Precise spatial tuning of visually driven alpha oscillations in human visual cortex

Hans Berger first measured electrical oscillations at 10 Hz from the human brain using EEG (Berger, 1929). Shortly thereafter, Adrian and Matthews, 1934 confirmed that when a participant closes their eyes or views a uniform field, the oscillatory power increases. They described the alpha oscillation as ‘show[ing] the negative rather than the positive side of cerebral activity; it shows what happens in an area of cortex which has nothing to do, and it disappears as soon as the area resumes its normal work.’ Consistent with this interpretation, alpha oscillatory power is reduced by arousal (Barry et al., 2007) and alertness (Matousek and Petersén, 1983). The negative association between alpha oscillations and cerebral activity can also be more specific. For example, when attending a location to the left or right of fixation, the occipital alpha power measured by EEG decreases contralaterally and increases ipsilaterally (Kelly et al., 2006; Sauseng et al., 2005; Worden et al., 2000), opposite to the fMRI response (reviewed by Carrasco, 2011; Somers et al., 1999). The reduction in alpha power can also be specific to cortical locations representing narrow angular wedges in the visual field, as measured by both EEG and MEG (Foster et al., 2017; Popov et al., 2019; Rihs et al., 2007; Samaha et al., 2016). The common finding across these studies is that the strength of the alpha rhythm is lower in the part of cortex that is more active.

It is, therefore, surprising that studies examining the spatial tuning of alpha oscillations with invasive methods have tended to find a positive association between alpha oscillations and cortical activity. Specifically, two studies in macaque measured spatial receptive fields using power in the alpha band as a dependent measure, and found that for most recording sites, alpha power increased rather than decreased when a stimulus was in the receptive field (Klink et al., 2021; Takaura et al., 2016). A positive association has also been found in human intracortical studies, reporting power increases in the alpha band when stimuli were in or near electrode receptive fields (Harvey et al., 2013; Luo et al., 2024).

Why do the intracranial studies on spatial tuning find increases in alpha oscillations, whereas the MEG and EEG studies find decreases? One difference is that cortical engagement was stimulus-driven in the intracranial studies and task-driven (spatial attention) in the MEG and EEG studies. We speculate that both visual stimulation and visual attention tend to decrease alpha oscillations, but that visual stimulation masks the alpha decrease with a simultaneous increase in broadband neural responses. Broadband spectral power increases are observed in many intracranial studies in association with increased neural activity (Crone et al., 1998; Hermes et al., 2017; Miller et al., 2009a; Winawer et al., 2013) and likely have a distinct biological cause from oscillations (Hermes et al., 2017; Ray and Maunsell, 2011). Because a visual stimulus can cause a simultaneous increase in broadband power and decrease in alpha oscillatory power, a power change at the alpha frequency is ambiguous. Separating responses by frequency band does not resolve the ambiguity because broadband power changes can extend to low frequencies, including the alpha band (Winawer et al., 2013).

Here, we examined how alpha oscillations are modulated in human visual cortex by visual stimulation using electrocorticographic (ECoG) recordings in nine participants. We addressed two main questions: First, are alpha oscillations increased or decreased by visual stimulation? Second, what is the spatial specificity of the modulation of alpha oscillations? To answer these questions, we used a modeling approach to separate the alpha oscillation from non-oscillatory (broadband) responses. We then used pRF modeling to quantify the spatial tuning of separate components of the ECoG signal, corresponding to alpha oscillations and broadband responses.

By separating the ECoG voltage time course into distinct components, we find that the broadband signal is increased and the alpha oscillation is decreased by visual stimulation. We also find that the broadband pRFs and alpha pRFs have similar locations (center positions) but differ in size. Together, the results suggest that the alpha oscillation in human visual cortex is spatially tuned and negatively modulated by visual input. In the Discussion, we consider how the spatially tuned reduction in the alpha oscillation may contribute to visual function, showing a surprisingly tight link between the spatial profiles of the stimulus-driven alpha oscillation on the one hand, and the behavioral effects of spatial attention on the other hand. Finally, we show how the former may give rise to the latter.

Our main two findings are that visual cortex alpha suppression is specific to stimulus location, as measured by its pRF, and that alpha pRFs are more than twice the size of broadband pRFs. This supports that alpha suppression in and near the locations of the driven response increases cortical excitability. This has implications for visual encoding in cortex, perception, and attention, with several close parallels between the alpha pRF and exogenous (stimulus-driven) attention.

What is the function of the large alpha pRFs?

The alpha oscillation was originally characterized as an ‘idling’ or default cortical state, reflecting the absence of sensory input or task demand (Adrian and Matthews, 1934; Barry et al., 2007; Berger, 1929; Klimesch et al., 1998; Matousek and Petersén, 1983). However, more recent work supports the idea that alpha oscillations act as a gate, actively suppressing neural signals by reducing cortical excitability (Bastos et al., 2020; Jensen and Mazaheri, 2010; Klimesch et al., 2007; Mazaheri and Jensen, 2010; Van Diepen et al., 2019) via ‘pulsed inhibition’ (hyperpolarization followed by rebound). This pulsed inhibition explains why cortical excitability depends on both alpha oscillation magnitude (Kelly et al., 2006; Rihs et al., 2007; Worden et al., 2000) and phase (Osipova et al., 2008; Spaak et al., 2014): when the oscillations are large, there is one phase when cortex is most excitable and one phase when it is least excitable (phase dependence), and when the oscillations are small, cortex is on average more excitable (magnitude dependence). When coupled to the large pRFs, this predicts that the onset of a focal visual stimulus will cause an increase in cortical excitability, spreading beyond the region that responds directly, i.e., with broadband field potentials and spiking (Figure 9). A transient increase in excitability spreading beyond the stimulus is conceptually similar to stimulus-driven (‘exogenous’) spatial attention, in which a visual cue leads to faster response times, increased discriminability, higher perceived contrast (Carrasco, 2011; Downing and Pinker, 1985; Shulman et al., 1986), and increased neural response, as measured by single unit spike rates (Wang et al., 2015a) and BOLD fMRI (Dugué et al., 2020).

Figure 9

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The relationship may be more than conceptual, however, as we find that the spatial profiles of attention, measured behaviorally (Downing and Pinker, 1985; Shulman et al., 1986), and of the alpha pRF are quite similar (Figure 10), with three close parallels. For both measures, the spatial spread (1) increases with eccentricity, (2) is asymmetric, spreading further into the periphery than toward the fovea, and (3) shows center/surround organization, with the attentional effect changing from a benefit to a cost and the alpha response changing from suppression to enhancement. The timing also matches, since exogenous attention has a maximal effect at about 100ms (Nakayama and Mackeben, 1989; Wang et al., 2015a), the duration of an alpha cycle.

Figure 10 with 1 supplement see all

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While many studies have proposed that attention, whether stimulus-driven (exogenous) or internal (endogenous), influences the alpha oscillation (Foster et al., 2017; Kelly et al., 2006; Popov et al., 2019; Rihs et al., 2007; Samaha et al., 2016; Sauseng et al., 2005; Worden et al., 2000), our proposal flips the causality. We propose that the stimulus causes a change in the alpha oscillation and this in turn causes increased neural responsivity and behavioral sensitivity, the hallmarks of covert spatial attention. The spatial spread of the alpha oscillation can be traced in part to the neural mechanisms that generate it in the thalamus (see section 3.2). Top-down attention may capitalize on some of the same mechanisms, as feedback to the LGN can modulate the alpha-initiating neural populations, then resulting in the same effects of cortical excitability in visual cortex that occur from bottom-up spatial processing. This is consistent with findings that endogenous attention is accompanied by local reductions in alpha oscillations (Popov et al., 2019; Sauseng et al., 2005; Worden et al., 2000).

