# Neuromodulation with transparent textured flicker preserves Alpha-band entrainment and improves visual comfort: A flanker paradigm

Blanc Clément, Dehais Frédéric, Scannella Sébastien

► To cite this version:

Blanc Clément, Dehais Frédéric, Scannella Sébastien. Neuromodulation with transparent textured flicker preserves Alpha-band entrainment and improves visual comfort: A flanker paradigm. International Journal of Psychophysiology, 2026, 220, 10.1016/j.ijpsycho.2025.113307. hal-05510790

HAL Id: hal-05510790

https://hal.science/hal-05510790v1

Submitted on 14 Feb 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Full Length Article

# Neuromodulation with transparent textured flicker preserves Alpha-band entrainment and improves visual comfort: A flanker paradigm

Clément Blanc* , Frédéric Dehais, Sébastien Scannella

Fédération ENAC ISAE-SUPAERO ONERA, Université de Toulouse, 10 Avenue Marc Pélegrin, Toulouse, 31400, France

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

ARTICLE INFO

Keywords:

Alpha oscillations

Flickers

Visual entrainment

Attention

Steady-state visually evoked potentials

(SSVEPs)

A B S T R A C T

Rhythmic visual flicker entrains cortical oscillations and can modulate selective attention, yet its practical use is often hampered by discomfort. We compared alpha-band (10 Hz) with a high-frequency control (30 Hz) flickers under three contrast conditions: full-contrast, near-threshold low-contrast, and a textured low-contrast "ricker". Twenty adults performed a modified Eriksen flanker task while recording occipital steady-state visually evoked potentials (SSVEPs). Across all intensities, 10 Hz flickers yielded SSVEPs approximately three times larger than 30 Hz, confirming its efficacy in alpha entrainment. At the behavioral level, alpha stimulation selectively reduced flanker interference – without evidence of a uniform suppression of flanker processing – compared to 30 Hz stimulation. Importantly, stimulus design had a marked impact on user comfort: the ricker flicker elicited reliable alpha-band entrainment while being rated as the most comfortable. These results demonstrate that visual entrainment in the alpha-band can enhance attentional control and that textured low-contrast flicker provides a promising pathway for comfortable and effective neuromodulation.

1. Introduction

The human brain exhibits a remarkable ability to adapt to rhythmic
sensory inputs by aligning its neural activity with the temporal struc-
ture of external stimuli (Lakatos et al., 2019). This phenomenon, known
as neural entrainment, is particularly evident in response to periodic
visual or auditory signals and underpins the generation of Steady-
State Visual Evoked Potentials (SSVEPs). These sustained oscillatory
responses, predominantly observed in occipital cortical regions, arise
when the visual system is stimulated by rapidly flickering inputs (Rapid
Visual Stimuli, RVS) and can be reliably measured using electroen-
cephalography (EEG) (Vialatte et al., 2010). SSVEPs have become
a cornerstone in cognitive neuroscience due to their robustness and
specificity (see Norcia et al. (2015) for a review). Frequency-tagging
paradigms, in which visual stimuli flicker at predefined rates, serve as
powerful tools for exploring the relationship between brain oscillations
and cognitive functions, including attention (Gulbinaite et al., 2019),
face perception (Boremanse et al., 2013), working memory (Ellis et al.,
2006), and language processing (Wang et al., 2021), among others.

Building on these foundations, a growing body of research has
aimed to go beyond passive observation by actively modulating brain
oscillations through frequency-specific visual entrainment (Schwab
et al., 2006). Notably, short visual training sessions using flicker in the
alpha range (10–12 Hz) have been shown to induce measurable changes
in occipital alpha activity. These include phase synchronization (Kizuk

and Mathewson, 2017; Keitel et al., 2019), increases in power (De Graaf et al., 2013; Spaak et al., 2014) and shifts in peak frequency (Ronconi and Melcher, 2017), suggesting that neural networks can be dynamically tuned to the frequency of stimulation (see Gallina et al. (2023) for a review). Collectively, these findings demonstrate that visual entrainment not only reflects but can also shape ongoing oscillatory activity, thereby modulating perceptual and cognitive processing (Mathewson et al., 2012; De Graaf et al., 2013; Spaak et al., 2014; Kizuk and Mathewson, 2017; Keitel et al., 2019).

Understanding the interaction between visual perception and al-
pha oscillations is crucial, as numerous studies have highlighted the
role of alpha-band activity in parieto-occipital cortices in the active
suppression of irrelevant visual information (Heinrichs-Graham and
Wilson, 2015; Jensen and Mazaheri, 2010). As an example, visual target
processing has been shown to depend on alpha power (Hanslmayr
et al., 2007; Romei et al., 2008) that can be modulated using alpha
entrainment (Wiesman et al., 2019). Furthermore, when attention is
directed toward a visual target, increased alpha activity in unattended
locations improves suppression of distractors (Worden et al., 2000).
Supporting this perspective, Wiesman and Wilson (2019) demonstrated
that entraining lateral flankers at 10 Hz in a modified Eriksen flanker
task (Eriksen and Eriksen, 1974) significantly reduced the congruency
effect—that is, the reaction time difference between incongruent and

<footnote>* Corresponding author.
E-mail address: clement.blanc@isae-supaero.fr (C. Blanc).</footnote>
congruent trials. This reduction was attributed to a facilitated inhibitory mechanism, which effectively suppressed the processing of lateral flankers (i.e., incongruent flankers). In contrast, entraining flankers at 30 Hz had no measurable effect, underscoring the frequency-specific nature of alpha-band modulation. These results provide compelling causal evidence that alpha-band entrainment supports distractor suppression, reinforcing the view that alpha oscillations act not only as markers of attentional deployment but also as active filters that gate irrelevant sensory input during visual processing.

Despite their advantages, SSVEP-based approaches pose challenges for cognitive enhancement via neuromodulation. With respect to the entrainment study apparatus, the intensity of visual stimulation is typically increased in terms of luminosity or contrast to ensure maximal neural responses. However, prolonged exposure to repetitive visual stimulation can trigger visual discomfort, eye strain, mental fatigue, drowsiness and – in photosensitive individuals – an elevated risk of seizure induction (Fisher et al., 2005). These adverse effects often constrain the length of SSVEP recording sessions. A straightforward countermeasure is to reduce the depth of amplitude modulation – that is, the contrast difference between successive states of the flicker – thus lowering both contrast and luminance (Mouli and Palaniappan, 2016; Ladouce et al., 2022). Crucially, even when attenuated to near-threshold levels, rhythmic visual stimulation (RVS) remains perceptible and continues to elicit reliable SSVEPs. Recent work shows that almost imperceptible flicker can still track moment-to-moment fluctuations of attention (Ladouce et al., 2025), and that periliminal flicker yields SSVEP amplitudes sufficient for decoding spatial attention in frequency-tagging paradigms (Ladouce and Dehais, 2024). Together, these findings underline the potential of low-contrast flicker to balance participant comfort with robust neural tagging. Finally, an alternative or complementary approach involves using a novel quasi-invisible flickering stimulus with small contrast textures, developed by our team (called “ricker”), which significantly enhances visual comfort. It is hypothesized that sharply contrasting borders may offer more distinct and salient visual cues to receptive fields, potentially amplifying brain responses (Dehais et al., 2024).

