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Olfactory bulb-cortex oscillations encode perceived odor intensity rather than concentration [1]

['Frans Nordén', 'Department Of Clinical Neuroscience', 'Karolinska Institutet', 'Stockholm', 'Irene Zanettin', 'Mikael Lundqvist', 'Artin Arshamian', 'Johan N. Lundström', 'Department Of Otorhinolaryngology', 'Karolinska University Hospital']

Date: 2026-06 

Perceived stimulus intensity is a core feature of sensory experience, yet how it emerges in the human olfactory system remains unknown. Here, we demonstrate that oscillatory dynamics in the human olfactory bulb (OB) and piriform cortex (PC) primarily encode subjective perceived intensity rather than physical concentration. Using noninvasive electrobulbogram recordings, we show that early gamma-band activity in the OB reflects bottom-up transmission of perceived intensity to the PC, which in turn sends top-down beta-band feedback that modulates OB activity via phase–amplitude coupling and transient beta bursts. This bidirectional communication supports a dynamic updating mechanism that maintains perceptual constancy across varying environmental odor concentrations. Our findings reveal a previously uncharacterized oscillatory framework for intensity coding in the human olfactory system, highlighting the primacy of perception over stimulus properties and offering a mechanistic basis for predictive processing in early sensory circuits.

Funding: This work was supported by the Knut and Alice Wallenberg Foundation (KAW 2018.0152; https://kaw.wallenberg.org/ ; awarded to JNL), the D2Smell ERC Synergy award (101118977; https://erc.europa.eu/ ; awarded to JNL), and the Swedish Research Council (2024-01605_VR; https://www.vr.se/ ; awarded to AA). The use of the MR facility at Stockholm University (SUBIC) was made possible by an institutional grant to SUBIC (SU FV-5.1.2-1035-15; https://www.su.se/english/divisions/subic---stockholm-university-brain-imaging-centre ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2026 Nordén et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

A) Odor concentration is a physical property (number of odorant molecules), whereas perceived intensity reflects the participant’s subjective experience. B) Single-trial structure. After a jittered pre-stimulus period, odor is delivered at inhalation onset and remains on for 2 s. Immediately after odor offset, participants rate perceived intensity and valence on a 0–10 visual analog scale. The response and subsequent rest period yield a minimum inter-trial interval of 14 s. C) Differences between the Low and High concentration conditions for each odor, expressed as concentration multiplier. D) Rated perceived intensity based on the median split of all individual trials. Each dot represents the mean rating of a participant with their ratings in both conditions connected with line-marking. The bar graph shows the mean perceived difference between conditions; blue denotes Low, red denotes High, and the error bar represents SEM. Data supporting this figure are available at https://doi.org/10.17605/OSF.IO/T7HJ2 . E ) Method to record signals from the OB and PC.

In the present study, we used the electrobulbogram to investigate how the OB and the PC, two primary nodes of the olfactory system, process and communicate odor intensity at their earliest stages ( Fig 1E ). We focused on neural oscillations in response to two key aspects: odor concentration and perceived odor intensity ( Fig 1A – 1D ). Given that oscillatory bursts observed in electrical fields have been proposed as proxies for population spiking or activity motifs [ 12 ] and previous animal studies have largely focused on spiking activity in relation to intensity, we also assessed burst activity in relation to both concentration and perceived intensity to enable more direct comparisons with the existing literature.

No study has yet examined how the human OB processes odor intensity and only a few studies have explored how the human brain more broadly represents odor intensity. Electrophysiological and imaging studies have revealed that neural responses obtained from the scalp or PC are more tightly linked to the subjective perception of intensity than the mere chemical concentration of odorants [ 8 , 9 ]. However, the mechanisms by which the initial nodes within the human olfactory system communicate intensity-related information are still unknown. This knowledge gap is largely due to the dearth of noninvasive methods for recording OB and PC oscillatory responses outside of surgical settings. Recent technological developments have, however, enabled in vivo, noninvasive, and simultaneous electrophysiological assessment of the OB and PC functions in humans. Specifically, we recently developed and validated the electrobulbogram (EBG), an EEG-based method for assessing oscillatory activity of the human OB and PC [ 10 , 11 ].