This link to attention contrasts with the proposal that the alpha oscillation in visual cortex reflects surround suppression (Harvey et al., 2013). We think the alpha oscillation is not tightly linked to surround suppression. First, the alpha pRF is negative, and alpha tends to be inhibitory (Bastos et al., 2020; Jensen and Mazaheri, 2010; Klimesch et al., 2007; Mazaheri and Jensen, 2010; Van Diepen et al., 2019), meaning that visual stimuli within the alpha pRF disinhibit cortex, the opposite of surround suppression. An excitatory surround (alpha suppression) and a suppressive surround can coexist if they differ in timing and feature tuning. Surround suppression is relatively fast, disappearing 60ms after surround stimulus offset (Bair et al., 2003), whereas the ~10 Hz alpha oscillation is likely to be modulated at a slower time scale. Furthermore, surround suppression is feature-specific (Bair et al., 2003), whereas the alpha oscillation, being coherent over several mm of cortex, is unlikely to be limited only to cells whose tuning match the stimulus. Nonetheless, surround suppression may be linked to neural oscillations, albeit at a higher frequency range (Hermes et al., 2019; Ray et al., 2013).

Other studies have proposed that retinotopically specific alpha oscillations are related to oculomotor behavior (Popov et al., 2021a; Popov et al., 2021b). Supporting evidence includes the observations that oscillations decrease with saccade initiation (Popov et al., 2021b) and that saccades can be phase-locked to the alpha rhythm (Drewes and VanRullen, 2011; Staudigl et al., 2017). In our study, participants maintained fixation at the center of the screen during receptive field mapping, dissociating modulations in the alpha oscillation from large eye movements. Nonetheless, there are many parallels, and likely shared circuitry, between spatial attention and eye movements (Moore and Fallah, 2001; Shepherd et al., 1986).

An alternative interpretation of our results is that the larger alpha pRFs are an artifact of lower signal-to-noise ratio. We think this is unlikely for three reasons. First, simulations of pRFs in which the ground truth is known show no systematic increase in pRF size with more noise (Lerma-Usabiaga et al., 2020, their Figure 8). Second, adding substantial noise to real fMRI data had little to no effect on pRF size (Le et al., 2017, their Figure 6). Third, the larger size of the alpha pRFs is preserved when regressing size vs variance explained (Figure 5—figure supplement 2).

We focused on the spatial specificity of alpha oscillations on the cortical surface. A separate line of research has also examined the specificity of alpha oscillations in terms of cortical depth and inter-areal communication. One claim is that oscillations in the alpha and beta range play a role in feedback between visual areas, with evidence from nonhuman primates for specificity of alpha and beta oscillations in cortical depth (Bastos et al., 2020; Bollimunta et al., 2011). To our knowledge, no one has examined the spatial specificity of the alpha oscillation across the cortical surface and across cortical depth in the same study, and it remains an open question as to whether the same neural circuits are involved in both types of effects.

The dataset was collected as part of a larger project funded by the NIH (R01MH111417). The methods for collecting and preprocessing the visual ECoG data have been described recently (Groen et al., 2022). For convenience, data collection methods below duplicate some of the text from the methods section of Groen et al., 2022 with occasional modifications to reflect slight differences in participants, electrode selection, and pre-processing.

pRF analysis

Each participant had multiple identical pRF runs, with the same sequence of 224 trials per run, and 2–6 runs per participant. For each electrode and for each of the 224 trials, we computed the geometric mean of the PSD per frequency bin across the repeated runs, to yield a sequence of 224 PSDs per electrode. We extracted a summary measure of alpha power and broadband power from each of the PSDs, so that for each electrode, we obtained a series of 224 alpha values and 224 broadband values.

Estimating alpha power from the PSD

We used a model-based approach to compute the alpha responses. This is important because the broadband response extends to low frequencies, and if one just measured the power in a pre-defined range of frequencies corresponding to the alpha band, it would reflect a sum of the two signals rather than the alpha response alone.

To decompose the spectral power into a broadband shift and a change in the alpha oscillation, we modeled the spectral power changes in the 3–26 Hz range as the sum of two responses in the log power /log frequency domain: a linear shift to capture the broadband response and a Gaussian to capture the alpha response (Figure 11), according to the formula based on Hermes et al., 2015 decomposition of broadband and gamma oscillations:

(1) ${\displaystyle {\displaystyle \begin{array}{rl}Stimulusevokedchangeinpower& =Broadbandelevation+Alphasuppression\\ \mathrm{l}\mathrm{o}{\mathrm{g}}_{10}(\frac{P_s(k)}{P_B(k)})& =({\beta }_{broadband\mathrm{\_}low}-n(k-\mu ))+{\beta }_{alpha}G(k|\mu ,\alpha ),\\ k& =\mathrm{l}\mathrm{o}{\mathrm{g}}_{10}\left(frequency\right),\\ G\left(k|\mu ,\sigma \right)& =e^{\frac{-{\left(k-\mu \right)}^2}{2{\sigma }^2}},\end{array}}}$

with ${\displaystyle 8Hz<{10}^{\mu }<13Hz}$.

$P\left(k\right)$ is the power spectral density (PSD), either for the stimulus ($P_S$) or the blank ($P_B$). ${10}^{\mu }$ indicates the peak alpha frequency. We define the change in power as the logarithm of the ratio, so that, for example, a doubling or halving in power are treated symmetrically. Typically, the response to a stimulus is an increase in broadband power and a decrease in the alpha oscillation, so ${\beta }_{broadband\_low}$ is usually positive and ${\beta }_{alpha}$ is usually negative. We refer to the broadband component as ${\beta }_{broadband\_low}$ to distinguish it from the broadband power we estimate at higher frequencies (70–180 Hz). While the two responses might result from a common biological cause, as proposed by Miller et al., 2009a, we treat the power elevation at lower and higher frequencies separately here. This ensures that the pRFs estimated from the broadband response (70–180 Hz) and from the alpha responses are independent measures, and that any relationship between them is not an artifact of the decomposition method. Confining the broadband signal to higher frequencies to estimate the broadband pRFs also facilitates comparison with other reports, since most groups exclude low frequencies from their estimates of ECoG broadband data. Hence the term ${\beta }_{broadband\_low}$ is defined here only to help estimate the alpha response, and is not used directly for fitting pRF models in our main analysis comparing alpha and broadband pRFs.

Figure 11

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For two participants, there was a prominent spectral peak just above the alpha band (~24 Hz, beta band), which interfered with this spectral decomposition. In this case, we added a third component to the model, identical to the term for alpha, except with a center constrained to 15–30 Hz instead of 8–13 Hz, and applied the model in the 3–32 Hz range:

(2) ${\displaystyle {\displaystyle \begin{array}{c}\mathrm{l}\mathrm{o}{\mathrm{g}}_{10}(\frac{P_S(k)}{P_B(k)})=({\beta }_{broadband\mathrm{\_}low}-n(k-\mu ))+{\beta }_{beta}G\left(k|{\mu }_2,{\sigma }_2\right),\\ k=\mathrm{l}\mathrm{o}{\mathrm{g}}_{10}(frequency),\\ G\left(k|{\mu }_1,{\sigma }_1\right)=e^{\frac{-{\left(k-{\mu }_1\right)}^2}{2{\sigma }_1^2}},G\left(k|{\mu }_2,{\sigma }_2\right)=e^{\frac{-{\left(k-{\mu }_2\right)}^2}{2{\sigma }_2^2}},\end{array}}}$

with ${\displaystyle 8Hz<{10}^{{\mu }_1}<13Hz}$ and ${\displaystyle 15Hz<{10}^{{\mu }_2}<30Hz}$.

The amplitude of this beta oscillation was modeled as a nuisance variable in that it helped improve our estimate of the alpha oscillation but was not used further in analysis. Including this additional term for the other participants had negligible effect, since there were not clear beta oscillations in other participants, so that the coefficient was usually close to 0. For simplicity, we omitted the beta terms for all datasets except for the two participants who required it. This spectral decomposition was computed for each aperture step in the pRF experiments with the constraint that the peak alpha frequency, ${10}^{\mu }$, was within ±1 Hz of the peak estimated from averaging across the responses to all apertures for each electrode.

