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial:Condition - 5 Hz vs. 1 Hz:Source</td><td>0,001</td><td>-0,022</td><td>0,023</td><td>0.011</td><td>1.105</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial:Condition - 10 Hz vs. 5 Hz:Source</td><td>0</td><td>-0,02</td><td>0,02</td><td>0.01</td><td>0.973</td></tr><tr><td>Theta</td><td>Cz</td><td>Trial</td><td>0,008</td><td>0,001</td><td>0,015</td><td>0.045</td><td>80.367</td></tr><tr><td>Theta</td><td>Cz</td><td>Exp vs. Control</td><td>0,074</td><td>0,018</td><td>0,131</td><td>0.755</td><td>&gt; 100</td></tr><tr><td>Theta</td><td>Cz</td><td>Condition - 5 Hz vs. 1 Hz</td><td>0,024</td><td>-0,051</td><td>0,1</td><td>0.047</td><td>2.844</td></tr><tr><td>Theta</td><td>Cz</td><td>Condition - 10 Hz vs. 5 Hz</td><td>0,035</td><td>-0,038</td><td>0,108</td><td>0.057</td><td>4.762</td></tr><tr><td>Theta</td><td>Cz</td><td>Source</td><td>-0,001</td><td>-0,243</td><td>0,24</td><td>0.12</td><td>0.986</td></tr><tr><td>Theta</td><td>Cz</td><td>Trial:Exp vs. Control</td><td>-0,004</td><td>-0,017</td><td>0,009</td><td>0.008</td><td>0.374</td></tr><tr><td>Theta</td><td>Cz</td><td>Trial:Condition - 5 Hz vs. 1 Hz</td><td>0,003</td><td>-0,012</td><td>0,018</td><td>0.008</td><td>1.853</td></tr><tr><td>Theta</td><td>Cz</td><td>Trial:Condition - 10 Hz vs. 5 Hz</td><td>-0,013</td><td>-0,027</td><td>0,001</td><td>0.037</td><td>0.037</td></tr><tr><td>Theta</td><td>Cz</td><td>Trial:Source</td><td>0</td><td>-0,006</td><td>0,007</td><td>0.003</td><td>1.226</td></tr><tr><td>Theta</td><td>Cz</td><td>Exp vs. Control:Source</td><td>0,074</td><td>0,017</td><td>0,131</td><td>0.713</td><td>&gt; 100</td></tr><tr><td>Theta</td><td>Cz</td><td>Condition - 5 Hz vs. 1 Hz:Source</td><td>0,01</td><td>-0,066</td><td>0,085</td><td>0.039</td><td>1.528</td></tr><tr><td>Theta</td><td>Cz</td><td>Condition - 10 Hz vs. 5 Hz:Source</td><td>-0,017</td><td>-0,09</td><td>0,056</td><td>0.041</td><td>0.476</td></tr><tr><td>Theta</td><td>Cz</td><td>Trial:Exp vs. Control:Source</td><td>-0,007</td><td>-0,02</td><td>0,006</td><td>0.011</td><td>0.179</td></tr><tr><td>Theta</td><td>Cz</td><td>Trial:Condition - 5 Hz vs. 1 Hz:Source</td><td>0,002</td><td>-0,013</td><td>0,016</td><td>0.008</td><td>1.39</td></tr><tr><td>Theta</td><td>Cz</td><td>Trial:Condition - 10 Hz vs. 5 Hz:Source</td><td>-0,006</td><td>-0,021</td><td>0,008</td><td>0.011</td><td>0.236</td></tr><tr><td><b>Alpha</b></td><td><b>Cz</b></td><td><b>Trial</b></td><td><b>0,017</b></td><td><b>0,011</b></td><td><b>0,023</b></td><td><b>&gt; 100</b></td><td><b>&gt; 100</b></td></tr><tr><td>Alpha</td><td>Cz</td><td>Exp vs. Control</td><td>0,014</td><td>-0,047</td><td>0,075</td><td>0.034</td><td>2.07</td></tr><tr><td>Alpha</td><td>Cz</td><td>Condition - 5 Hz vs. 1 Hz</td><td>-0,012</td><td>-0,084</td><td>0,059</td><td>0.038</td><td>0.585</td></tr><tr><td>Alpha</td><td>Cz</td><td>Condition - 10 Hz vs. 5 Hz</td><td>0,051</td><td>-0,025</td><td>0,126</td><td>0.091</td><td>9.871</td></tr><tr><td>Alpha</td><td>Cz</td><td>Source</td><td>-0,02</td><td>-0,263</td><td>0,225</td><td>0.123</td><td>0.768</td></tr><tr><td>Alpha</td><td>Cz</td><td>Trial:Exp vs. Control</td><td>0</td><td>-0,012</td><td>0,011</td><td>0.006</td><td>0.875</td></tr><tr><td>Alpha</td><td>Cz</td><td>Trial:Condition - 5 Hz vs. 1 Hz</td><td>-0,006</td><td>-0,02</td><td>0,008</td><td>0.011</td><td>0.23</td></tr><tr><td>Alpha</td><td>Cz</td><td>Trial:Condition - 10 Hz vs. 5 Hz</td><td>0</td><td>-0,014</td><td>0,014</td><td>0.007</td><td>1</td></tr><tr><td>Alpha</td><td>Cz</td><td>Trial:Source</td><td>0,006</td><td>0</td><td>0,012</td><td>0.017</td><td>29.384</td></tr><tr><td>Alpha</td><td>Cz</td><td>Exp