These studies consistently report improved visual comfort and preserved EEG signal quality compared to conventional flickers (Dehais et al., 2024). However, to our knowledge, none have examined whether such textured flickers can be used as a form of neural entrainment to modulate behavioral performance. Our study therefore extends this line of work by testing whether textured alpha-band flickers can influence attentional control within a flanker task. Combining amplitude depth reduction with ricker-textured stimulation thus presents promising prospects for the development of “transparent” visual neuromodulation techniques.

The present study has two main objectives. First, we replicate Wiesman and colleagues' flanker paradigm by presenting flankers flickering at 10 Hz versus a control frequency (30 Hz) to confirm frequency-specific alpha-band modulation of flankers interference, reflected in a reduced behavioral congruency effect (Jensen and Mazaheri, 2010; Klimesch, 2012). This replication serves as a necessary validation step to ensure frequency specificity prior to evaluating novel stimulation designs. Second, we investigate whether comfort-preserving flicker alternative, specifically periliminal low-contrast flickers and textured (“ricker”) flickers can achieve comparable neural entrainment and behavioral modulation while improving visual comfort. We hypothesize that alpha-band (10 Hz) flicker of flankers will reduce the congruency effect, reflected in faster response times and smaller accuracy differences between incongruent and congruent trials, relative to control frequencies. Moreover, we expect that low amplitude and ricker flickers will produce comparable behavioral improvements while being rated as more comfortable. At the neural level, we predict stronger occipital SSVEP responses at 10 Hz compared to 30 Hz (i.e., control frequency), reflecting effective entrainment of alpha oscillations. Plain high contrast flicker should elicit the largest SSVEP amplitudes, while

periliminal and periliminal textured “ricker” stimuli are expected to yield measurable but reduced responses consistent with their lower perceptual salience. By jointly assessing EEG and behavioral outcomes, this work aims to clarify how rhythmic visual input in the alpha band shapes both neural synchronization and selective attention in a more user-friendly format.

## 2. Materials and methods

### 2.1. Participants

20 volunteers, aged 19 to 40 years (mean age = 23.6, sd = 4.53, 11 males), took part in this study. The participants were compensated €10 for their participation; all had normal or corrected-to-normal vision. None reported neurological, psychological, or psychiatric disorders. They all provided their written informed consent prior to participate in this study which was approved by the Research Ethics Committee of the Federal University of Toulouse (No. 2024-857), and conformed to the principles expressed in the Declaration of Helsinki.

### 2.2. Experimental design

#### 2.2.1. Stimuli and procedure

The experimental paradigm was adapted from Wiesman and Wilson (2019) and was based on a modified version of the Eriksen flanker task uses arrow stimuli (Fig. 1). The task was programmed in Python (version 3.8), and stimuli were presented on a 24-inch LCD monitor (1920 × 1080 pixels, 60 Hz refresh rate) using a sin-wave flicker at either 10 Hz or 30 Hz. The participants were seated approximately 1 meter from the screen.

The experiment began with a five-minute resting-state task, during which participants alternated 30-second periods with their eyes closed and eyes open. This was followed by a practice session consisting of 48 trials. The main task included six experimental blocks, each comprising 150 trials. The experimental conditions varied in flicker frequency (10 Hz vs. 30 Hz, the latter serving as a control condition outside the 8–12 Hz brain’s alpha band) and stimulus intensity which included plain flicker (standard luminance contrast), periliminal threshold, and periliminal threshold with a contrasted texture (the “ricker” texture developed by our team, Ladouce et al. (2022), Fig. 1). These conditions were randomized among the participants. We adjusted the amplitude depth, which ranges from 1.0 (high intensity) to 0 (invisible), to ensure that the flickers were comfortable while still maintaining their effectiveness. The amplitude depth was set to 1.0 for plain flicker, 0.2 for periliminal, and 0.3 for the ricker texture. Specifically, the 1.0 value corresponds to the maximal contrast used for conventional SSVEP flickers; the 0.3 value follows earlier work from our laboratory (Ladouce et al., 2022) using ricker textures; and the 0.2 value was determined from pilot sessions as providing an optimal trade-off between visual comfort and reliable SSVEP responses.

Each block started with a five-minute flickering stimulation period, during which participants completed 50 trials per condition (congruent and incongruent). This was followed by a 2-minute period during which participants performed the same task without flickering (25 trials per condition). A three-minute rest period was provided between blocks to allow neural activity to return to baseline and to ensure participant comfort. At the end of each block, participants completed four questionnaires assessing their subjective experience of flicker stimuli. The entire experiment lasted approximately one hour.

Each trial began with a fixation cross (diamond-shaped) displayed at the center of the screen for 1000 ms. This was followed by the presentation of four lateral flankers (also diamond-shaped) flickering stimuli at either 10 Hz or 30 Hz for 1500 ms. These flankers were shown at one of three intensity levels: standard luminance contrast, periliminal threshold, or ricker. Next, a row of five arrows appeared in the same locations as the preceding stimuli (central fixation and four flankers)
<figure><img src="image_3.png" /><figcaption>Fig. 1. Type of flankers.</figcaption></figure>

and remained visible for 2000 ms or until the participant responded (Fig. 2). Participants were instructed to indicate the direction of the central arrow as quickly and accurately as possible: pressing the “a” key with their left index finger for left-pointing arrows, and the “p” key with their right index finger for right-pointing arrows on a low-latency AZERTY keyboard (Corsair K70 core). The number of congruent and incongruent trials, as well as left and right responses, was balanced throughout the task.

Alpha-band oscillations govern attention and sensory processing both before and during stimulus presentation (Foxe and Snyder, 2011; Van Diepen et al., 2016). However, most studies aiming to entrain these rhythms through rhythmic visual stimulation in the alpha range (Spaak et al., 2014; Notbohm et al., 2016; Thut et al., 2011) have presented the task-relevant stimulus only after the flicker train ended, allowing the rhythmic stimulation to act as a temporal cue for target onset. Under such conditions, behavioral modulations may therefore reflect temporal expectation rather than genuine neural entrainment (Mathewson et al., 2012; De Graaf et al., 2013; Spaak et al., 2014; Wiesman and Wilson, 2019), especially given the known involvement of alpha activity in anticipatory timing processes (Rohenkohl and Nobre, 2011). To minimize temporal prediction while maintaining rhythmic stimulation, we presented continuously flickering flankers throughout stimulus processing, following the approach of Gulbinaite et al. (2017). This design maintained frequency-specific entrainment during task performance but provided no discrete temporal markers for target onset, ensuring that any behavioral modulation could be attributed to ongoing alpha-band synchronization rather than to temporal anticipation (Breska and Deouell, 2014).