Estimating broadband power from the PSD

Separately, we estimated the broadband power elevation in the higher frequency range (70–180 Hz) for each electrode and each stimulus location. The broadband power elevation was defined as the ratio of stimulus power (${\displaystyle P_S}$) to blank power (${\displaystyle P_B}$), where power was computed as the geometric mean across frequencies from 70 to 180 Hz in 1 Hz bins, according to the formula:

(3) ${\displaystyle {\displaystyle Broadbandelevation={\beta }_{broadband}=\frac{geometricmean\left(P_S\left(f\right)\right)}{geometricmean\left(P_B\left(f\right)\right)},}}$

with ${\displaystyle 70Hz<f<180Hz}$ (excluding harmonics of line noise).

The harmonics of power line components were excluded from the averaging: 116–125 and 176–180 Hz from NYU patients and 96–105 and 146–155 Hz from UMCU patients.

Time courses from pRF analyses

The computations above yield one estimate of alpha power and one estimate of broadband power per trial per electrode, meaning two time series with 224 samples. Visual inspection showed that these two metrics both tended to rise and fall relatively slowly relative to the small changes in position of the sweeping bar aperture. Therefore, the rapid fluctuations in the summary time series were mostly noise. To increase signal-to-noise ratio, each time course was decimated using a low-pass Chebyshev Type I infinite impulse response (IIR) filter of order 3, resulting in 75 time points (see ecog_prf_fitalpha.m and ecog_prf_constructTimeSeries.m). The same decimation procedure was applied to the binary contrast aperture time series.

Fitting pRF models

The alpha and broadband power time course and the sequential stimulus contrast apertures were used to estimate two pRFs per electrode (one for alpha, one for broadband), using a Difference of Gaussians (DoG) model (Zuiderbaan et al., 2012). The DoG model predicts the response amplitude as the dot product of the binarized stimulus aperture and a pRF. For broadband, the pRF was assumed to be a positive 2D Gaussian (circular) summed with a negative surround. For alpha, the pRF was assumed to be negative with a positive surround. Early analyses indicated a need for the surround, but a lack of precision in estimating its size. For simplicity and to avoid overfitting, we assumed the surround to extend infinitely. Hence, the pRF is a Gaussian with an offset. For each model (alpha and broadband) for each electrode, five parameters were fit: center location of the pRF (${\displaystyle x,y}$), standard deviation of the center Gaussian (${\displaystyle \sigma }$), and the gains of the center (${\displaystyle g_1}$) and the surround ($g_2$). We estimated these pRF parameters using the analyzePRF toolbox (Kay et al., 2013a). We modified the toolbox to incorporate the surround (see analyzePRFdog.m in ECoG_utils). The predicted response is obtained by the dot product of aperture and pRF:

(4) ${\displaystyle {\displaystyle \begin{array}{rcl}RESP& =& STIM\cdot PRF\\ & =& STIM\cdot \left(g_1\cdot G_1\left(x,y,\sigma \right)-g_2\right).\end{array}}}$

We optimized the parameters (${\displaystyle x,y,\sigma ,g_1,\mathrm{a}\mathrm{n}\mathrm{d}{g}_2}$) across all stimulus locations by maximizing the coefficient of determination (${\displaystyle R^2}$) between the 75 predicted time points and observed time series using a least squares non-linear search solver (lsqcurvefit), employing the Levenberg-Marquardt algorithm:

(5) ${\displaystyle {\displaystyle R^2=1-\frac{\sum _{stimuli}{\left(RES{P}_{MODEL}-RES{P}_{DATA}\right)}^2}{\sum _{stimuli}{RES{P}_{DATA}}^2}.}}$

We set the boundary on the center of Gaussian (${\displaystyle x,y}$) as [–16.6° and 16.6°], two times the maximal extent of the visual stimulus. We performed the fitting twice, once to compute pRF parameters and once to compute model accuracy separately. We use the results of the fit to the complete dataset for each electrode to report pRF parameters. To estimate model accuracy, we fit the data using twofold cross-validation. For cross-validation, each time course was separated into first and second half time courses. Both halves included all horizontal and vertical stimulus positions but reversed in temporal order. We estimated the pRF parameters from each half-time course (37 or 38 time points), and used these parameters to predict the response to the other half of the time course. Then we obtained the cross-validated coefficient of determination as the model accuracy of the concatenated predictions ($R^2$):

(6) ${\displaystyle {\displaystyle {cross\text{-}validatedR}^2=1-\frac{\sum {\left(RES{P}_{cross\text{-}validatedMODEL}-RES{P}_{DATA}\right)}^2}{\sum {RES{P}_{DATA}}^2}.}}$

To summarize the data from each electrode, we took the cross-validated variance explained from the split half analysis, and the parameter estimates from the full fit, converting the $x$ and $y$ parameters to polar angle and eccentricity (see ecog_prf_analyzePRF.m).

Comparison of alpha and broadband pRFs

We compared the pRF parameters for the alpha model and broadband models for a subset of electrodes which showed reasonably good fits for each of the two measures. To be included in the comparison, electrodes needed to have a pRF center within the maximal stimulus extent (8.3°) for both broadband and alpha and to have a goodness of fit exceeding thresholds for both broadband and alpha.

The goodness of fit thresholds were defined based on null distributions. The null distributions were computed separately for broadband and alpha. In each case, we shuffled the assignment of pRF parameters to electrodes, computing how accurately the pRF model from one electrode explained the time series from a different electrode. We did this 5000 times to compute a distribution, and chose a variance explained threshold that was higher than 95% of the values in this null distribution. That value was 31% for broadband and 22% for alpha (Figure 4—figure supplement 1). Electrodes whose cross-validated variance explained without shuffling exceeded these values were included for the comparison between the two types of signals (assuming their pRF centers were within the 8.3° stimulus aperture).

This procedure resulted in 54 electrodes from four patients. Of these, 35 electrodes had non-zero probability assignments to V1, V2, or V3, 45 to dorsolateral visual areas (TO, LO, V3AB, or IPS), and three to ventral visual areas (hV4, VO, or PHC). The total is greater than 54 because of the probabilistic assignment of electrodes to areas (See Electrode localization for the probabilistic assignment). See Table 2 for details.

Table 2

	Visual area	# of electrodes with probability >0	Contributing patients (# of electrodes)
Electrodes in visual areas (Figure 3—figure supplement 1, Figure 4—figure supplement 1)	V1–V3	104	1 (1), 2 (15), 3 (6), 4 (13), 5 (6), 8 (54), 9 (9)
	Dorsolateral	303	1 (7), 2 (7), 3 (18), 4 (19), 5 (18), 6 (6), 7 (8), 8 (130), 9 (90)
	V1–V3 or dorsolateral	329	1 (7), 2 (19), 3 (21), 4 (28), 5 (20), 6 (6), 7 (8), 8 (130), 9 (90)
Electrodes with broadband pRFs (Figures 4 and 7, Figure 4—figure supplement 2, Figure 7—figure supplement 1)	V1–V3	55	1 (1), 2 (9), 5 (6), 8 (32), 9 (7)
	Dorsolateral	121	1 (1), 2 (2), 3 (1), 4 (1), 5 (4), 7 (1), 8 (63), 9 (48)
	V1–V3 or dorsolateral	131	1 (1), 2 (10), 3 (1), 4 (1), 5 (6), 7 (1), 8 (63), 9 (48)
Electrodes with broadband and alpha pRFs (Figures 3, 5 and 6, Figure 1—figure supplement 2, Figure 5—figure supplements 1 and 2, Figure 6—figure supplements 1 and 2)	V1–V3	35	2 (6), 5 (5), 8 (20), 9 (4)
	Dorsolateral	45	5 (3), 8 (25), 9 (17)
	V1–V3 or dorsolateral	53	2 (6), 5 (5), 8 (25), 9 (17)

To summarize the relationship between broadband and alpha pRF parameters separately for low (V1–V3) vs. high (dorsolateral) visual areas, we used a resampling procedure that accounts for uncertainty of the assignment between electrodes and visual area. We sampled from the 54 electrodes 54 times with replacement, and randomly assigned each electrode to a visual area according to the Wang full probability distributions. After the resampling, on average, 17.7 electrodes were assigned to V1–V3, 23.2 to dorsolateral, and 12.1 to neither. The reason for the ‘neither’ assignments is that the total probability (100%) of each electrode is the sum of probability of V1-V3, probability of dorsolateral, and probability of elsewhere in the brain. We repeated this procedure 5000 times. We then plotted pRF parameters from the broadband and alpha models as 2D histograms (e.g. Figure 5, Figure 5—figure supplement 1, and Figure 6—figure supplement 1). For the 2D histograms, we computed and plotted 68% confidence intervals derived from the covariance matrix using the function error_ellipse.m (https://www.mathworks.com/matlabcentral/fileexchange/4705-error_ellipse).