In nonhuman mammals, it is thought that encoding of odor concentration occurs through precise temporal patterns of mitral and tufted cell spiking in the olfactory bulb (OB), where increasing concentration leads to earlier and more synchronized responses [ 2 , 3 ]. These temporal patterns are believed to be further processed in the piriform cortex (PC), where recurrent network dynamics amplify early OB inputs and suppress later activity, forming ensemble codes for concentration [ 4 ]. Critically, these mechanisms are embedded in oscillatory neural dynamics with the amplitude and frequency of OB and PC oscillations varying systematically with odor concentration and volatility [ 5 – 7 ]. Together, these studies have laid the foundation for a temporal and oscillatory framework of odor intensity processing; however, this framework remains untested in humans.

The lack of trial-specific information presents a challenge, given that perceived odor intensity is not a direct reflection of the physical concentration of odorant molecules. Rather, it represents a sophisticated perceptual construct in which the brain’s early olfactory structures transform the raw chemical input received from odor receptors into the subjective experience of odor intensity. While stimulus concentration serves as the initial sensory signal, perceived intensity exhibits an imperfect logarithmic relationship with concentration [ 1 ].

A core function of all sensory systems is to determine the intensity of a stimulus. This basic perceptual processing, where stimulus quantity is transformed into perceived intensity, enables us to differentiate a touch from a punch, a whisper from a scream, a candle from the sun, and a whiff from a stench. Today, we have a detailed mechanistic understanding of how the human auditory and visual systems encode both physical stimulus properties (e.g., volume and luminance) and their perceived perceptual counterparts (e.g., loudness and brightness). This is not the case for the human olfactory system, where nearly all our knowledge of how the brain processes odor intensity comes from animal studies. These animal studies primarily focus on stimulus concentration, as it is difficult to obtain subjective, trial-by-trial ratings of perceived odor intensity from animal models.

In the PC, perceived intensity was associated with significant beta burst activity in a time window that closely matched the initial OB gamma burst effect (800–1100ms; t = 2.28, p = .027, CI = [.0170, .0370]). Critically, this is the same period in which the coherence and Granger analyses indicated top-down information transfer from PC to OB in the beta band, and the same interval in which phase–amplitude coupling linked PC beta phase to OB gamma amplitude.

In contrast, perceived intensity produced robust, cluster-corrected effects. In the OB, gamma bursts were significantly related to perceived intensity at two distinct time points. The first occurred 800–1,100 ms after odor onset (t = 2.88, p = .006, CI = [.0012, .0108]). Interestingly, after this event, the OB gamma burst activity then became anti-correlated to perceived odor intensity, suggesting suppression of high-frequency bursts by intensity. Later, burst activity in the gamma band was again positively related to odor intensity ratings between the timepoints 1,350–1,450 ms (t = 2.53, p = .015, CI = [.0075, .0225]).

When relating odor concentration to burst activity, we found no beta or gamma effects in the OB that survived cluster correction. We did find, however, at an uncorrected level (p < .05) that beta burst rate was associated with concentration from 1,500 to −1800 ms into the trial (t = 2.80, p = .0051, CI = [7.70e−5, 5.85e−4]). A similar, likewise uncorrected pattern was observed in the PC at the same time (t = 2.87, p = .004, CI = [1.50e−4, 7.94e−4]).

Analyzing intermittent burst activity provides additional insights beyond those offered by traditional wave-based analyses because it considers that oscillatory activity is intermittent and vary from trial to trial. This aligns with the view that cognition and executive control are supported by rapid transitions between discrete, transient neural states, rather than by slow, continuous dynamics [ 13 ]. Furthermore, oscillatory bursts are closely linked to population spiking, making them a useful proxy for ensemble activity. Thus, to further investigate the relationship between odor intensity processing, cognition, and neural oscillations, we analyzed single-trial burst activity in the beta and gamma bands during odor presentation, examining its association with both odor concentration and perceived intensity. Burst counts within each frequency band were extracted for every trial, and their association with concentration and perceived intensity was evaluated using linear mixed-effects models, with statistical significance assessed via a Monte–Carlo permutation test (1,000 permutations) using weighted cluster-mass correction.