To minimize eye movements, participants were asked to maintain their gaze on a small green dot displayed in the center of the screen throughout the experiment. The arrows were positioned at five equally spaced locations along the horizontal axis of the screen, with each arrow measuring 120 pixels in both width and height. The angular position of each arrow was 1.90°. Diamond-shaped stimuli were selected to match the height and width of the arrow stimuli, ensuring consistent spatial coverage across visual conditions.

To better illustrate the visual stimuli used in the task, we include a video (Video S1) showing each flicker type as it appears within a trial.

### 2.2.2. EEG data acquisition and preprocessing

EEG data were recorded at a sampling rate of 512 Hz using a 32-channel Active-two Biosemi system, with electrodes positioned according to the international 10–20 system and mounted in an elastic cap. Data preprocessing was performed using EEGLAB (version 2024) and custom MATLAB (version R2024a) scripts. Offline, the EEG data were re-referenced to the average of all scalp electrodes and band-pass filtered between 1 and 40 Hz using a zero-phase finite impulse response (FIR) filter. Channels exhibiting excessive noise defined as exceeding 3 standard deviations from the median power spectrum were identified via spectral thresholding. These channels were removed and subsequently interpolated using spherical interpolation based on

neighboring electrode signals (mean = 0.98, SD = 0.78). Independent Component Analysis (ICA) was then performed using the acsobiro pipeline with the InfoMax algorithm to identify and remove non-neural artifacts. Components associated with artifacts (e.g., eye movements, muscle activity, cardiac signals, line noise, or poor electrode contact) were identified using the ICLabel algorithm (version 1.5), followed by a visual inspection to ensure data quality. Components with a classification confidence greater than 70% for any artifact category were excluded from the data. No separate EOG electrodes were recorded. On average, approximately 6.5 components (SD = 2.16) were removed per participant. Finally, the artifact-free continuous EEG signal was segmented into epochs based on stimulus presentation. Each epoch spanned from –500 to 2200 ms relative to the stimulus onset.

### 2.2.3. SSVEP: RESS method

Rhythmic Entrainment Source Separation (RESS) is an EEG signal processing technique designed to isolate and extract neural responses to rhythmic sensory stimulation (Cohen and Gulbinaite, 2017). This method leverages the principle of rhythmic entrainment, whereby brain activity becomes synchronized with the frequency of an external rhythmic stimulus, thereby enhancing the detection of frequency-specific neural responses. RESS employs matrix decomposition techniques, such as Generalized Eigenvalue Decomposition (GED), to extract neural components related to a specific frequency while minimizing the influence of noise and unrelated brain activity. Unlike traditional approaches that analyze SSVEPs based on electrodes showing maximum power at the tagging frequency, RESS computes a spatial filter based on a linearly weighted combination of all electrodes. This approach allows optimal separation of frequency-specific SSVEPs from background neural activity.

First, EEG data were filtered using a Gaussian filter (full-width at half-maximum [FWHM] = 1 Hz) centered on the stimulation frequency (10 or 30 Hz) and its neighboring frequencies. Separate covariance matrices were computed for the stimulation frequency (S) and for the surrounding frequencies (R). The R matrix was generated by filtering the data at frequencies slightly above and below the target frequency (i.e. +/- 1 Hz), and then averaging the resulting covariance matrices. A GED was applied to the S and R matrices to derive spatial filters, with the eigenvector corresponding to the largest eigenvalue selected as the primary RESS component. To enhance spatial precision, zero-phase Component Analysis (ZCA) whitening was applied to the EEG data. The selected RESS component was then back-projected onto the EEG data, functioning as a spatial filter to enhance the signal-to-noise ratio (SNR) of the SSVEP response. SNR was computed by comparing the power at the stimulation frequency to the power in neighboring frequency bands, thereby improving the identification of stimulus-locked neural activity. The resulting RESS components were analyzed to examine the frequency spectra and time courses of the components across participants and experimental conditions.
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Experimental paradigm. Each trial began with a central fixation diamond displayed for 1000 ms, followed by four lateral diamond shaped flankers flickering at either 10 Hz or 30 Hz for 1500 ms at one of three intensity levels (plain, periliminal, or ricker). Subsequently, a row of five arrows (one not flickering central target and four flickering flankers) appeared for up to 2000 ms or until response. Participants indicated the direction of the central arrow. The central diamond served as fixation, while the lateral diamonds acted as flickering flankers.</figcaption></figure>

*Source:* Adapted from Wiesman and Wilson (2019).

### 2.2.4. Behavioral responses

Participants' accuracy and response speed to target stimuli (i.e., the central arrow) were recorded. For each participant, reaction times (RTs) were extracted on a trial-by-trial basis. Trials were excluded if they met any of the following criteria: no response, incorrect response, RTs shorter than 150 ms, or RTs exceeding 3 standard deviations above the participant's mean within each condition. The remaining RTs were then averaged for each participant across flicker intensity levels (plain vs. periliminal, vs. 'ricker'), stimulation frequencies (10 Hz vs. 30 Hz), and trial types (congruent vs. incongruent).

On average, 277 out of 300 trials (SD = 14.88) were included per participant for congruent conditions, and 272 out of 300 trials (SD = 15.08) for incongruent conditions, after applying all exclusion criteria. To evaluate participants' ability to manage interference and inhibit automatic responses in favor of flankers, we computed the congruency effect, defined as the difference between RTs for incongruent and congruent trials. Larger values could either indicate greater facilitating effect of congruent flankers or greater interference from incongruent flankers.

### 2.2.5. Questionnaires

At the end of each block, participants completed a series of questions designed to assess their subjective experience of the flickering stimuli. Specifically, the questionnaires assessed visual comfort and mental fatigue. Responses were collected using an 11-point Likert scale ranging from 0 to 10. The questions were formulated as follows: How did you perceive the stimuli in terms of visual fatigue, neutrality, or comfort? (0 = highly uncomfortable, 10 = very comfortable); What was your level of mental fatigue after this block? (0 = very fatigued, 10 = not fatigued).