Graphical and statistical support for primary claims

Here, we note which analyses support each major claim in the paper and, where appropriate, how we dealt with nesting (the grouping of electrodes to individual participant).

1. Claim: Alpha responses are accurately predicted by a pRF model (Section 2.3).

   • Support:

     • Figure 4 (left side) shows that for V1 to V3, there is greater variance explained in the alpha responses by the pRF model for visually responsive than for visually non-responsive electrodes (where ‘visually responsive’ is defined by variance explained by the broadband pRF fits). The difference in variance explained (visually responsive vs non-responsive) is many times larger than the variability, making formal null hypothesis testing superfluous.

     • Figure 4—figure supplement 2 (left) shows the same pattern for dorsolateral maps.

   • Nesting:

     • Figure 4 (right side) shows that for V1–V3, the pattern observed when pooling across all electrodes is not due to the influence of a single participant’s electrodes; the same pattern is observed in individual participants.

     • Figure 4—figure supplement 2 (right) shows that for dorsolateral electrodes, the pattern for non-visually responsive electrodes is the same in all patients: little to no variance explained by the pRF model. There were only two patients with large numbers of visually responsive electrodes in dorsolateral maps, and in each of these patients the pattern was the same (high variance explained by the alpha pRF model).

2. Claim: Alpha pRFs are larger than broadband pRFs (Section 2.4).

   • Support:

     • Figure 5a shows the alpha and broadband pRFs for each electrode in V1 to V3 (for electrodes whose probabilistic location is higher for V1 to V3 than for other maps). The alpha pRF is larger in nearly every case (15 out of 17). If we include all electrodes that have non-zero probability of localization in V1 to V3, the pattern is the same (33 out of 35 electrodes have larger size for alpha than for broadband).

     • Figure 5—figure supplement 1a shows the same pattern for dorsolateral electrodes (electrodes whose maximum probability is in one of the dorsolateral maps): 34 of 36 electrodes have larger size for alpha than broadband pRFs. If we count all electrodes that have none-zero probability of localization in dorsolateral maps, the numbers are 41 of 45 electrodes.

     • Figure 5d (V1 to V3) and Figure 5—figure supplement 1d (dorsolateral) plot pRF size vs eccentricity separately for broadband and alpha pRFs. Both of these plots show non-overlapping 68% confidence intervals for broadband vs alpha.

     • Figure 5c (V1 to V3) and Figure 5—figure supplement 1c (dorsolateral) plot 2D histograms of pRF parameters, alpha vs broadband. The rightmost panel in both plots compares pRF size for alpha vs broadband. Nearly all the values are above the identity line, meaning larger size for alpha pRFs.

   • Nesting:

     • In Figure 5a, Figure 5—figure supplement 1a, showing alpha and broadband pRF solutions for every individual electrode, we grouped and color-coded electrodes by individual patient. The larger size for the alpha pRFs is evident in each individual patient.

3. Claim: The alpha and broadband pRFs have similar locations (Section 2.4).

   • Support:

     • Figure 5a shows the pRF solutions for broadband and alpha in each electrode (n=17). The broadband pRFs have similar centers and are nearly always inside the alpha pRFs. We report in the text that on average, 92.3% of the broadband pRFs (up to 1-SD) are inside the alpha pRFs (up to 1-SD). To show that this is not a trivial consequence of the alpha pRFs being larger, we recomputed this value after shuffling the electrodes, and the number decreases to 30.0%. We do not think a null hypothesis statistical test is needed to show that these percentages are different.

     • The same visualization (Figure 5—figure supplement 1a) shows that in dorsolateral maps, the broadband pRFs are inside the alpha pRFs. The calculation shows that the containment percentage is 98.0%, decreasing to 25.7% with a shuffle analysis, again a large effect well above the noise.

     • The similarity in pRF polar angle is shown in a 2D histogram in Figure 5c (left panel) for V1 to V3. Nearly all the data are concentrated close to the identity line.

     • The same pattern is shown for the dorsolateral maps in Figure 5—figure supplement 1c.

   • Nesting:

     • The similarity in pRF location between broadband and alpha is observed across individual patients, shown in Figure 5a, Figure 5—figure supplement 1a (grouped and color coded by patient).

4. Claim: The difference in pRF size is not due to a difference in temporal frequency between broadband and alpha. (Section 2.5)

   • Support:

     • Figure 6 shows the distributions of broadband pRF sizes, separated into high frequency broadband (70–180 Hz) and low-frequency broadband (3–26 Hz), compared to alpha pRF sizes. The alpha pRF size is more than double those of low- and high-frequency broadband, whereas the low and high frequency broadband pRF sizes are the same as each other.

     • Figure 6—figure supplement 2b shows 2D histograms of pRF parameters in V1 to V3, comparing low- and high-frequency broadband. The angle, eccentricity, and size histograms all show the majority of the data lying on or near the identity line, confirming that low- and high-frequency broadband pRFs are similar, including the size. This differs from the comparison between high frequency broadband and alpha, in which the pRF sizes differ (see Claim 2 above).

   • Nesting:

     • Figure 6—figure supplement 2a shows that pRF sizes for high- and low-frequency broadband for each individual electrode. Electrodes are grouped and color-coded by individual patient. Each patient shows a similar size for low- and high-frequency broadband pRFs.

5. Claim: The accuracy of the alpha pRFs depends on the model-based calculation. (Section 2.6)

   • Support:

     • Figure 7 compares pRF solutions with and without the model-based baseline correction.

       • Left panel: The figure shows a higher cross-validated variance explained for the model-based method (43% vs 10%, median).

       • Middle panel: The model-based method also shows a consistently negative gain (29 out of 31 electrodes), whereas without correction there is a mixture of positive and negative gain (14 negative, 17 positive), inconsistent with 100 y of measurements of the alpha oscillation.

       • Right panel: The estimated pRF size correlates positively with eccentricity when doing the baseline correction, but not without the correction.

   • Nesting:

     • All three patterns described above are evident in individual patents (Figure 7—figure supplement 1).

6. Claim: Alpha oscillations are coherent across a larger spatial extent than broadband signals.

   • Support:

     • Figure 8 shows the significant differences by bootstrapping between broadband and alpha coherence.

   • Nesting:

     • The results are not averaged but are provided for individual patients.

The data are shared in BIDS format via Open Neuro (https://doi.org/10.18112/openneuro.ds004194.v3.0.0). The analysis depends on two repositories with MATLAB code: one for conversion to BIDS and pre-processing (ECoG_utils, https://github.com/WinawerLab/ECoG_utils copy archived at Yuasa, 2025a), and one for analysis of the pre-processed data (ECoG_alphaPRF, https://github.com/KenYMB/ECoG_alphaPRF copy archived at Yuasa, 2025b).