Guided by this hypothesis, we performed a PAC-analysis in this window and found that beta phase (12–16 Hz) modulated gamma amplitude (70–90 Hz) as a function of perceived intensity (t = 2.35, p = .023, CI = [.0137, .0323]; Fig 3F ). Granger causality also indicated that the beta-band region observed around ~1,400 ms falls within a window of significant PC → OB information transfer, consistent with continuous updating of OB representations by the PC.

To assess the direction of information flow, we performed frequency-resolved Granger causality, contrasting OB → PC and PC → OB to remove shared variance. High gamma band connectivity reflected information transfer from OB to PC (t = 5.04, p = 8.3e−6, CI = [0.2275, 0.5313]; Fig 3C ). In contrast, lower frequencies reflected information transfer from PC to OB (t = 2.46, p = .0134, CI = [0.0833, 0.8417]). Thus, the beta-band activity observed around ~1 s in the coherence spectrum likely corresponds to top-down communication from PC to OB.

Odors were selected to be neutral in valence and displayed little variability ( S5A Fig ). Moreover, perceived valence was included in analyses as a covariate of no interest. Still, to ensure that coherence-based decoding was not driven by valence, we conducted a parallel classification of perceived valence (median-split procedure on trials and participants). As shown in S5B Fig , there was no overlap between perceived valence and perceived intensity in the coherence spectrum.

A third cluster spanned 1,200–1,500 ms and extended from beta into low gamma frequencies (10–38 Hz; t = 3.03, p = .004, CI = [.0001, .0079]). Because this cluster covered both frequency ranges, we examined classification performance separately for beta and gamma. In both bands, high perceived intensity was decoded more accurately (76%) than low (67% in beta; 63% in gamma).

A second decoding window was observed around 1 s after odor onset in the beta band (12–25 Hz; t = 2.56, p = .014, CI = [.0068, .0212]). This time period coincides with significant beta clusters identified in the mixed-effects analyses for both OB and PC. Classification accuracy in this region was highest overall (74%), with better performance for high perceived intensity (78%) than low (70%).

The first significant decoding window occurred early, ~200 ms after odor onset, in the higher gamma band (70–90 Hz, t = 2.56, p = .014, CI = [.0068, .0212]). As shown in the confusion matrix ( Fig 3E – 3I ), the classifier performed better at identifying low perceived intensity (76%) than high (67%). This early gamma effect overlaps temporally with the gamma-band activity observed in the linear mixed-effects analysis of the OB and PC power spectra ( Fig 2C , 2D ). Although those early OB/PC gamma effects did not survive cluster correction they were significant on the voxel level. Together, the converging evidence suggests that both OB and PC encode perceived intensity at this early stage.

A) Classification of high vs. low odor concentration in the coherence spectrogram revealed no significant time–frequency regions . B) In contrast, classification of high vs. low perceived intensity showed significant decoding accuracy in both the gamma and beta bands. C) Granger causality demonstrated information flow from OB → PC in the gamma band, and from PC → OB in the beta band, indicating that the beta-band region identified in B reflects top-down signaling from PC to OB. D) Time–frequency regions of interest identified in B. E) Confusion matrices for the four significant time–frequency clusters shown in B. F) Phase–amplitude coupling (PAC) within the 950–1,100 ms window showed beta-phase modulation of gamma-amplitude in the OB, consistent with PC-to-OB feedback at the time when perceived intensity is communicated back to the OB. Data supporting this figure are available at https://doi.org/10.17605/OSF.IO/T7HJ2 .

We next asked how odor concentration and perceived odor intensity are communicated between the OB and PC. To address this, we first computed the coherence spectrum between the two regions and subsequently applied separate SVM classifiers for binary decoding of either concentration (high/low) or perceived intensity (high/low, median split). The classifier could not decode concentration in any time–frequency region ( Fig 3A ). In contrast, it successfully decoded perceived intensity from several time–frequency windows ( Fig 3B ).