### 2.2.6. Statistical analyses

Statistical analyses were carried out with JASP (version 0.19.3) software. A series of repeated-measures analysis of variance (ANOVA's) were conducted to investigate the effects of stimulation frequency (10 Hz vs. 30 Hz), flicker intensity (plain vs. periliminal vs. ricker), and trial condition (congruent vs. incongruent) on behavioral performance, subjective ratings, and EEG measures. A 2 (frequency) × 3

(flicker intensity) repeated-measures ANOVA was first applied to SSVEP signal-to-noise ratio (SNR) values obtained using the RESS method. To complement this amplitude-based analysis, the same ANOVA was conducted on inter-trial phase coherence (ITPC) as a measure of phase-locking consistency. Lastly, we computed correlations between participants' SSVEP SNR values and the absolute distance between their individual alpha frequency (IAF) and the 10 Hz stimulation frequency. Next, a 2 (frequency) × 3 (flicker intensity) × 2 (condition) ANOVA was performed on reaction times (RTs), followed by a 2 (frequency) × 3 (flicker intensity) ANOVA on the congruency effect, defined as the RT difference between incongruent and congruent trials. Finally, a 2 (frequency) × 3 (flicker intensity) ANOVA was conducted on participants' subjective ratings of visual comfort and mental fatigue. When applicable, post hoc comparisons were performed using Holm's correction (Holm, 1979) to adjust p-values for multiple comparisons.

## 3. Results

### 3.1. SSVEPs analyses

Our results indicated that both flicker frequency and intensity significantly influenced SSVEP signal quality. As illustrated in Fig. 3, a significant main effect of frequency for the SNR values was observed ($F(1, 19) = 45.682, p < .001, \eta^2 = .589$), indicating that 10 Hz flickers elicited substantially higher SNR than 30 Hz flickers ($d = 1.676$). Flicker intensity also had a significant effect, with overall SNR differing across the three intensity levels ($F(2, 38) = 17.948, p < .01, \eta^2 = .043$). Follow-up post-hoc comparisons showed that the plain intensity flicker produced a significantly higher SNR than both the periliminal flicker ($p < .001, d = .555$) and ricker ($p < .05, d = .284$). Moreover, the ricker flicker yielded greater SNR than the periliminal flicker ($p < .002, d = .271$).

Importantly, an interaction between frequency and flicker intensity was found to significantly affect the SNR ($F(2, 38) = 8.178, p < .05, \eta^2 = .023$). Further post-hoc comparisons indicated that, for each flicker intensity level, the 10 Hz flicker yielded significantly greater SNR than its 30 Hz counterpart ($p < .001, d = 2.107$ for plain; $p < .05, d = 1.295$ for periliminal; $p < .05, d = 1.626$ for ricker). Furthermore, within the 10 Hz
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Steady-States Visually Evoked Potentials (SSVEP) Signal-to-Noise (SNR) responses across flicker intensities and frequencies. Vertical bars depict the standard error (SEM), and asterisks indicate statistical significance levels (*** for *p* < 0.001 and *p* < .05).</figcaption></figure>

condition, SNR was significantly higher for the plain flicker compared to the perilimal flicker (*p* < .05, *d* = .917). No significant differences were found between the plain and ricker flickers conditions at 10 Hz (*p* = 0.228, *d* = .745).

Our data showed that both flicker frequency and intensity signif-
icantly influenced phase-locking consistency (ITPC). As illustrated in
Fig. 4, a significant interaction between frequency and flicker intensity
was also observed (F(2, 38) = 9.416, *p* < .001, *η*² = .066). Follow-
up comparisons showed that, for both the plain and ricker intensity
levels, the 10 Hz flicker elicited markedly higher ITPC than its 30 Hz
counterpart (*p* < .001, *d* = 2.154 for plain; *p* < .001, *d* = 1.698 for ricker).
No significant difference was found between perilimal flicker at 10 Hz
and 30 Hz (*p* = .245, *d* = .710). Within the 10 Hz condition, ITPC was
significantly higher for the plain flicker than for the perilimal flicker
(*p* = .004, *d* = 1.565), and for the ricker compared to the perilimal
flicker (*p* < .001, *d* = .938). Finally, no significant difference was
found between the plain and ricker flicker conditions at 10 Hz (*p* =
.322, *d* = .627). There was also significant main effect of frequency on
ITPC values (F(1, 19) = 39.839, *p* < .001, *η*² = .417), showing that 10 Hz
flickers elicited substantially higher phase consistency than 30 Hz
flickers (*d* = 1.521). Flicker intensity also had a significant main effect
(F(2, 38) = 16.222, *p* < .01, *η*² = .086), with overall ITPC differing across
the three intensity levels. Post hoc comparisons revealed that the plain-
intensity flicker produced significantly higher phase consistency than
both the perilimal (*p* < .001, *d* = .843) and ricker (*p* = .024, *d* = .399)
flickers. In addition, the ricker flicker yielded greater phase consistency
than the perilimal flicker (*p* < .001, *d* = .444).

Finally, we tested whether entrainment strength depended on the match between stimulation and intrinsic alpha frequency. As lustrated in Fig. 5, significant negative correlations were observed between SNR and the distance from 10 Hz to participants’ IAF for both the plain (*p* = .028; *r* = -.49) and ricker (*p* = .016; *r* = -.53) conditions, confirming stronger SNR as stimulation frequency approached individual alpha peaks.

In summary, 10 Hz stimulation elicited higher SNR and ITPC val-
ues than 30 Hz across all intensity levels, confirming stronger alpha-
band entrainment. The plain flicker produced the strongest responses,
whereas perilimal and ricker flickers yielded slightly weaker but
reliable entrainment, particularly when stimulation was close to each

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Inter-trial phase coherence (ITPC) results. Mean ITPC is shown for each combination of flanker flickering frequency (10 Hz and 30 Hz) and intensity condition (plain, perilimal and ricker). Vertical bars represent the SEM, and asterisks denote significance (*** *p* < .001; ** *p* < .01).</figcaption></figure>

participant's IAF. Notably, the Ricker condition produced clear phase-
locked responses at 10 Hz, indicating that textured modulation can
support reliable temporal synchronization while improving visual com-
fort.

3.2. *Subjective measures*

Subjective ratings showed that visual comfort was influenced by stimulation parameters, while mental fatigue remained unaffected. As depicted in Fig. 6, subjective ratings of visual comfort were significantly affected by both frequency (F(1, 19) = 13.178, *p* < .01, *η*² = .048) and flicker intensity (F(2, 38) = 26.925 *p* < .001, *η*² = .378). In other words, participants reported the lowest comfort under the 10 Hz, plain intensity flicker and the highest comfort in the ricker intensity condition. However, the interaction between frequency and flicker intensity was not significant (F(2, 38) = 2.480, *p* = .097).

When it came to mental fatigue, neither flicker frequency nor in-
tensity, nor their interaction, produced any significant differences in
participants' fatigue ratings.

Taken together, our findings indicate that low-frequency (10 Hz) stimulation at plain intensity markedly reduced visual comfort whereas ricker flicker was rated as the most comfortable. None of these stimulation parameters affected self reported mental fatigue.