1. 1. Groen IIA
   2. Yuasa K
   3. Brands AM
   4. Piantoni G
   5. Montenegro S
   6. Flinker A
   7. Devore S
   8. Devinsky O
   9. Doyle W
   10. Dugan P
   11. Friedman D
   12. Ramsey N
   13. Petridou N
   14. Winawer J

   (2025) OpenNeuro

   Visual ECoG dataset.

   https://doi.org/10.18112/openneuro.ds004194.v3.0.0

1. 1. Adrian ED
   2. Matthews BHC

   (1934) The berger rhythm: potential changes from the occipital lobes in man

   Brain 57:355–385.

   https://doi.org/10.1093/brain/57.4.355

   • Google Scholar
2. 1. Bair W
   2. Cavanaugh JR
   3. Movshon JA

   (2003) Time course and time-distance relationships for surround suppression in macaque V1 neurons

   The Journal of Neuroscience 23:7690–7701.

   https://doi.org/10.1523/JNEUROSCI.23-20-07690.2003

   • PubMed
   • Google Scholar
3. Software

   1. BAIRR01

   (2022) Vistadisp, version 2d21f3f

   GitHub.

   https://github.com/BAIRR01/vistadisp
4. 1. Barry RJ
   2. Clarke AR
   3. Johnstone SJ
   4. Magee CA
   5. Rushby JA

   (2007) EEG differences between eyes-closed and eyes-open resting conditions

   Clinical Neurophysiology 118:2765–2773.

   https://doi.org/10.1016/j.clinph.2007.07.028

   • PubMed
   • Google Scholar
5. 1. Bartoli E
   2. Bosking W
   3. Chen Y
   4. Li Y
   5. Sheth SA
   6. Beauchamp MS
   7. Yoshor D
   8. Foster BL

   (2019) Functionally distinct gamma range activity revealed by stimulus tuning in human visual cortex

   Current Biology 29:3345–3358.

   https://doi.org/10.1016/j.cub.2019.08.004

   • PubMed
   • Google Scholar
6. 1. Bastos AM
   2. Lundqvist M
   3. Waite AS
   4. Kopell N
   5. Miller EK

   (2020) Layer and rhythm specificity for predictive routing

   PNAS 117:31459–31469.

   https://doi.org/10.1073/pnas.2014868117

   • PubMed
   • Google Scholar
7. 1. Bédard C
   2. Kröger H
   3. Destexhe A

   (2006) Does the 1/f frequency scaling of brain signals reflect self-organized critical states?

   Physical Review Letters 97:118102.

   https://doi.org/10.1103/PhysRevLett.97.118102

   • PubMed
   • Google Scholar
8. 1. Berger H

   (1929) Über das Elektrenkephalogramm des Menschen

   Archiv Für Psychiatrie Und Nervenkrankheiten 87:527–570.

   https://doi.org/10.1007/BF01797193

   • Google Scholar
9. 1. Bollimunta A
   2. Mo J
   3. Schroeder CE
   4. Ding M

   (2011) Neuronal mechanisms and attentional modulation of corticothalamic α oscillations

   The Journal of Neuroscience 31:4935–4943.

   https://doi.org/10.1523/JNEUROSCI.5580-10.2011

   • PubMed
   • Google Scholar
10. 1. Brovelli A
    2. Lachaux JP
    3. Kahane P
    4. Boussaoud D

    (2005) High gamma frequency oscillatory activity dissociates attention from intention in the human premotor cortex

    NeuroImage 28:154–164.

    https://doi.org/10.1016/j.neuroimage.2005.05.045

    • PubMed
    • Google Scholar
11. 1. Busch NA
    2. Dubois J
    3. VanRullen R

    (2009) The phase of ongoing EEG oscillations predicts visual perception

    The Journal of Neuroscience 29:7869–7876.

    https://doi.org/10.1523/JNEUROSCI.0113-09.2009

    • PubMed
    • Google Scholar
12. 1. Canolty RT
    2. Edwards E
    3. Dalal SS
    4. Soltani M
    5. Nagarajan SS
    6. Kirsch HE
    7. Berger MS
    8. Barbaro NM
    9. Knight RT

    (2006) High gamma power is phase-locked to theta oscillations in human neocortex

    Science 313:1626–1628.

    https://doi.org/10.1126/science.1128115

    • PubMed
    • Google Scholar
13. 1. Carrasco M

    (2011) Visual attention: the past 25 years

    Vision Research 51:1484–1525.

    https://doi.org/10.1016/j.visres.2011.04.012

    • PubMed
    • Google Scholar
14. 1. Clayton MS
    2. Yeung N
    3. Cohen Kadosh R

    (2018) The many characters of visual alpha oscillations

    The European Journal of Neuroscience 48:2498–2508.

    https://doi.org/10.1111/ejn.13747

    • PubMed
    • Google Scholar
15. 1. Crone NE
    2. Miglioretti DL
    3. Gordon B
    4. Lesser RP

    (1998) Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. II. Event-related synchronization in the gamma band

    Brain 121 ( Pt 12):2301–2315.

    https://doi.org/10.1093/brain/121.12.2301

    • PubMed
    • Google Scholar
16. 1. Donoghue T
    2. Haller M
    3. Peterson EJ
    4. Varma P
    5. Sebastian P
    6. Gao R
    7. Noto T
    8. Lara AH
    9. Wallis JD
    10. Knight RT
    11. Shestyuk A
    12. Voytek B

    (2020) Parameterizing neural power spectra into periodic and aperiodic components

    Nature Neuroscience 23:1655–1665.

    https://doi.org/10.1038/s41593-020-00744-x

    • PubMed
    • Google Scholar
17. 1. Downing CJ
    2. Pinker S

    (1985)

    In Mechanisms of Attention: Attention and Performance XI

    171–187, The spatial structure of visual attention, In Mechanisms of Attention: Attention and Performance XI, Taylor & Francis group.

    • Google Scholar
18. 1. Drewes J
    2. VanRullen R

    (2011) This is the rhythm of your eyes: the phase of ongoing electroencephalogram oscillations modulates saccadic reaction time

    The Journal of Neuroscience 31:4698–4708.

    https://doi.org/10.1523/JNEUROSCI.4795-10.2011

    • PubMed
    • Google Scholar
19. 1. Dugué L
    2. Merriam EP
    3. Heeger DJ
    4. Carrasco M

    (2020) Differential impact of endogenous and exogenous attention on activity in human visual cortex

    Scientific Reports 10:21274.

    https://doi.org/10.1038/s41598-020-78172-x

    • PubMed
    • Google Scholar
20. 1. Dumoulin SO
    2. Wandell BA

    (2008) Population receptive field estimates in human visual cortex

    NeuroImage 39:647–660.

    https://doi.org/10.1016/j.neuroimage.2007.09.034

    • PubMed
    • Google Scholar
21. 1. Eckhorn R
    2. Bauer R
    3. Jordan W
    4. Brosch M
    5. Kruse W
    6. Munk M
    7. Reitboeck HJ

    (1988) Coherent oscillations: a mechanism of feature linking in the visual cortex? Multiple electrode and correlation analyses in the cat

    Biological Cybernetics 60:121–130.

    https://doi.org/10.1007/BF00202899

    • PubMed
    • Google Scholar
22. 1. Foster JJ
    2. Sutterer DW
    3. Serences JT
    4. Vogel EK
    5. Awh E

    (2017) Alpha-band oscillations enable spatially and temporally resolved tracking of covert spatial attention

    Psychological Science 28:929–941.

    https://doi.org/10.1177/0956797617699167

    • PubMed
    • Google Scholar
23. 1. Gray CM
    2. Singer W

    (1989) Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex

    PNAS 86:1698–1702.

    https://doi.org/10.1073/pnas.86.5.1698

    • PubMed
    • Google Scholar
24. 1. Groen IIA
    2. Piantoni G
    3. Montenegro S
    4. Flinker A
    5. Devore S
    6. Devinsky O
    7. Doyle W
    8. Dugan P
    9. Friedman D
    10. Ramsey NF
    11. Petridou N
    12. Winawer J

    (2022) Temporal dynamics of neural responses in human visual cortex

    The Journal of Neuroscience 42:7562–7580.

    https://doi.org/10.1523/JNEUROSCI.1812-21.2022

    • PubMed
    • Google Scholar
25. Software

    1. Groen I

    (2023) TemporalECoG, version 64593fb

    GitHub.