Finally, because our primary concentration analysis used a dichotomized variable (High/Low), we examined whether the lack of variance might obscure effects. We transformed the six odor conditions into a continuous “effective concentration” metric (vapor pressure × dilution) and reran all time-frequency analyses ( S6A , S6B Fig ). We also quantified absolute concentration values, measured at the olfactometer outlet (within the experiment, located about 2 cm into the nose) using proton transfer reaction time of flight mass spectroscopy (PTR-TOF). These analyses replicated the original finding: no cluster-corrected effects of effective concentration on time–frequency amplitude ( S7A , S7B Fig ).

We obtained a broad range of intensity percepts for nearly all participants. To verify that this reflected perception rather than idiosyncratic rating behavior, we utilized the established fact that there is a weak but linear relationship between perceived intensity and the sniff response. Subjective ratings correlated positively with sniff AUC (t = 3.79, p = .00015), whereas physical concentration (High/Low) did not (t = .19, p = .85). Sniff parameters were included as covariates of no interest in all other models.

Animal studies have shown that the OB encodes odor concentration [ 5 – 7 ]. Thus, the lack of clear concentration effects in our time–frequency analysis was unexpected. To test whether concentration effects might be present outside the time-frequency domain, we performed a PCA on raw OB signals for high versus low concentration. The trajectories diverged early in the odor period (0–2000 ms) before converging later ( S3 Fig ), suggesting concentration-dependent processing that is not captured by time-frequency amplitudes.

To assess whether the observed effects of perceived intensity on OB and PC power reflected a cortex-wide phenomenon, we performed the same analysis in two additional control regions: a primary sensory region (primary visual cortex, V1) and a higher-order region implicated in global arousal and attentional processes (posterior cingulate cortex, PCC). Using the identical cluster-based permutation framework, no significant intensity-related clusters were observed in either control region ( S6 Fig ). These findings suggest that the reported effects are not ubiquitous across the cortex and instead appear relatively specific to the olfactory ROIs.

To account for the rapid dynamics of olfactory processing, we repeated the analyses using a restricted time window of the first 500 ms following odor onset. This analysis did not reveal any significant cluster-corrected effects for either perceived intensity or odor concentration.

Gamma-band results from the mixed-effect models showed a similar pattern. In the OB, gamma power (70–100 Hz) was significantly related to perceived intensity between ~1,100 and 1800 ms, and a later cluster emerged in the PC (80–100 Hz) from ~1,400 to 2000 ms. Associations were comparable in the PC (t = 2.37, p = .022, CI = [.020, .064]) and the OB (t = 2.41, p = .02, CI = [.021, .061]). As in the beta band, early OB gamma effects (~200 ms; 70–80 Hz) were present but did not survive cluster correction. The activity was also visible in the raw power spectrum, both for perceived intensity and concentration.

These effects were also visible in the raw power spectra ( S3 , S4 Figs ). When trials were divided by perceived intensity using a median split, one significant cluster was observed in the PC in the gamma band (~30 Hz, 1,500–1800 ms; t = 3.7, p = .018, CI = [.0064, .0116]). Similarly, dividing trials by low versus high odor concentration revealed a significant cluster in the beta band (~25 Hz, 800–1,200 ms; t = 3.5, p = .049, CI = [.0094, .0176]) overlapping with the time window identified in the mixed-effects model for perceived intensity.

In contrast, perceived odor intensity revealed robust cluster-corrected effects in both neural regions when continuous intensity ratings were modeled as a fixed effect. In the OB, beta-band (12–25 Hz) power was significantly related to intensity ratings from 600 to 1,600 ms after odor onset (t = 3.51, p = .001, CI = [.0081, .0101]; Fig 2C ). A similar beta-band effect in the PC occurred slightly later, from 850 to 2000 ms in the 15–30 Hz range (t = 3.03, p = .004, CI = [.0142, .0222]; Fig 2D ). Both regions showed intermittent beta-band peaks as early as ~200 ms after odor onset; however, these early peaks did not survive cluster correction. Around 1 s into the trial, the association in the PC (t = 4.65, p = 3.36 × 10⁻⁶, CI = [.0757, .186]) was comparable to the ones in the OB (t = 4.08, p = 4.49 × 10⁻⁵, CI = [.0726, .210]).