vs. Control:Source</td><td>-0,018</td><td>-0,079</td><td>0,043</td><td>0.037</td><td>0.391</td></tr><tr><td>Alpha</td><td>Cz</td><td>Condition - 5 Hz vs. 1 Hz:Source</td><td>0,002</td><td>-0,069</td><td>0,073</td><td>0.036</td><td>1.098</td></tr><tr><td>Alpha</td><td>Cz</td><td>Condition - 10 Hz vs. 5 Hz:Source</td><td>-0,053</td><td>-0,128</td><td>0,022</td><td>0.101</td><td>0.089</td></tr><tr><td>Alpha</td><td>Cz</td><td>Trial:Exp vs. Control:Source</td><td>0,003</td><td>-0,009</td><td>0,015</td><td>0.007</td><td>2.303</td></tr><tr><td>Alpha</td><td>Cz</td><td>Trial:Condition - 5 Hz vs. 1 Hz:Source</td><td>-0,004</td><td>-0,018</td><td>0,01</td><td>0.008</td><td>0.434</td></tr><tr><td>Alpha</td><td>Cz</td><td>Trial:Condition - 10 Hz vs. 5 Hz:Source</td><td>0,004</td><td>-0,01</td><td>0,017</td><td>0.008</td><td>2.38</td></tr><tr><td>SMR</td><td>Cz</td><td>Trial</td><td>0,003</td><td>-0,002</td><td>0,009</td><td>0.006</td><td>8.98</td></tr><tr><td>SMR</td><td>Cz</td><td>Exp vs. Control</td><td>0,077</td><td>0,017</td><td>0,138</td><td>0.707</td><td>&gt; 100</td></tr><tr><td>SMR</td><td>Cz</td><td>Condition - 5 Hz vs. 1 Hz</td><td>-0,042</td><td>-0,103</td><td>0,019</td><td>0.077</td><td>0.095</td></tr></tbody></table>
PASSIVE EEG-NF

<table><tbody><tr><td>SMR</td><td>Cz</td><td>Condition - 10 Hz vs. 5 Hz</td><td>0,076</td><td>0,01</td><td>0,142</td><td>0,432</td><td>82.487</td></tr><tr><td>SMR</td><td>Cz</td><td>Source</td><td>-0,008</td><td>-0,263</td><td>0,249</td><td>0,128</td><td>0.9</td></tr><tr><td>SMR</td><td>Cz</td><td>Trial:Exp vs. Control</td><td>-0,005</td><td>-0,018</td><td>0,009</td><td>0,009</td><td>0.324</td></tr><tr><td>SMR</td><td>Cz</td><td>Trial:Condition - 5 Hz vs. 1 Hz</td><td>0,006</td><td>-0,01</td><td>0,021</td><td>0,01</td><td>3.351</td></tr><tr><td>SMR</td><td>Cz</td><td>Trial:Condition - 10 Hz vs. 5 Hz</td><td>-0,002</td><td>-0,017</td><td>0,012</td><td>0,007</td><td>0.603</td></tr><tr><td>SMR</td><td>Cz</td><td>Trial:Source</td><td>0,002</td><td>-0,003</td><td>0,007</td><td>0,003</td><td>3.42</td></tr><tr><td>SMR</td><td>Cz</td><td>Exp vs. Control:Source</td><td>0,019</td><td>-0,041</td><td>0,079</td><td>0,037</td><td>2.765</td></tr><tr><td>SMR</td><td>Cz</td><td>Condition - 5 Hz vs. 1 Hz:Source</td><td>-0,036</td><td>-0,097</td><td>0,025</td><td>0,062</td><td>0.138</td></tr><tr><td>SMR</td><td>Cz</td><td>Condition - 10 Hz vs. 5 Hz:Source</td><td>-0,021</td><td>-0,087</td><td>0,045</td><td>0,041</td><td>0.355</td></tr><tr><td>SMR</td><td>Cz</td><td>Trial:Exp vs. Control:Source</td><td>0,001</td><td>-0,012</td><td>0,014</td><td>0,007</td><td>1.299</td></tr><tr><td>SMR</td><td>Cz</td><td>Trial:Condition - 5 Hz vs. 1 Hz:Source</td><td>0,006</td><td>-0,009</td><td>0,021</td><td>0,011</td><td>3.693</td></tr><tr><td>SMR</td><td>Cz</td><td>Trial:Condition - 10 Hz vs. 5 Hz:Source</td><td>-0,009</td><td>-0,023</td><td>0,005</td><td>0,016</td><td>0.114</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial</td><td>0</td><td>-0,007</td><td>0,006</td><td>0,003</td><td>0.846</td></tr><tr><td>Beta</td><td>Cz</td><td>Exp vs. Control</td><td>0,053</td><td>-0,027</td><td>0,133</td><td>0,097</td><td>9.446</td></tr><tr><td>Beta</td><td>Cz</td><td>Condition - 5 Hz vs. 1 Hz</td><td>-0,02</td><td>-0,082</td><td>0,042</td><td>0,039</td><td>0.355</td></tr><tr><td>Beta</td><td>Cz</td><td>Condition - 10 Hz vs. 5 Hz</td><td>0,105</td><td>0,022</td><td>0,187</td><td>0,95</td><td>&gt; 100</td></tr><tr><td>Beta</td><td>Cz</td><td>Source</td><td>-0,004</td><td>-0,254</td><td>0,249</td><td>0,126</td><td>0.957</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial:Exp