    https://github.com/irisgroen/temporalECoG
26. 1. Harvey BM
    2. Vansteensel MJ
    3. Ferrier CH
    4. Petridou N
    5. Zuiderbaan W
    6. Aarnoutse EJ
    7. Bleichner MG
    8. Dijkerman HC
    9. van Zandvoort MJE
    10. Leijten FSS
    11. Ramsey NF
    12. Dumoulin SO

    (2013) Frequency specific spatial interactions in human electrocorticography: V1 alpha oscillations reflect surround suppression

    NeuroImage 65:424–432.

    https://doi.org/10.1016/j.neuroimage.2012.10.020

    • PubMed
    • Google Scholar
27. 1. Hermes D
    2. Miller KJ
    3. Noordmans HJ
    4. Vansteensel MJ
    5. Ramsey NF

    (2010) Automated electrocorticographic electrode localization on individually rendered brain surfaces

    Journal of Neuroscience Methods 185:293–298.

    https://doi.org/10.1016/j.jneumeth.2009.10.005

    • PubMed
    • Google Scholar
28. 1. Hermes D
    2. Miller KJ
    3. Wandell BA
    4. Winawer J

    (2015) Stimulus dependence of gamma oscillations in human visual cortex

    Cerebral Cortex 25:2951–2959.

    https://doi.org/10.1093/cercor/bhu091

    • PubMed
    • Google Scholar
29. 1. Hermes D
    2. Nguyen M
    3. Winawer J

    (2017) Neuronal synchrony and the relation between the blood-oxygen-level dependent response and the local field potential

    PLOS Biology 15:e2001461.

    https://doi.org/10.1371/journal.pbio.2001461

    • PubMed
    • Google Scholar
30. 1. Hermes D
    2. Petridou N
    3. Kay KN
    4. Winawer J

    (2019) An image-computable model for the stimulus selectivity of gamma oscillations

    eLife 8:e47035.

    https://doi.org/10.7554/eLife.47035

    • PubMed
    • Google Scholar
31. 1. Holdgraf C
    2. Appelhoff S
    3. Bickel S
    4. Bouchard K
    5. D’Ambrosio S
    6. David O
    7. Devinsky O
    8. Dichter B
    9. Flinker A
    10. Foster BL
    11. Gorgolewski KJ
    12. Groen I
    13. Groppe D
    14. Gunduz A
    15. Hamilton L
    16. Honey CJ
    17. Jas M
    18. Knight R
    19. Lachaux J-P
    20. Lau JC
    21. Lee-Messer C
    22. Lundstrom BN
    23. Miller KJ
    24. Ojemann JG
    25. Oostenveld R
    26. Petridou N
    27. Piantoni G
    28. Pigorini A
    29. Pouratian N
    30. Ramsey NF
    31. Stolk A
    32. Swann NC
    33. Tadel F
    34. Voytek B
    35. Wandell BA
    36. Winawer J
    37. Whitaker K
    38. Zehl L
    39. Hermes D

    (2019) iEEG-BIDS, extending the brain imaging data structure specification to human intracranial electrophysiology

    Scientific Data 6:102.

    https://doi.org/10.1038/s41597-019-0105-7

    • PubMed
    • Google Scholar
32. 1. Hughes SW
    2. Crunelli V

    (2007) Just a phase they’re going through: the complex interaction of intrinsic high-threshold bursting and gap junctions in the generation of thalamic alpha and theta rhythms

    International Journal of Psychophysiology 64:3–17.

    https://doi.org/10.1016/j.ijpsycho.2006.08.004

    • PubMed
    • Google Scholar
33. 1. Hughes SW
    2. Lőrincz ML
    3. Blethyn K
    4. Kékesi KA
    5. Juhász G
    6. Turmaine M
    7. Parnavelas JG
    8. Crunelli V

    (2011) Thalamic gap junctions control local neuronal synchrony and influence macroscopic oscillation amplitude during EEG alpha rhythms

    Frontiers in Psychology 2:193.

    https://doi.org/10.3389/fpsyg.2011.00193

    • PubMed
    • Google Scholar
34. 1. Jensen O
    2. Mazaheri A

    (2010) Shaping functional architecture by oscillatory alpha activity: gating by inhibition

    Frontiers in Human Neuroscience 4:186.

    https://doi.org/10.3389/fnhum.2010.00186

    • PubMed
    • Google Scholar
35. 1. Kay KN
    2. Winawer J
    3. Mezer A
    4. Wandell BA

    (2013a) Compressive spatial summation in human visual cortex

    Journal of Neurophysiology 110:481–494.

    https://doi.org/10.1152/jn.00105.2013

    • PubMed
    • Google Scholar
36. 1. Kay KN
    2. Winawer J
    3. Rokem A
    4. Mezer A
    5. Wandell BA

    (2013b) A two-stage cascade model of BOLD responses in human visual cortex

    PLOS Computational Biology 9:e1003079.

    https://doi.org/10.1371/journal.pcbi.1003079

    • PubMed
    • Google Scholar
37. Software

    1. Kay K

    (2025) AnalyzePRF, version dca6c75

    GitHub.

    https://github.com/cvnlab/analyzePRF
38. 1. Kelly SP
    2. Lalor EC
    3. Reilly RB
    4. Foxe JJ

    (2006) Increases in alpha oscillatory power reflect an active retinotopic mechanism for distracter suppression during sustained visuospatial attention

    Journal of Neurophysiology 95:3844–3851.

    https://doi.org/10.1152/jn.01234.2005

    • PubMed
    • Google Scholar
39. 1. Klimesch W
    2. Doppelmayr M
    3. Russegger H
    4. Pachinger T
    5. Schwaiger J

    (1998) Induced alpha band power changes in the human EEG and attention

    Neuroscience Letters 244:73–76.

    https://doi.org/10.1016/S0304-3940(98)00122-0

    • Google Scholar
40. 1. Klimesch W
    2. Sauseng P
    3. Hanslmayr S

    (2007) EEG alpha oscillations: the inhibition-timing hypothesis

    Brain Research Reviews 53:63–88.

    https://doi.org/10.1016/j.brainresrev.2006.06.003

    • PubMed
    • Google Scholar
41. 1. Klink PC
    2. Chen X
    3. Vanduffel W
    4. Roelfsema PR

    (2021) Population receptive fields in nonhuman primates from whole-brain fMRI and large-scale neurophysiology in visual cortex

    eLife 10:e67304.

    https://doi.org/10.7554/eLife.67304

    • PubMed
    • Google Scholar
42. 1. Kupers ER
    2. Wang HX
    3. Amano K
    4. Kay KN
    5. Heeger DJ
    6. Winawer J

    (2018) A non-invasive, quantitative study of broadband spectral responses in human visual cortex

    PLOS ONE 13:e0193107.

    https://doi.org/10.1371/journal.pone.0193107

    • PubMed
    • Google Scholar
43. 1. Le R
    2. Witthoft N
    3. Ben-Shachar M
    4. Wandell B

    (2017) The field of view available to the ventral occipito-temporal reading circuitry

    Journal of Vision 17:6.

    https://doi.org/10.1167/17.4.6

    • PubMed
    • Google Scholar
44. 1. Lerma-Usabiaga G
    2. Benson N
    3. Winawer J
    4. Wandell BA

    (2020) A validation framework for neuroimaging software: the case of population receptive fields

    PLOPS Computational Biology 16:e1007924.

    https://doi.org/10.1371/journal.pcbi.1007924

    • PubMed
    • Google Scholar
45. 1. Lorincz ML
    2. Kékesi KA
    3. Juhász G
    4. Crunelli V
    5. Hughes SW

    (2009) Temporal framing of thalamic relay-mode firing by phasic inhibition during the alpha rhythm

    Neuron 63:683–696.

    https://doi.org/10.1016/j.neuron.2009.08.012

    • PubMed
    • Google Scholar
46. 1. Luo L
    2. Wang X
    3. Lu J
    4. Chen G
    5. Luan G
    6. Li W
    7. Wang Q
    8. Fang F