3.3. *Behavioral performance : congruency effect*

To assess the influence of visual flicker parameters on conflict
processing, we examined the congruency effect (RTs difference between
incongruent and congruent trials). Our findings indicated that both
flicker frequency and intensity modulated the congruency effect. As
shown in Fig. 7, a significant main effect of frequency was found
(F(1, 19) = 4.839, *p* < .05, *η*² = .029; Fig. 7A). Specifically, alpha-band
(10 Hz) stimulation reduced the congruency effect compared to 30 Hz
stimulation (*p* < .05, *d* = −0.347, mean difference: 4.29 ms). Addi-
tionally, flicker intensity also had an influence (F(2, 38) = 18.096, *p* <
.001, *η*² = .230; Fig. 7B), with a congruency effect larger in plain
compared to both perilimal (*p* < .001, *d* = 1.154) and ricker flicker
types (*p* < .001, *d* = 0.846) and no difference were found between
<figure><img src="image_7.png" /><figcaption>Fig. 5. Relationship between SSVEP SNR and the proximity of stimulation to individual alpha frequency (IAF). Each panel shows the correlation between SNR and the distance from 10 Hz to IAF for the plain, periliminal, and ricker flickers.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 6. Visual comfort assessment across flicker intensity and frequency. Higher values indicate better visual comfort. Vertical bars represent the SD, and asterisks denote significance (*** $p < .001$, ** $p < .01$).</figcaption></figure>

periliminal and ricker flickers ($p = 0.176, d = -.308$). However, no significant interaction between the frequency and the flicker intensity has been observed ($F(2, 38) = 0.707, p = .500$; Fig. 7C).

Overall, our results highlight that 10 Hz flicker and reduced intensity flicker both attenuate the congruency effect.

To further explore the origin of the congruency effect – specifically, whether reduced RTs in incongruent trials under 10 Hz flicker result from enhanced inhibitory control of lateral flankers – we analyzed RTs across all conditions. As illustrated in Fig. 8, a significant second-level interaction between frequency and congruency was observed for RTs ($F(1, 19) = 4.849, p < .05, \eta^2 = .003$). Under 30 Hz

stimulation, participants responded significantly faster on congruent compared to incongruent trials ($p < .01, d = -.125$, difference means : 6.37 ms). In contrast, at 10 Hz, this difference disappeared ($p = .975, d = -.041$), suggesting equivalent response times across conditions (difference means : 2.09 ms) (Fig. 8).

Collectively, these results suggest that both 10 Hz flicker and lower flicker intensities diminish the congruency effect, supporting the notion that visual entrainment can modulate conflict-processing mechanisms.

## 4. Discussion

The present study pursued two objectives: first, to assess whether periodic flickering flankers elicits stimulus-locked (frequency-tagged) alpha activity that can modulate cognition supported by those rhythms (Williams, 2001; De Graaf et al., 2013) and second, to evaluate whether our novel low-contrast flicker designs (periliminal and textured ricker) (Ladouce et al., 2022) can preserve neural entrainment and behavioral modulation while improving visual comfort.

### 4.1. Neural entrainment and visual comfort

In the present study, we used a modified arrow-based version of Eriksen flanker task (Wiesman and Wilson, 2019; Eriksen and Eriksen, 1974) paired with 10 Hz or 30 Hz flickering stimuli on flankers. Consistent with previous findings (Norcia et al., 2015; Herrmann, 2001), the stronger SSVEP SNR observed at 10 Hz relative to 30 Hz likely reflects the combined effects of low-pass filtering in the visual pathway and the higher sensitivity of the human visual system to lower temporal frequencies. This enhanced alpha-band response aligns with prior findings of visual system sensitivity to alpha frequencies (Mathewson et al., 2012; Spaak et al., 2014; Keitel et al., 2019; Ladouce et al., 2022) and our full-contrast 10 Hz SNR matches closely those reported by Gulbinaite et al. (2017), further confirming the robustness of 10 Hz flicker for selective attention paradigms.

Beyond confirming frequency-specific effects, we aimed to reduce the discomfort associated with high-contrast flicker by testing two alternative designs: reduced luminance contrast flicker (i.e., perilim-inal flicker) and textured modulation (i.e., ricker flicker) (Ladouce et al., 2022; Ladouce and Dehais, 2024). Periliminal flicker resulted in lower SNR values compared to standard flicker, indicating that decreasing flicker visibility weakens phase-locked neural responses. Moreover, SSVEP SNR correlated negatively with the distance be-tween the 10 Hz stimulation frequency and each participant's individ-ual alpha frequency (IAF), replicating prior findings that entrainment strength peaks when stimulation frequency closely matches intrinsic
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Congruency effect (RTs difference between incongruent and congruent trials). A. Congruency effect (ms) across frequency. B. Congruency effect (ms) across flicker intensity. C. Congruency effect (ms) across flicker intensity and frequency. Vertical bars represent the standard error (SD), and asterisks denote statistical significance levels (*** for *p* < .001 and * for *p* < 0.05).</figcaption></figure>

alpha rhythms (Gulbinaite et al., 2017; Notbohm and Herrmann, 2016). These results align with the Arnold Tongue framework (Pikovsky et al., 2003; Notbohm and Herrmann, 2016), which posits that the strength of neural entrainment depends not only on the frequency match between external stimulation and intrinsic brain rhythms, but also on stimulus intensity (Notbohm and Herrmann, 2016). Crucially, at 10 Hz, plain flickering flankers produced the strongest SSVEP response, while both periliminal and ricker flickers still generated reliable SNRs, demonstrating effective visuocortical entrainment even at reduced contrast or with textured modulation. Importantly, participants rated the ricker flicker as more visually pleasant and less distracting, showing that substantial gains in comfort can be achieved without eliminating alpha-band neural responses. Beyond amplitude measures, ITPC analyses confirmed that the textured flicker also elicited clear phase-locked activity at 10 Hz, indicating that it can support temporal synchronization within the alpha band. Together, these results demonstrate that textured modulation can sustain reliable alpha-band entrainment while markedly improving visual comfort, a combination that is highly relevant for future neuromodulation and BCI applications.

## 4.2. Congruency effect

To assess whether 10 Hz flicker – particularly Ricker-modulated 10 Hz stimulation – translates into measurable behavioral effects, we compared the flanker task congruency effect under both 10 Hz and 30 Hz flicker at three contrast levels. Consistent with prior work, full-contrast 10 Hz stimulation significantly reduced the incongruent–congruent RT difference compared to 30 Hz (Wiesman and Wilson, 2019; Gulbinaite et al., 2017).