vs. Control</td><td>0</td><td>-0,015</td><td>0,015</td><td>0,008</td><td>1.015</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial:Condition - 5 Hz vs. 1 Hz</td><td>-0,005</td><td>-0,02</td><td>0,011</td><td>0,01</td><td>0.369</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial:Condition - 10 Hz vs. 5 Hz</td><td>0</td><td>-0,017</td><td>0,017</td><td>0,009</td><td>1.007</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial:Source</td><td>0,001</td><td>-0,005</td><td>0,008</td><td>0,004</td><td>2.008</td></tr><tr><td>Beta</td><td>Cz</td><td>Exp vs. Control:Source</td><td>-0,023</td><td>-0,104</td><td>0,057</td><td>0,048</td><td>0.388</td></tr><tr><td>Beta</td><td>Cz</td><td>Condition - 5 Hz vs. 1 Hz:Source</td><td>-0,046</td><td>-0,108</td><td>0,015</td><td>0,093</td><td>0.075</td></tr><tr><td>Beta</td><td>Cz</td><td>Condition - 10 Hz vs. 5 Hz:Source</td><td>0,01</td><td>-0,073</td><td>0,092</td><td>0,043</td><td>1.44</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial:Exp vs. Control:Source</td><td>0,008</td><td>-0,008</td><td>0,023</td><td>0,013</td><td>5.299</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial:Condition - 5 Hz vs. 1 Hz:Source</td><td>0,006</td><td>-0,01</td><td>0,021</td><td>0,01</td><td>3.207</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial:Condition - 10 Hz vs. 5 Hz:Source</td><td>-0,007</td><td>-0,024</td><td>0,01</td><td>0,012</td><td>0.255</td></tr><tr><td>Theta</td><td>Pz</td><td>Trial</td><td>0,01</td><td>0,003</td><td>0,018</td><td>0,128</td><td>&gt; 100</td></tr><tr><td>Theta</td><td>Pz</td><td>Exp vs. Control</td><td>0,081</td><td>0,021</td><td>0,141</td><td>1.059</td><td>&gt; 100</td></tr><tr><td>Theta</td><td>Pz</td><td>Condition - 5 Hz vs. 1 Hz</td><td>0,01</td><td>-0,074</td><td>0,094</td><td>0,043</td><td>1.432</td></tr><tr><td>Theta</td><td>Pz</td><td>Condition - 10 Hz vs. 5 Hz</td><td>0,007</td><td>-0,069</td><td>0,083</td><td>0,039</td><td>1.334</td></tr><tr><td>Theta</td><td>Pz</td><td>Source</td><td>-0,001</td><td>-0,238</td><td>0,24</td><td>0,118</td><td>0.985</td></tr><tr><td>Theta</td><td>Pz</td><td>Trial:Exp vs. Control</td><td>-0,006</td><td>-0,019</td><td>0,007</td><td>0,01</td><td>0.235</td></tr><tr><td>Theta</td><td>Pz</td><td>Trial:Condition - 5 Hz vs. 1 Hz</td><td>0,003</td><td>-0,013</td><td>0,019</td><td>0,009</td><td>1.732</td></tr><tr><td>Theta</td><td>Pz</td><td>Trial:Condition - 10 Hz vs. 5 Hz</td><td>-0,011</td><td>-0,026</td><td>0,004</td><td>0,023</td><td>0.072</td></tr><tr><td>Theta</td><td>Pz</td><td>Trial:Source</td><td>0</td><td>-0,008</td><td>0,008</td><td>0,004</td><td>0.997</td></tr><tr><td>Theta</td><td>Pz</td><td>Exp vs. Control:Source</td><td>0,085</td><td>0,025</td><td>0,145</td><td>1.457</td><td>&gt; 100</td></tr><tr><td>Theta</td><td>Pz</td><td>Condition - 5 Hz vs. 1 Hz:Source</td><td>-0,014</td><td>-0,098</td><td>0,07</td><td>0,045</td><td>0.592</td></tr><tr><td>Theta</td><td>Pz</td><td>Condition - 10 Hz vs. 5 Hz:Source</td><td>-0,042</td><td>-0,118</td><td>0,034</td><td>0,07</td><td>0.162</td></tr><tr><td>Theta</td><td>Pz</td><td>Trial:Exp vs. Control:Source</td><td>-0,01</td><td>-0,023</td><td>0,004</td><td>0,019</td><td>0.087</td></tr><tr><td>Theta</td><td>Pz</td><td>Trial:Condition - 5 Hz vs. 1 Hz:Source</td><td>0,003</td><td>-0,013</td><td>0,02</td><td>0,009</td><td>1.941</td></tr><tr><td>Theta</td><td>Pz</td><td>Trial:Condition - 10 Hz vs. 5 Hz:Source</td><td>-0,005</td><td>-0,02</td><td>0,009</td><td>0,01</td><td>0.302</td></tr><tr><td><strong>Alpha</strong></td><td><strong>Pz</strong></td><td><strong>Trial</strong></td><td><strong>0,018</strong></td><td><strong>0,011</strong></td><td><strong>0,024</strong></td><td><strong>&gt; 