    (2024) Local field potentials, spiking activity, and receptive fields in human visual cortex

    Science China. Life Sciences 67:543–554.

    https://doi.org/10.1007/s11427-023-2436-x

    • PubMed
    • Google Scholar
47. 1. Makeig S

    (1993) Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones

    Electroencephalography and Clinical Neurophysiology 86:283–293.

    https://doi.org/10.1016/0013-4694(93)90110-h

    • PubMed
    • Google Scholar
48. 1. Manning JR
    2. Jacobs J
    3. Fried I
    4. Kahana MJ

    (2009) Broadband shifts in local field potential power spectra are correlated with single-neuron spiking in humans

    The Journal of Neuroscience 29:13613–13620.

    https://doi.org/10.1523/JNEUROSCI.2041-09.2009

    • PubMed
    • Google Scholar
49. 1. Mathewson KE
    2. Gratton G
    3. Fabiani M
    4. Beck DM
    5. Ro T

    (2009) To see or not to see: prestimulus alpha phase predicts visual awareness

    The Journal of Neuroscience 29:2725–2732.

    https://doi.org/10.1523/JNEUROSCI.3963-08.2009

    • PubMed
    • Google Scholar
50. 1. Matousek M
    2. Petersén I

    (1983) A method for assessing alertness fluctuations from EEG spectra

    Electroencephalography and Clinical Neurophysiology 55:108–113.

    https://doi.org/10.1016/0013-4694(83)90154-2

    • PubMed
    • Google Scholar
51. 1. Mazaheri A
    2. Jensen O

    (2010) Rhythmic pulsing: linking ongoing brain activity with evoked responses

    Frontiers in Human Neuroscience 4:177.

    https://doi.org/10.3389/fnhum.2010.00177

    • PubMed
    • Google Scholar
52. 1. Miller KJ
    2. Sorensen LB
    3. Ojemann JG
    4. den Nijs M

    (2009a) Power-law scaling in the brain surface electric potential

    PLOS Computational Biology 5:e1000609.

    https://doi.org/10.1371/journal.pcbi.1000609

    • PubMed
    • Google Scholar
53. 1. Miller KJ
    2. Zanos S
    3. Fetz EE
    4. den Nijs M
    5. Ojemann JG

    (2009b) Decoupling the cortical power spectrum reveals real-time representation of individual finger movements in humans

    The Journal of Neuroscience 29:3132–3137.

    https://doi.org/10.1523/JNEUROSCI.5506-08.2009

    • PubMed
    • Google Scholar
54. 1. Miller KJ
    2. Honey CJ
    3. Hermes D
    4. Rao RPN
    5. denNijs M
    6. Ojemann JG

    (2014) Broadband changes in the cortical surface potential track activation of functionally diverse neuronal populations

    NeuroImage 85 Pt 2:711–720.

    https://doi.org/10.1016/j.neuroimage.2013.08.070

    • PubMed
    • Google Scholar
55. 1. Moore T
    2. Fallah M

    (2001) Control of eye movements and spatial attention

    PNAS 98:1273–1276.

    https://doi.org/10.1073/pnas.021549498

    • Google Scholar
56. 1. Nakayama K
    2. Mackeben M

    (1989) Sustained and transient components of focal visual attention

    Vision Research 29:1631–1647.

    https://doi.org/10.1016/0042-6989(89)90144-2

    • PubMed
    • Google Scholar
57. 1. Newsome WT
    2. Maunsell JH
    3. Van Essen DC

    (1986) Ventral posterior visual area of the macaque: visual topography and areal boundaries

    The Journal of Comparative Neurology 252:139–153.

    https://doi.org/10.1002/cne.902520202

    • PubMed
    • Google Scholar
58. 1. Oostenveld R
    2. Fries P
    3. Maris E
    4. Schoffelen JM

    (2011) FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

    Computational Intelligence and Neuroscience 2011:156869.

    https://doi.org/10.1155/2011/156869

    • PubMed
    • Google Scholar
59. Software

    1. Oostenveld R
    2. Schoffelen JM

    (2025) Fieldtrip, version 6a80ed0

    GitHub.

    https://github.com/fieldtrip/fieldtrip
60. 1. Osipova D
    2. Hermes D
    3. Jensen O

    (2008) Gamma power is phase-locked to posterior alpha activity

    PLOS ONE 3:e3990.

    https://doi.org/10.1371/journal.pone.0003990

    • PubMed
    • Google Scholar
61. 1. Palva S
    2. Palva JM

    (2011) Functional roles of alpha-band phase synchronization in local and large-scale cortical networks

    Frontiers in Psychology 2:204.

    https://doi.org/10.3389/fpsyg.2011.00204

    • PubMed
    • Google Scholar
62. 1. Palva S
    2. Palva JM

    (2012) Discovering oscillatory interaction networks with M/EEG: challenges and breakthroughs

    Trends in Cognitive Sciences 16:219–230.

    https://doi.org/10.1016/j.tics.2012.02.004

    • PubMed
    • Google Scholar
63. 1. Popov T
    2. Gips B
    3. Kastner S
    4. Jensen O

    (2019) Spatial specificity of alpha oscillations in the human visual system

    Human Brain Mapping 40:4432–4440.

    https://doi.org/10.1002/hbm.24712

    • PubMed
    • Google Scholar
64. Preprint

    1. Popov T
    2. Langer N
    3. Gips B
    4. Weisz N
    5. Jensen O

    (2021a) Sound-Location Specific Alpha Power Modulation in the Visual Cortex in Absence of Visual Input

    bioRxiv.

    https://doi.org/10.1101/2021.03.15.435371

    • Google Scholar
65. Preprint

    1. Popov T
    2. Miller GA
    3. Rockstroh B
    4. Jensen O
    5. Langer N

    (2021b) Alpha oscillations link action to cognition: an oculomotor account of the brain’s dominant rhythm

    bioRxiv.

    https://doi.org/10.1101/2021.09.24.461634

    • Google Scholar
66. 1. Ray S
    2. Maunsell JHR

    (2011) Different origins of gamma rhythm and high-gamma activity in macaque visual cortex

    PLOS Biology 9:e1000610.

    https://doi.org/10.1371/journal.pbio.1000610

    • PubMed
    • Google Scholar
67. 1. Ray S
    2. Ni AM
    3. Maunsell JHR

    (2013) Strength of gamma rhythm depends on normalization

    PLOS Biology 11:e1001477.

    https://doi.org/10.1371/journal.pbio.1001477

    • PubMed
    • Google Scholar
68. 1. Rihs TA
    2. Michel CM
    3. Thut G

    (2007) Mechanisms of selective inhibition in visual spatial attention are indexed by alpha-band EEG synchronization

    The European Journal of Neuroscience 25:603–610.

    https://doi.org/10.1111/j.1460-9568.2007.05278.x

    • PubMed
    • Google Scholar
69. 1. Samaha J
    2. Sprague TC
    3. Postle BR

    (2016) Decoding and reconstructing the focus of spatial attention from the topography of alpha-band oscillations

    Journal of Cognitive Neuroscience 28:1090–1097.

    https://doi.org/10.1162/jocn_a_00955

    • PubMed
    • Google Scholar
70. 1. Sauseng P
    2. Klimesch W
    3. Stadler W
    4. Schabus M
    5. Doppelmayr M
    6. Hanslmayr S
    7. Gruber WR
    8. Birbaumer N

    (2005) A shift of visual spatial attention is selectively associated with human EEG alpha activity

    The European Journal of Neuroscience 22:2917–2926.

    https://doi.org/10.1111/j.1460-9568.2005.04482.x

    • PubMed
    • Google Scholar
71. 1. Shepherd M
    2. Findlay JM
    3. Hockey RJ

    (1986) The relationship between eye movements and spatial attention

    The Quarterly Journal of Experimental Psychology. A, Human Experimental Psychology 38:475–491.