Importantly, flicker intensity exerted a robust main effect independent of frequency: both ricker and periliminal flickers markedly attenuated the congruency effect compared to plain contrast, confirming that conflict processing scales with flankers salience. Although no *frequency × intensity* interaction reached significance, descriptive trends revealed that 10 Hz stimulation produced a numerically greater reduction in the congruency effect only in conditions showing at least some degree of entrainment (plain and ricker). Periliminal flicker, by contrast, abolished behavioral differences altogether, likely because flankers (i.e., lateral arrows) fell below perceptual threshold. One
<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Mean reaction times (ms) displayed as a function of condition at 10 Hz and 30 Hz. Vertical bars represent the standard error (SD), and asterisks denote statistical significance levels (*** for $p < .001$ and * for $p < .05$).</figcaption></figure>

possible explanation for the non-significant reduction in the congruency effect observed under both ricker and plain flicker is that the textured spatial pattern of the ricker flicker may more effectively engage distributed visual neurons sensitive to edges and orientation. This may result in a more stable or spatially focused synchronization that does not necessarily boost overall SNR but increases phase consistency, which is sufficient to influence attentional networks and reduce flanker interference. By contrast, the periliminal flicker may have been too weak or lacked sufficient visual saliency to elicit comparable behavioral modulation, whereas the plain and ricker flickers tended to produce similar behavioral patterns – suggesting a potential modulation of performance – even though these effects did not reach statistical significance.

To disentangle facilitation and interference components, we next analyzed mean RTs for each experimental condition. With 30 Hz stimulation, congruent trials remained statistically faster than incongruent ones whereas 10 Hz flicker abolished this difference, slowing congruent RTs until they matched incongruent trials. This mirrors (Wiesman and Wilson, 2019)'s observation that alpha entrainment both speeds incongruent responses and slows congruent ones. We interpret these findings as evidence that alpha-band flicker induces a blanket suppression of flanker processing – facilitation and interference alike – consistent with the view that parieto-occipital alpha oscillations implement active inhibition of visual cortex (Jensen and Mazaheri, 2010; Calderone et al., 2014; Spaak et al., 2014; Thut et al., 2011). Importantly, however, the magnitude of behavioral change was modest. As in prior work, the impact of neural entrainment on performance appears subtle and yields small effect sizes. These findings therefore call for cautious interpretation and underscore the need for larger samples and refined stimulus parameters in future studies.

### 4.3. Limitations and perspectives

This study has several limitations. First, we did not employ an individualized staircase procedure to establish each participant's perceptual threshold for periliminal flicker. As a result, variability in near-threshold perception may have contributed to the reduced SSVEP SNR and the absence of behavioral effects at low contrasts. Future work should use adaptive thresholding to precisely titrate flicker visibility and more accurately assess neural entrainment strength near perceptual limits.

Second, our sample size ($N = 20$) may have constrained statistical power, particularly for detecting smaller frequency-specific effects at plain and ricker intensities. This relatively small sample also limits the generalizability of the present findings, which should therefore be considered preliminary and pilot in nature. A larger cohort would not

only allow confirmation of the descriptive trends observed here but also enable modeling of inter-individual differences – such as intrinsic alpha-band variability – which are known to modulate responses to rhythmic visual stimulation (Nuttall et al., 2022).

Finally, our study did not include a true no-flicker baseline condition. Because our primary aim was to examine frequency-specific effects of rhythmic visual stimulation on distractor processing, we employed an active-control design contrasting 10 Hz (alpha) and 30 Hz (non-alpha) flicker, following prior flanker-entrainment studies that likewise compared two active frequencies without a concurrent no-flicker block (e.g., Gulbinaite et al. (2017), Wiesman and Wilson (2019)). This approach maximizes construct validity for isolating frequency-specific effects and maintains a constant visual stimulation context for steady-state tagging across trials. Nevertheless, including a no-flicker baseline in future work would provide a valuable behavioral reference, clarifying whether the 10 Hz–30 Hz difference reflects facilitation under 10 Hz, impairment under 30 Hz, or a combination of both.

Addressing these issues (individualized thresholding and increased sample size) will be critical for translating alpha-band flicker techniques into robust tools for cognitive enhancement and clinical neuromodulation.

### 4.4. Conclusion

Our study shows that 10 Hz flicker reliably entrains alpha-band activity in a flanker task, replicating key findings from Gulbinaite (2017) and Wiesman (2019). Crucially, our low-contrast “Ricker” flicker elicits reliable alpha-band entrainment, while greatly improving visual comfort, making it a practical alternative for SSVEP and Brain Computer Interface (BCI) applications. Behavioral effects were modest and sensitive to both intensity and frequency, underscoring the need for individualized thresholding, larger samples, and systematic parameter tuning to translate neural entrainment into consistent performance gains.

## CRediT authorship contribution statement

Clément Blanc: Writing – original draft, Methodology, Investigation, Formal analysis, Conceptualization. Frédéric Dehais: Writing – review & editing, Validation, Supervision, Project administration, Funding acquisition, Conceptualization. Sébastien Scannella: Writing – review & editing, Validation, Supervision, Project administration, Methodology, Conceptualization.

## Acknowledgment

The research was funded by Defence Innovation Agency # 2023 65 0083.

## Data availability

Data will be made available on request.

## References

* Boremanse, A., Norcia, A.M., Rossion, B., 2013. An objective signature for visual binding of face parts in the human brain. J. Vis. 13 (11), 6–6.

* Breska, A., Deouell, L.Y., 2014. Automatic bias of temporal expectations following temporally regular input independently of high-level temporal expectation. J. Cogn. Neurosci. 26 (7), 1555–1571. http://dx.doi.org/10.1162/jocn_a_00564, URL https://direct.mit.edu/jocn/article/26/7/1555/28135/Automatic-Bias-of-Temporal-Expectations-following.

* Calderone, D.J., Lakatos, P., Butler, P.D., Castellanos, F.X., 2014. Entrainment of neural oscillations as a modifiable substrate of attention. Trends Cogn. Sci. 18 (6), 300–309. http://dx.doi.org/10.1016/j.tics.2014.02.005, URL https://linkinghub.elsevier.com/retrieve/pii/S1364661314000485.
Cohen, M.X., Gulbinaite, R., 2017. Rhythmic entrainment source separation: Optimizing analyses of neural responses to rhythmic sensory stimulation. *NeuroImage* 147, 43–56. http://dx.doi.org/10.1016/j.neuroimage.2016.11.036, URL https://linkinghub.elsevier.com/retrieve/pii/S105381191630653X.

De Graaf, T.A., Gross, J., Paterson, G., Rusch, T., Sack, A.T., Thut, G., 2013. Alpha-band rhythms in visual task performance: Phase-locking by rhythmic sensory stimulation. *PLoS One* 8 (3), e60035. http://dx.doi.org/10.1371/journal.pone.0060035, URL https://dx.plos.org/10.1371/journal.pone.0060035.

Dehais, F., Cabrera Castillos, K., Ladouce, S., Clisson, P., 2024. Leveraging grating-based flickers: A leap toward practical, visually comfortable, and high-performance dry EEG code-VEP bci. http://dx.doi.org/10.1101/2024.07.17.603960, URL http://biorxiv.org lookup/doi/10.1101/2024.07.17.603960.