100</strong></td><td><strong>&gt; 100</strong></td></tr><tr><td>Alpha</td><td>Pz</td><td>Exp vs. Control</td><td>-0,02</td><td>-0,088</td><td>0,047</td><td>0,041</td><td>0.378</td></tr><tr><td>Alpha</td><td>Pz</td><td>Condition - 5 Hz vs. 1 Hz</td><td>-0,01</td><td>-0,079</td><td>0,058</td><td>0,036</td><td>0.619</td></tr><tr><td>Alpha</td><td>Pz</td><td>Condition - 10 Hz vs. 5 Hz</td><td>0,036</td><td>-0,043</td><td>0,115</td><td>0,06</td><td>4.503</td></tr><tr><td>Alpha</td><td>Pz</td><td>Source</td><td>-0,022</td><td>-0,265</td><td>0,22</td><td>0,123</td><td>0.748</td></tr><tr><td>Alpha</td><td>Pz</td><td>Trial:Exp vs. Control</td><td>-0,005</td><td>-0,017</td><td>0,008</td><td>0,008</td><td>0.312</td></tr></tbody></table>
PASSIVE EEG-NF

<table>
  <tbody>
    <tr>
      <td>Alpha</td>
      <td>Pz</td>
      <td>Trial:Condition - 5 Hz vs. 1 Hz</td>
      <td>-0,007</td>
      <td>-0,021</td>
      <td>0,007</td>
      <td>0.012</td>
      <td>0.181</td>
    </tr>
    <tr>
      <td>Alpha</td>
      <td>Pz</td>
      <td>Trial:Condition - 10 Hz vs. 5 Hz</td>
      <td>0</td>
      <td>-0,013</td>
      <td>0,013</td>
      <td>0.007</td>
      <td>0.961</td>
    </tr>
    <tr>
      <td>Alpha</td>
      <td>Pz</td>
      <td>Trial:Source</td>
      <td>0,006</td>
      <td>0</td>
      <td>0,013</td>
      <td>0.023</td>
      <td>41.899</td>
    </tr>
    <tr>
      <td>Alpha</td>
      <td>Pz</td>
      <td>Exp vs. Control:Source</td>
      <td>0,005</td>
      <td>-0,063</td>
      <td>0,073</td>
      <td>0.034</td>
      <td>1.285</td>
    </tr>
    <tr>
      <td>Alpha</td>
      <td>Pz</td>
      <td>Condition - 5 Hz vs. 1 Hz:Source</td>
      <td>0,008</td>
      <td>-0,061</td>
      <td>0,076</td>
      <td>0.035</td>
      <td>1.422</td>
    </tr>
    <tr>
      <td>Alpha</td>
      <td>Pz</td>
      <td>Condition - 10 Hz vs. 5 Hz:Source</td>
      <td>-0,015</td>
      <td>-0,095</td>
      <td>0,063</td>
      <td>0.043</td>
      <td>0.537</td>
    </tr>
    <tr>
      <td>Alpha</td>
      <td>Pz</td>
      <td>Trial:Exp vs. Control:Source</td>
      <td>0,002</td>
      <td>-0,011</td>
      <td>0,014</td>
      <td>0.007</td>
      <td>1.474</td>
    </tr>
    <tr>
      <td>Alpha</td>
      <td>Pz</td>
      <td>Trial:Condition - 5 Hz vs. 1 Hz:Source</td>
      <td>-0,007</td>
      <td>-0,021</td>
      <td>0,007</td>
      <td>0.012</td>
      <td>0.187</td>
    </tr>
    <tr>
      <td>Alpha</td>
      <td>Pz</td>
      <td>Trial:Condition - 10 Hz vs. 5 Hz:Source</td>
      <td>0</td>
      <td>-0,013</td>
      <td>0,014</td>
      <td>0.007</td>
      <td>1.098</td>
    </tr>
    <tr>
      <td>SMR</td>
      <td>Pz</td>
      <td>Trial</td>
      <td>0,004</td>
      <td>-0,001</td>
      <td>0,009</td>
      <td>0.007</td>
      <td>12.163</td>
    </tr>
    <tr>
      <td>SMR</td>
      <td>Pz</td>
      <td>Exp vs. Control</td>
      <td>0,068</td>
      <td>0,007</td>
      <td>0,128</td>
      <td>0.354</td>
      <td>70.559</td>
    </tr>
    <tr>
      <td>SMR</td>
      <td>Pz</td>
      <td>Condition - 5 Hz vs. 1 Hz</td>
      <td>-0,023</td>
      <td>-0,08</td>
      <td>0,034</td>
      <td>0.039</td>
      <td>0.271</td>
    </tr>
    <tr>
      <td>SMR</td>
      <td>Pz</td>
      <td>Condition - 10 Hz vs. 5 Hz</td>
      <td>0,039</td>
      <td>-0,021</td>
      <td>0,099</td>
      <td>0.07</td>
      <td>8.925</td>
    </tr>
    <tr>
      <td>SMR</td>
      <td>Pz</td>
      <td>Source</td>
      <td>-0,003</td>
      <td>-0,262</td>
      <td>0,253</td>
      <td>0.128</td>
      <td>0.957</td>
    </tr>
    <tr>
      <td>SMR</td>
      <td>Pz</td>
      <td>Trial:Exp vs. Control</td>