    https://doi.org/10.1080/14640748608401609

    • PubMed
    • Google Scholar
72. 1. Shulman GL
    2. Sheehy JB
    3. Wilson J

    (1986) Gradients of spatial attention

    Acta Psychologica 61:167–181.

    https://doi.org/10.1016/0001-6918(86)90029-6

    • PubMed
    • Google Scholar
73. 1. Somers DC
    2. Dale AM
    3. Seiffert AE
    4. Tootell RB

    (1999) Functional MRI reveals spatially specific attentional modulation in human primary visual cortex

    PNAS 96:1663–1668.

    https://doi.org/10.1073/pnas.96.4.1663

    • PubMed
    • Google Scholar
74. 1. Spaak E
    2. de Lange FP
    3. Jensen O

    (2014) Local entrainment of α oscillations by visual stimuli causes cyclic modulation of perception

    The Journal of Neuroscience 34:3536–3544.

    https://doi.org/10.1523/JNEUROSCI.4385-13.2014

    • PubMed
    • Google Scholar
75. 1. Staudigl T
    2. Hartl E
    3. Noachtar S
    4. Doeller CF
    5. Jensen O

    (2017) Saccades are phase-locked to alpha oscillations in the occipital and medial temporal lobe during successful memory encoding

    PLOS Biology 15:e2003404.

    https://doi.org/10.1371/journal.pbio.2003404

    • PubMed
    • Google Scholar
76. 1. Takaura K
    2. Tsuchiya N
    3. Fujii N

    (2016) Frequency-dependent spatiotemporal profiles of visual responses recorded with subdural ECoG electrodes in awake monkeys: differences between high- and low-frequency activity

    NeuroImage 124:557–572.

    https://doi.org/10.1016/j.neuroimage.2015.09.007

    • PubMed
    • Google Scholar
77. 1. Van Diepen RM
    2. Foxe JJ
    3. Mazaheri A

    (2019) The functional role of alpha-band activity in attentional processing: the current zeitgeist and future outlook

    Current Opinion in Psychology 29:229–238.

    https://doi.org/10.1016/j.copsyc.2019.03.015

    • PubMed
    • Google Scholar
78. 1. Wang F
    2. Chen M
    3. Yan Y
    4. Zhaoping L
    5. Li W

    (2015a) Modulation of neuronal responses by exogenous attention in macaque primary visual cortex

    The Journal of Neuroscience 35:13419–13429.

    https://doi.org/10.1523/JNEUROSCI.0527-15.2015

    • PubMed
    • Google Scholar
79. 1. Wang L
    2. Mruczek REB
    3. Arcaro MJ
    4. Kastner S

    (2015b) Probabilistic maps of visual topography in human cortex

    Cerebral Cortex 25:3911–3931.

    https://doi.org/10.1093/cercor/bhu277

    • PubMed
    • Google Scholar
80. 1. Welch P

    (1967) The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms

    IEEE Transactions on Audio and Electroacoustics 15:70–73.

    https://doi.org/10.1109/TAU.1967.1161901

    • Google Scholar
81. 1. Winawer J
    2. Kay KN
    3. Foster BL
    4. Rauschecker AM
    5. Parvizi J
    6. Wandell BA

    (2013) Asynchronous broadband signals are the principal source of the BOLD response in human visual cortex

    Current Biology 23:1145–1153.

    https://doi.org/10.1016/j.cub.2013.05.001

    • PubMed
    • Google Scholar
82. Software

    1. Winawer J
    2. Montenegro S

    (2025) BAIR_stimuli, version 5abe350

    GitHub.

    https://github.com/BAIRR01/BAIR_stimuli
83. 1. Worden MS
    2. Foxe JJ
    3. Wang N
    4. Simpson GV

    (2000) Anticipatory biasing of visuospatial attention indexed by retinotopically specific alpha-band electroencephalography increases over occipital cortex

    The Journal of Neuroscience 20:RC63.

    https://doi.org/10.1523/JNEUROSCI.20-06-j0002.2000

    • PubMed
    • Google Scholar
84. 1. Yang AI
    2. Wang X
    3. Doyle WK
    4. Halgren E
    5. Carlson C
    6. Belcher TL
    7. Cash SS
    8. Devinsky O
    9. Thesen T

    (2012) Localization of dense intracranial electrode arrays using magnetic resonance imaging

    NeuroImage 63:157–165.

    https://doi.org/10.1016/j.neuroimage.2012.06.039

    • PubMed
    • Google Scholar
85. Software

    1. Yuasa K

    (2025a) ECoG_utils, version swh:1:rev:72214cd6f77ad31aca7975064011859c35270fed

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:f03bac6f24d1d4f7fb14050845972b66e141180a;origin=https://github.com/WinawerLab/ECoG_utils;visit=swh:1:snp:9b5ada2c526c074548290626422923aab5fb4cb1;anchor=swh:1:rev:72214cd6f77ad31aca7975064011859c35270fed
86. Software

    1. Yuasa K

    (2025b) ECoG_alphaPRF, version swh:1:rev:4321eb4374db782d09f3f43362836f234a4e4f9b

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:733a447c12855556722226588e352e029ec619ad;origin=https://github.com/KenYMB/ECoG_alphaPRF;visit=swh:1:snp:0b1fdf0c47fa69d0d29727bd3454ecdb3800a805;anchor=swh:1:rev:4321eb4374db782d09f3f43362836f234a4e4f9b
87. 1. Zuiderbaan W
    2. Harvey BM
    3. Dumoulin SO

    (2012) Modeling center-surround configurations in population receptive fields using fMRI

    Journal of Vision 12:10.

    https://doi.org/10.1167/12.3.10

    • PubMed
    • Google Scholar

Author details

1. Kenichi Yuasa

   1. Department of Psychology, New York University, New York, United States
   2. Division of Neural Dynamics, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Japan

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   kenyuasa@nips.ac.jp

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5697-7537

2. Iris IA Groen

   1. Department of Psychology, New York University, New York, United States
   2. Video & Image Sense Lab, Informatics Institute, University of Amsterdam, Amsterdam, Netherlands

   Contribution

   Data curation, Software, Investigation, Methodology, Writing – review and editing

   Competing interests

   Reviewing editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0002-5536-6128

3. Giovanni Piantoni

   University Medical Center Utrecht, Utrecht, Netherlands

   Contribution

   Data curation, Software, Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5308-926X

4. Stephanie Montenegro

   New York University School of Medicine, New York, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

5. Adeen Flinker

   New York University School of Medicine, New York, United States

   Contribution

   Resources, Investigation, Project administration

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1247-1283

6. Sasha Devore

   New York University School of Medicine, New York, United States

   Contribution

   Resources, Investigation, Project administration

   Competing interests

   No competing interests declared

7. Orrin Devinsky

   New York University School of Medicine, New York, United States

   Contribution

   Supervision, Funding acquisition, Investigation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0044-4632

8. Werner Doyle

   New York University School of Medicine, New York, United States

   Contribution

   Resources, Investigation, Methodology

   Competing interests

   No competing interests declared

9. Patricia Dugan

   New York University School of Medicine, New York, United States

   Contribution

   Resources, Funding acquisition, Investigation, Project administration

   Competing interests

   No competing interests declared

10. Daniel Friedman

    New York University School of Medicine, New York, United States

    Contribution

    Resources, Funding acquisition, Investigation, Project administration

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1068-1797

11. Nick F Ramsey

    University Medical Center Utrecht, Utrecht, Netherlands

    Contribution

    Resources, Funding acquisition, Investigation, Project administration

    Competing interests

    No competing interests declared

12. Natalia Petridou

    University Medical Center Utrecht, Utrecht, Netherlands

    Contribution

    Resources, Data curation, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0783-0387

13. Jonathan Winawer

    1. Department of Psychology, New York University, New York, United States
    2. Center for Neural Science, New York University, New York, United States

    Contribution

    Conceptualization, Resources, Software, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Project administration, Writing – review and editing

    For correspondence

    jonathan.winawer@nyu.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-7475-5586

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