Ellis, K.A., Silberstein, R.B., Nathan, P.J., 2006. Exploring the temporal dynamics of the spatial working memory n-back task using steady state visual evoked potentials (ssvep). *NeuroImage* 31 (4), 1741–1751.

Eriksen, B.A., Eriksen, C.W., 1974. Effects of noise letters upon the identification of a target letter in a nonsearch task. Percept. Psychophys. 16 (1), 143–149. http://dx.doi.org/10.3758/BF03203267, URL http://link.springer.com/10.3758/BF03203267.

Fisher, R.S., Harding, G., Erba, G., Barkley, G.L., Wilkins, A., 2005. Photic- and pattern-induced seizures: A review for the epilepsy foundation of america working group. *Epilepsia* 46 (9), 1426–1441. http://dx.doi.org/10.1111/j.1528-1167.2005.31405.x, URL https://onlinelibrary.wiley.com/doi/10.1111/j.1528-1167.2005.31405.x.

Foxe, J.J., Snyder, A.C., 2011. The role of alpha-band brain oscillations as a sensory suppression mechanism during selective attention. Front. Psychol. 2, http://dx.doi.org/10.3389/fpsyg.2011.00154, URL http://journal.frontiersin.org/article/10.3389/fpsyg.2011.00154/abstract.

Gallina, J., Marsicano, G., Romei, V., Bertini, C., 2023. Electrophysiological and behavioral effects of alpha-band sensory entrainment: Neural mechanisms and clinical applications. *Biomedicines* 11 (5), 1399. http://dx.doi.org/10.3390/biomedicines11051399, URL https://www.mdpi.com/2227-9059/11/5/1399.

Gulbinaite, R., Roozendaal, D.H., VanRullen, R., 2019. Attention differentially modulates the amplitude of resonance frequencies in the visual cortex. *NeuroImage* 203, 116146. http://dx.doi.org/10.1016/j.neuroimage.2019.116146, URL https://linkinghub.elsevier.com/retrieve/pii/S1053811919307372.

Gulbinaite, R., Van Vigen, T., Wieling, M., Cohen, M.X., VanRullen, R., 2017. Individual alpha peak frequency predicts 10 Hz flicker effects on selective attention. *J. Neurosci.* 37 (42), 10173–10184. http://dx.doi.org/10.1523/JNEUROSCI.1163-17.2017, URL https://www.jneurosci.org/lookup/doi/10.1523/JNEUROSCI.1163-17.2017.

Hanslmayr, S., Aslan, A., Staudigl, T., Klimesch, W., Herrmann, C.S., Bümler, K-H., 2007. Prestimulus oscillations predict visual perception performance between and within subjects. *NeuroImage* 37 (4), 1465–1473. http://dx.doi.org/10.1016/j.neuroimage.2007.07.011, URL https://linkinghub.elsevier.com/retrieve/pii/S1053811907006039.

Heinrichs-Graham, E., Wilson, T.W., 2015. Spatiotemporal oscillatory dynamics during the encoding and maintenance phases of a visual working memory task. *Cortex* 69, 121–130. http://dx.doi.org/10.1016/j.cortex.2015.04.022, URL https://linkinghub.elsevier.com/retrieve/pii/S001094521500146X.

Herrmann, C.S., 2001. Human EEG responses to 17100Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena. *Exp. Brain Res.* 137 (3-4), 346–353. http://dx.doi.org/10.1007/s002210100682, URL http://link.springer.com/10.1007/s002210100682.

Holm, S., 1979. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6 (2), 65–70, URL http://www.jstor.org/stable/4615733.

Jensen, O., Mazaheri, A., 2010. Shaping functional architecture by oscillatory alpha activity: Gating by inhibition. Front. Hum. Neurosci. 4, http://dx.doi.org/10.3389/fnhum.2010.00186, URL http://journal.frontiersin.org/article/10.3389/fnhum.2010.00186/abstract.

Keitel, C., Keitel, A., Benwell, C.S., Daube, C., Thut, G., Gross, J., 2019. Stimulus-driven brain rhythms within the alpha band: The attentional-modulation conundrum. *J. Neurosci.* 39 (16), 3119–3129. http://dx.doi.org/10.1523/JNEUROSCI.1633-18.2019, URL https://www.jneurosci.org/lookup/doi/10.1523/JNEUROSCI.1633-18.2019.

Kizuk, S.A.D., Mathewson, K.E., 2017. Power and phase of alpha oscillations reveal an interaction between spatial and temporal visual attention. *J. Cogn. Neurosci.* 29 (3), 480–494. http://dx.doi.org/10.1162/jocn_a_01058, URL https://direct.mit.edu/jocn/article/29/3/480/28589/Power-and-Phase-of-Alpha-Oscillations-Reveal-an.

Klimesch, W., 2012. Alpha-band oscillations, attention, and controlled access to stored information. Trends Cogn. Sci. 16 (12), 606–617. http://dx.doi.org/10.1016/j.tics.2012.10.007, URL https://linkinghub.elsevier.com/retrieve/pii/S1364661312002434.

Ladouce, S., Darmet, L., Torre Tresols, J.J., Velut, S., Ferraro, G., Dehais, F., 2022. Improving user experience of SSVEP BCI through low amplitude depth and high frequency stimuli design. Sci. Rep. 12 (1), 8865. http://dx.doi.org/10.1038/s41598-022-12733-0, URL https://www.nature.com/articles/s41598-022-12733-0.

Ladouce, S., Dehais, F., 2024. Frequency-tagging of spatial attention using perliminal flickers, preprint. Neuroscience http://dx.doi.org/10.1101/2024.02.29.582725, URL http://biorxiv.org/lookup/doi/10.1101/2024.02.29.582725.

Ladouce, S., Tresols, J.T., Le Goff, K., Dehais, F., 2025. Eeg-based assessment of long-term vigilance and lapses of attention using a user-centered frequency-tagging approach. *J. Neural Eng. 22* (3), 036018.

Lakatos, P., Gross, J., Thut, G., 2019. A new unifying account of the roles of neuronal entrainment. Curr. Biol. 29 (18), R890-R905. http://dx.doi.org/10.1016/j.cub.2019.07.075, URL https://linkinghub.elsevier.com/retrieve/pii/S0960982219309558.

Mathewson, K.E., Prudhomme, C., Fabiani, M., Beck, D.M., Lleras, A., Gratton, G., 2012. Making waves in the stream of consciousness: Entrainment oscillations in EEG alpha and fluctuations in visual awareness with rhythmic visual stimulation. *J. Cogn. Neurosci.* 24 (12), 2321–2333. http://dx.doi.org/10.1162/jocn_a_00288, URL https://direct.mit.edu/jocn/article/24/12/2321/27873/Making-Waves-in-the-Stream-of-Consciousness.