      <td>-0,008</td>
      <td>-0,02</td>
      <td>0,003</td>
      <td>0.017</td>
      <td>0.078</td>
    </tr>
    <tr>
      <td>SMR</td>
      <td>Pz</td>
      <td>Trial:Condition - 5 Hz vs. 1 Hz</td>
      <td>0</td>
      <td>-0,015</td>
      <td>0,015</td>
      <td>0.007</td>
      <td>0.955</td>
    </tr>
    <tr>
      <td>SMR</td>
      <td>Pz</td>
      <td>Trial:Condition - 10 Hz vs. 5 Hz</td>
      <td>0,001</td>
      <td>-0,012</td>
      <td>0,014</td>
      <td>0.007</td>
      <td>1.235</td>
    </tr>
    <tr>
      <td>SMR</td>
      <td>Pz</td>
      <td>Trial:Source</td>
      <td>0,001</td>
      <td>-0,004</td>
      <td>0,006</td>
      <td>0.003</td>
      <td>2.057</td>
    </tr>
    <tr>
      <td>SMR</td>
      <td>Pz</td>
      <td>Exp vs. Control:Source</td>
      <td>0,024</td>
      <td>-0,037</td>
      <td>0,084</td>
      <td>0.042</td>
      <td>3.661</td>
    </tr>
    <tr>
      <td>SMR</td>
      <td>Pz</td>
      <td>Condition - 5 Hz vs. 1 Hz:Source</td>
      <td>-0,014</td>
      <td>-0,071</td>
      <td>0,043</td>
      <td>0.033</td>
      <td>0.462</td>
    </tr>
    <tr>
      <td>SMR</td>
      <td>Pz</td>
      <td>Condition - 10 Hz vs. 5 Hz:Source</td>
      <td>-0,026</td>
      <td>-0,086</td>
      <td>0,034</td>
      <td>0.044</td>
      <td>0.242</td>
    </tr>
    <tr>
      <td>SMR</td>
      <td>Pz</td>
      <td>Trial:Exp vs. Control:Source</td>
      <td>0,002</td>
      <td>-0,009</td>
      <td>0,014</td>
      <td>0.006</td>
      <td>1.808</td>
    </tr>
    <tr>
      <td>SMR</td>
      <td>Pz</td>
      <td>Trial:Condition - 5 Hz vs. 1 Hz:Source</td>
      <td>-0,001</td>
      <td>-0,015</td>
      <td>0,014</td>
      <td>0.007</td>
      <td>0.861</td>
    </tr>
    <tr>
      <td>SMR</td>
      <td>Pz</td>
      <td>Trial:Condition - 10 Hz vs. 5 Hz:Source</td>
      <td>-0,003</td>
      <td>-0,016</td>
      <td>0,01</td>
      <td>0.008</td>
      <td>0.443</td>
    </tr>
    <tr>
      <td>Beta</td>
      <td>Pz</td>
      <td>Trial</td>
      <td>-0,006</td>
      <td>-0,014</td>
      <td>0,001</td>
      <td>0.014</td>
      <td>0.058</td>
    </tr>
    <tr>
      <td>Beta</td>
      <td>Pz</td>
      <td>Exp vs. Control</td>
      <td>0,063</td>
      <td>-0,027</td>
      <td>0,154</td>
      <td>0.121</td>
      <td>10.839</td>
    </tr>
    <tr>
      <td>Beta</td>
      <td>Pz</td>
      <td>Condition - 5 Hz vs. 1 Hz</td>
      <td>-0,013</td>
      <td>-0,073</td>
      <td>0,046</td>
      <td>0.033</td>
      <td>0.485</td>
    </tr>
    <tr>
      <td>Beta</td>
      <td>Pz</td>
      <td>Condition - 10 Hz vs. 5 Hz</td>
      <td>0,09</td>
      <td>-0,002</td>
      <td>0,182</td>
      <td>0.298</td>
      <td>35.523</td>
    </tr>
    <tr>
      <td>Beta</td>
      <td>Pz</td>
      <td>Source</td>
      <td>0,003</td>
      <td>-0,252</td>
      <td>0,258</td>
      <td>0.127</td>
      <td>1.036</td>
    </tr>
    <tr>
      <td>Beta</td>
      <td>Pz</td>
      <td>Trial:Exp vs. Control</td>
      <td>-0,001</td>
      <td>-0,018</td>
      <td>0,016</td>
      <td>0.009</td>
      <td>0.838</td>
    </tr>
    <tr>
      <td>Beta</td>
      <td>Pz</td>
      <td>Trial:Condition - 5 Hz vs. 1 Hz</td>
      <td>-0,011</td>
      <td>-0,026</td>
      <td>0,004</td>
      <td>0.021</td>
      <td>0.079</td>
    </tr>
    <tr>
      <td>Beta</td>
      <td>Pz</td>
      <td>Trial:Condition - 10 Hz vs. 5 Hz</td>
      <td>-0,001</td>
      <td>-0,019</td>
      <td>0,018</td>
      <td>0.009</td>
      <td>0.87</td>
    </tr>
    <tr>
      <td>Beta</td>
      <td>Pz</td>
      <td>Trial:Source</td>
      <td>-0,001</td>
      <td>-0,008</td>
      <td>0,006</td>
      <td>0.004</td>
      <td>0.635</td>
    </tr>
    <tr>
      <td>Beta</td>
      <td>Pz</td>
      <td>Exp vs. Control:Source</td>
      <td>0,006</td>
      <td>-0,085</td>
      <td>0,097</td>
      <td>0.046</td>
      <td>1.223</td>
    </tr>
    <tr>
      <td>Beta</td>
      <td>Pz</td>
      <td>Condition - 5 Hz vs. 1 Hz:Source</td>
      <td>-0,02</td>
      <td>-0,079</td>
      <td>0,04</td>
      <td>0.038</td>
      <td>0.343</td>
    </tr>
    <tr>
      <td>Beta</td>
      <td>Pz</td>
      <td>Condition - 10 Hz vs. 5 Hz:Source</td>
      <td>0,019</td>
      <td>-0,074</td>
      <td>0,11</td>
      <td>0.049</td>
      <td>1.908</td>
    </tr>
    <tr>
      <td>Beta</td>
      <td>Pz</td>
      <td>Trial:Exp vs. Control:Source</td>
      <td>0,007</td>
      <td>-0,01</td>
      <td>0,025</td>
      <td>0.012</td>
      <td>4.092</td>
    </tr>
    <tr>
      <td>Beta</td>
      <td>Pz</td>
      <td>Trial:Condition - 5 Hz vs. 1 Hz:Source</td>
      <td>-0,003</td>
      <td>-0,018</td>
      <td>0,011</td>
      <td>0.008</td>
      <td>0.489</td>
    </tr>
    <tr>
      <td>Beta</td>
      <td>Pz</td>
      <td>Trial:Condition - 10 Hz vs. 5 Hz:Source</td>
      <td>-0,007</td>
      <td>-0,025</td>
      <td>0,012</td>
      <td>0.012</td>
      <td>0.301</td>
    </tr>
    <tr>
      <td>Theta</td>
      <td>Fz</td>
      <td>Trial</td>
      <td>0,01</td>
      <td>0,004</td>
      <td>0,017</td>
      <td>0.329</td>
      <td>> 100</td>
    </tr>
    <tr>
      <td>Theta</td>
      <td>Fz</td>
      <td>Exp vs. Control</td>
      <td>0,038</td>
      <td>-0,026</td>
      <td>0,102</td>
      <td>0.065</td>
      <td>7.315</td>
    </tr>
    <tr>
      <td>Theta</td>
      <td>Fz</td>
      <td>Condition - 5 Hz vs. 1 Hz</td>
      <td>0,02</td>
      <td>-0,054</td>
      <td>0,095</td>
      <td>0.043</td>
      <td>2.409</td>
    </tr>
    <tr>
      <td>Theta</td>
      <td>Fz</td>
      <td>Condition - 10 Hz vs. 5 Hz</td>
      <td>0,037</td>
      <td>-0,038</td>
      <td>0,112</td>
      <td>0.062</td>
      <td>5.147</td>
    </tr>
    <tr>
      <td>Theta</td>
      <td>Fz</td>
      <td>Source</td>
      <td>0</td>
      <td>-0,24</td>
      <td>0,244</td>
      <td>0.121</td>
      <td>1</td>
    </tr>
    <tr>
      <td>Theta</td>
      <td>Fz</td>
      <td>Trial:Exp vs. Control</td>
      <td>-0,002</td>
      <td>-0,014</td>
      <td>0,01</td>
      <td>0.007</td>
      <td>0.587</td>
    </tr>
    <tr>
      <td>Theta</td>
      <td>Fz</td>
      <td>Trial:Condition - 5 Hz vs. 1 Hz</td>
      <td>0,001</td>
      <td>-0,014</td>
      <td>0,016</td>
      <td>0.008</td>
      <td>1.263</td>
    </tr>
    <tr>
      <td>Theta</td>
      <td>Fz</td>
      <td>Trial:Condition - 10 Hz vs. 5 Hz</td>
      <td>-0,011</td>
      <td>-0,025</td>
      <td>0,003</td>
      <td>0.024</td>
      <td>0.063</td>
    </tr>
    <tr>
      <td>Theta</td>
      <td>Fz</td>
      <td>Trial:Source</td>
      <td>0</td>
      <td>-0,007</td>
      <td>0,007</td>
      <td>0.003</td>
      <td>0.996</td>
    </tr>
  </tbody>
</table>
<table><tr><td>Theta</td><td>Fz</td><td>Exp vs. Control:Source</td><td>0,065</td><td>0,001</td><td>0,13</td><td>0,238</td><td>42.214</td></tr><tr><td>Theta</td><td>Fz</td><td>Condition - 5 Hz vs. 1 Hz:Source</td><td>-0,025</td><td>-0,099</td><td>0,05</td><td>0,047</td><td>0.344</td></tr></table>