Mouli, S., Palaniappan, R., 2016. Eliciting higher SSVEP response from LED visual stimulus with varying luminosity levels. In: 2016 International Conference for Students on Applied Engineering. ISCAE, IEEE, Newcastle upon Tyne, United Kingdom, pp. 201–206. http://dx.doi.org/10.1109/ICSAE.2016.7810188, URL http://ieeexplore.ieee.org/document/7810188/.

Norcia, A.M., Appelbaum, L.G., Ales, J.M., Cottereau, B.R., Rossier, B., 2015. The steady-state visual evoked potential in vision research: A review. J. Vis. 15 (6), 4. http://dx.doi.org/10.1167/15.6.4.

Notbohm, A., Herrmann, C.S., 2016. Flicker regularity is crucial for entrainment of alpha oscillations. Front. Hum. Neurosci. 10, http://dx.doi.org/10.3389/fnhum.2016.00503, URL http://journal.frontiersin.org/article/10.3389/fnhum.2016.00503/full.

Notbohm, A., Kurths, J., Herrmann, C.S., 2016. Modification of brain oscillations via rhythmic light stimulation provides evidence for entrainment but not for superposition of event-related responses. Front. Hum. Neurosci. 10, http://dx.doi.org/10.3389/fnhum.2016.00010, URL http://journal.frontiersin.org/Article/10.3389/fnhum.2016.00010/abstract.

Nuttall, R., Jäger, C., Zimmermann, J., Archila-Melendez, M.E., Preibisch, C., Taylor, P., Sauseng, P., Wohlschläger, A., Sorg, C., Dowsett, J., 2022. Evoked responses to rhythmic visual stimulation vary across sources of intrinsic alpha activity in humans. Sci. Rep. 12 (1), 5986. http://dx.doi.org/10.1038/s41598-022-09922-2, URL https://www.nature.com/articles/s41598-022-09922-2.

Pikovsky, A., Rosenblum, M., Kurths, J., 2003. Synchronization: A universal concept in nonlinear sciences.

Rohenkohl, G., Nobre, A.C., 2011. Alpha oscillations related to anticipatory attention follow temporal expectations. *J. Neurosci.* 31 (40), 14076–14084. http://dx.doi.org/10.1523/JNEUROSCI.3387-11.2011, URL https://www.jneurosci.org/lookup/doi/10.1523/JNEUROSCI.3387-11.2011.

Romei, V., Brodbeck, V., Michel, C., Amedi, A., Pascual-Leone, A., Thut, G., 2008. Spontaneous fluctuations in posterior -band EEG activity reflect variability in excitability of Human Visual Areas. Cerebral Cortex 18 (9), 2010–2018. http://dx.doi.org/10.1093/cercor/bhm229, URL https://academic.oup.com/cercor/article-lookup/doi/10.1093/cercor/bhm229.

Ronconi, L., Melcher, D., 2017. The role of oscillatory phase in determining the temporal organization of perception: Evidence from sensory entrainment. *J. Neurosci.* 37 (44), 10636–10644. http://dx.doi.org/10.1523/JNEUROSCI.1704-17.2017, URL https://www.jneurosci.org/lookup/doi/10.1523/JNEUROSCI.1704-17.2017.

Schwab, K., Ligges, C., Jungmann, T., Hilgenfeld, B., Haueisen, J., Witte, H., 2006. Alpha entrainment in human electroencephalogram and magnetoeNSEphalogram recordings. NeuroReport 17 (17), 1829–1833. http://dx.doi.org/10.1097/01.wnr.0000246326.89308.ec, URL https://journals.lww.com/00001756-200611270-00015.

Spaak, E., De Lange, F.P., Jensen, O., 2014. Local entrainment of alpha oscillations by visual stimuli causes cyclic modulation of perception. *J. Neurosci.* 34 (10), 3536–3544. http://dx.doi.org/10.1523/JNEUROSCI.4385-13.2014, URL https://www.jneurosci.org/lookup/doi/10.1523/JNEUROSCI.4385-13.2014.

Thut, G., Schyns, P.G., Gross, J., 2011. Entrainment of perceptually relevant brain oscillations by non-invasive rhythmic stimulation of the human brain. Front. Psychol. 2, http://dx.doi.org/10.3389/fpsyg.2011.00170, URL http://journal.frontiersin.org/article/10.3389/fpsyg.2011.00170/abstract.

Van Diepen, R.M., Miller, L.M., Mazaheri, A., Geng, J.J., 2016. The role of alpha activity in spatial and feature-based attention. *Eneuro* 3 (5), ENEURO.0204–16.2016. http://dx.doi.org/10.1523/ENEURO.0204-16.2016, URL https://www.eneuro.org/lookup/doi/10.1523/ENEURO.0204-16.2016.

Vialatte, F.-B., Maurice, M., Dauwels, J., Cichocki, A., 2010. Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives. Prog. Neurobiol. 90 (4), 418–438. http://dx.doi.org/10.1016/j.pneurobio.2009.11.005, URL https://linkinghub.elsevier.com/retrieve/pii/S0301008209001853.

Wang, F., Kaneshiro, B., Strauber, C.B., Hasak, L., Nguyen, Q.T.H., Yakovleva, A., Vildavski, V.Y., Norcia, A.M., McCandliss, B.D., 2021. Distinct neural sources underlying visual word form processing as revealed by steady state visual evoked potentials (ssvep). Sci. Rep. 11 (1), 18229.
Wiesman, A.I., Groff, B.R., Wilson, T.W., 2019. Frontoparietal networks mediate the behavioral impact of alpha inhibition in visual cortex. Cerebral Cortex 29 (8), 3505–3513.

Wiesman, A.I., Wilson, T.W., 2019. Alpha frequency entrainment reduces the effect of visual distractors. J. Cogn. Neurosci. 31 (9), 1392–1403. http://dx.doi.org/10.1162/jocn_a_01422, URL https://direct.mit.edu/jocn/article/31/9/1392-1403/29051.

Williams, J., 2001. Frequency-specific effects of flicker on recognition memory. Neuroscience 104 (2), 283–286. http://dx.doi.org/10.1016/S0306-4522(00)00579-0, URL https://linkinghub.elsevier.com/retrieve/pii/S0306452200005790.

Worden, M.S., Foxe, J.J., Wang, N., Simpson, G.V., 2000. Anticipatory biasing of visuospatial attention indexed by retinotopically specific α-bank electroencephalography increases over occipital cortex. J. Neurosci. 20 (6), http://dx.doi.org/10.1523/JNEUROSCI.20-06-j0002.2000, RC63–RC63. URL https://www.jneurosci.org/lookup/doi/10.1523/JNEUROSCI.20-06-j0002.2000.