Each model reported has been computed three times 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 three model computations. The 'Estimate' column stands for the estimated group-level effect (slope) of each predictor considered in a model (in z-score standardised units). For the 'Trial' predictor, the estimate corresponds to the group-level effect of one trial of the control condition (modality of Condition predictor defined as reference for subsequent comparisons) and averaged across both experiments (defined as reference for subsequent comparisons for the Source predictor). For the 'Exp vs. Control', '5 Hz vs. 1 Hz' and '10 Hz vs. 5 Hz' predictors, the estimate refers to the group-level effect when comparing, during each condition first trial (modality of Trial predictor defined as reference for subsequent comparisons) and averaged across both experiments (defined as reference for subsequent comparisons for the Source predictor), the mean of the three Experimental conditions (1 Hz, 5 Hz, 10 Hz) to the control condition, the 5 Hz Condition to the 1 Hz Condition, and the 10 Hz Condition to the 1 Hz Condition, respectively. Concerning the 'Source' predictor, the estimate refers to the group-level effect, during the first trial (modality of Trial predictor defined as reference for subsequent comparisons) of the control condition (modality of Condition predictor defined as reference for subsequent comparisons), when comparing the group for which the source of the feedback update was Online alpha (current experiment) and the group for which it was Offline alpha (experiment of Maaz et al., 2024). The 'Lower' and 'Upper' columns correspond to the minimal lower and maximal upper bounds of the three 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 8 Estimates from models considering the 'Trial' predictor (integers of 1 to 32) throughout the entire task.</caption><thead><tr><th>Frequency Band</th><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>Theta</td><td>Fz</td><td>Trial</td><td>0,006</td><td>0,003</td><td>0,009</td><td>0.792</td><td>&gt; 100</td></tr><tr><td>Theta</td><td>Fz</td><td>Source</td><td>0,025</td><td>-0,207</td><td>0,255</td><td>0.119</td><td>1.433</td></tr><tr><td>Theta</td><td>Fz</td><td>Trial:Source</td><td>-0,002</td><td>-0,005</td><td>0,001</td><td>0.003</td><td>0.161</td></tr><tr><td><strong>Alpha</strong></td><td><strong>Fz</strong></td><td><strong>Trial</strong></td><td><strong>0,007</strong></td><td><strong>0,004</strong></td><td><strong>0,01</strong></td><td><strong>&gt; 100</strong></td><td><strong>&gt; 100</strong></td></tr><tr><td>Alpha</td><td>Fz</td><td>Source</td><td>0,041</td><td>-0,188</td><td>0,269</td><td>0.122</td><td>1.772</td></tr><tr><td>Alpha</td><td>Fz</td><td>Trial:Source</td><td>-0,003</td><td>-0,005</td><td>0</td><td>0.009</td><td>0.027</td></tr><tr><td>SMR</td><td>Fz</td><td>Trial</td><td>0,003</td><td>0,001</td><td>0,005</td><td>0.026</td><td>&gt; 100</td></tr><tr><td>SMR</td><td>Fz</td><td>Source</td><td>0,026</td><td>-0,212</td><td>0,266</td><td>0.122</td><td>1.413</td></tr><tr><td>SMR</td><td>Fz</td><td>Trial:Source</td><td>-0,002</td><td>-0,004</td><td>0,001</td><td>0.003</td><td>0.101</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial</td><td>0,005</td><td>0,002</td><td>0,008</td><td>0.121</td><td>&gt; 100</td></tr><tr><td>Beta</td><td>Fz</td><td>Source</td><td>-0,012</td><td>-0,243</td><td>0,221</td><td>0.115</td><td>0.843</td></tr><tr><td>Beta</td><td>Fz</td><td>Trial:Source</td><td>0,001</td><td>-0,002</td><td>0,004</td><td>0.002</td><td>2.164</td></tr><tr><td>Theta</td><td>Cz</td><td>Trial</td><td>0,003</td><td>0</td><td>0,006</td><td>0.011</td><td>34.99</td></tr><tr><td>Theta</td><td>Cz</td><td>Source</td><td>0,021</td><td>-0,217</td><td>0,255</td><td>0.12</td><td>1.333</td></tr><tr><td>Theta</td><td>Cz</td><td>Trial:Source</td><td>-0,001</td><td>-0,005</td><td>0,002</td><td>0.002</td><td>0.23</td></tr><tr><td><strong>Alpha</strong></td><td><strong>Cz</strong></td><td><strong>Trial</strong></td><td><strong>0,007</strong></td><td><strong>0,004</strong></td><td><strong>0,01</strong></td><td><strong>&gt; 100</strong></td><td><strong>&gt; 100</strong></td></tr><tr><td>Alpha</td><td>Cz</td><td>Source</td><td>0,057</td><td>-0,167</td><td>0,28</td><td>0.129</td><td>2.293</td></tr><tr><td>Alpha</td><td>Cz</td><td>Trial:Source</td><td>-0,004</td><td>-0,007</td><td>-0,001</td><td>0.04</td><td>0.005</td></tr><tr><td>SMR</td><td>Cz</td><td>Trial</td><td>0,002</td><td>0</td><td>0,004</td><td>0.005</td><td>24.922</td></tr><tr><td>SMR</td><td>Cz</td><td>Source</td><td>0,019</td><td>-0,227</td><td>0,262</td><td>0.123</td><td>1.284</td></tr><tr><td>SMR</td><td>Cz</td><td>Trial:Source</td><td>-0,001</td><td>-0,003</td><td>0,001</td><td>0.002</td><td>0.137</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial</td><td>0,001</td><td>-0,001</td><td>0,004</td><td>0.002</td><td>6.62</td></tr><tr><td>Beta</td><td>Cz</td><td>Source</td><td>-0,003</td><td>-0,245</td><td>0,24</td><td>0.121</td><td>0.965</td></tr><tr><td>Beta</td><td>Cz</td><td>Trial:Source</td><td>0</td><td>-0,002</td><td>0,003</td><td>0.001</td><td>1.266</td></tr><tr><td>Theta</td><td>Pz</td><td>Trial</td><td>0,006</td><td>0,002</td><td>0,009</td><td>0.253</td><td>&gt; 100</td></tr><tr><td>Theta</td><td>Pz</td><td>Source</td><td>0,037</td><td>-0,183</td><td>0,26</td><td>0.118</td><td>1.689</td></tr></tbody></table>
<table><tr><td>Theta</td><td>Pz</td><td>Trial:Source</td><td>-0,002</td><td>-0,006</td><td>0,001</td><td>0.004</td><td>0.092</td></tr><tr><td><b>Alpha</b></td><td><b>Pz</b></td><td><b>Trial</b></td><td><b>0,009</b></td><td><b>0,006</b></td><td><b>0,012</b></td><td><b>&gt; 100</b></td><td><b>&gt; 100</b></td></tr><tr><td>Alpha</td><td>Pz</td><td>Source</td><td>0,072</td><td>-0,154</td><td>0,296</td><td>0.138</td><td>2.812</td></tr><tr><td>Alpha</td><td>Pz</td><td>Trial:Source</td><td>-0,005</td><td>-0,007</td><td>-0,002</td><td>0.473</td><td>0</td></tr><tr><td>SMR</td><td>Pz</td><td>Trial</td><td>0,002</td><td>0</td><td>0,004</td><td>0.014</td><td>80.223</td></tr><tr><td>SMR</td><td>Pz</td><td>Source</td><td>0,022</td><td>-0,226</td><td>0,27</td><td>0.127</td><td>1.327</td></tr><tr><td>SMR</td><td>Pz</td><td>Trial:Source</td><td>-0,001</td><td>-0,004</td><td>0,001</td><td>0.003</td><td>0.085</td></tr><tr><td>Beta</td><td>Pz</td><td>Trial</td><td>0</td><td>-0,003</td><td>0,003</td><td>0.001</td><td>0.874</td></tr><tr><td>Beta</td><td>Pz</td><td>Source</td><td>-0,001</td><td>-0,245</td><td>0,245</td><td>0.122</td><td>0.98</td></tr><tr><td>Beta</td><td>Pz</td><td>Trial:Source</td><td>0</td><td>-0,002</td><td>0,003</td><td>0.001</td><td>1.191</td></tr></table>

Each model reported has been computed three times 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 three 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 averaged across both experiments (defined as reference for subsequent comparisons for the Source predictor). For the 'Source' predictor, the estimate refers to the group-level effect, during the first trial of the session (modality of Trial predictor defined as reference for subsequent comparisons), when comparing the group for which the source of the feedback update was Online alpha (current experiment) and the group for which it was Offline alpha (experiment of Maaz et al., 2024). The 'Lower' and 'Upper' columns correspond to the minimal lower and maximal upper bounds of the three 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).

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