A) Linear mixed-effects model applied to the OB time–frequency spectrum testing whether odor concentration (High vs. Low) predicts power amplitude. Subject was modeled as a random intercept, and perceived valence and sniff parameters were included as fixed effects. Significant clusters after weighted cluster-mass correction are outlined in black . B) Same as A, but for PC. C) Same model as in A but with perceived odor intensity instead of odor concentration, revealing significant beta- and gamma-band clusters associated with perceived intensity in the OB. D) Same as C but applied to the PC, revealing significant beta- and gamma-band clusters associated with perceived odor intensity in the PC. Data supporting this figure are available at https://doi.org/10.17605/OSF.IO/T7HJ2 .

When examining odor concentration, we found surprisingly little evidence of concentration-related modulation in the time–frequency spectrum. Despite a more than average 10-fold difference in stimuli concentration between weak and strong concentrations ( Fig 1C ), only small, scattered time–frequency regions in the OB and PC showed any relationship between concentration and power amplitude ( Fig 2A , 2B ), and none survived cluster correction.

We first determined whether odor concentration or perceived odor intensity is related to frequency amplitudes by assessing each time-frequency point in the power spectrum with a linear mixed-effects model. To control for individual differences in rating behavior, we included Participant as a random intercept, and to control for the influence of perceived odor valence and sniff size, we included perceived valence and the sniff parameters amplitude and AUC as fixed effects. Statistical significance was evaluated using a Monte–Carlo test with 1,000 permutations and weighted cluster-mass correction.

Discussion

We demonstrate here that oscillatory activity within the OB and PC is clearly associated with perceived odor intensity, but only weakly with physical odor concentration. Specifically, we show that perceived odor intensity information, after initial processing in the OB within the gamma band, is transmitted as a bottom-up signal from the OB to the PC via gamma-band oscillations and is subsequently modulated through top-down feedback from the PC to the OB via beta-band oscillations. This top-down modulation updates the internal state of OB gamma activity through phase-amplitude coupling, driven by an underlying burst-like dynamic. No similar patterns were found for physical odor concentration, suggesting that oscillatory activity in the human OB and PC primarily reflects perceptual processing rather than stimulus concentration. This constitutes the first direct evidence in humans that subjective odor intensity, not physical concentration, is encoded through coordinated oscillatory dynamics between the OB and PC. These findings challenge the traditional view that early olfactory processing reflects stimulus concentration and instead reveal that perception is already manifested at the earliest hierarchical stages of the human olfactory system.

We found a strong link between OB processing and the subjective perception of odor intensity, suggesting that by the time oscillatory population activity is measured (hundreds of milliseconds post-stimulus), it reflects a perceptually transformed representation. This view is supported by rodent studies showing that the OB can encode internal variables corresponding to perceived intensity rather than just reporting concentration linearly [3], and that intensity coding can involve structured population dynamics [14].

At the same time, animal research has also shown that the OB processes odor concentration [2,3], so the lack of a clear link in our data between stimulus concentration and neural processing was therefore unexpected. However, this absence of concentration effects cannot be attributed to nonlinear liquid–vapor relationships [15]. We directly measured vapor-phase concentrations using PTR-TOF mass spectrometry and repeated all analyses using empirically derived concentration values. These analyses yielded results indistinguishable from those based on nominal dilution levels. Although model-free PCA of raw OB signals revealed early divergence between concentrations, these effects were not reflected in oscillatory amplitude measures. Thus, while concentration-dependent processing may occur at early sensory stages, it does not appear to dominate the coordinated oscillatory dynamics measured here.

Notably, an additional cluster-based analysis revealed a small late concentration-related effect in the beta range that overlapped temporally with the intensity-related activity. However, this effect was not observed in the linear mixed-effects model and was less consistent across analytical approaches. The temporal overlap with intensity-related dynamics suggests that such activity may reflect shared or interacting processes rather than a distinct encoding of physical concentration. Thus, while some late concentration-related signatures may be present, they appear weaker and less robust than the effects associated with perceived intensity.

These findings align with a broader view of intensity coding in animal models, where concentration is not represented simply through increased firing rates, but through temporal and population-level dynamics. In the rodent piriform cortex, overall firing rates remain relatively stable across concentrations for a given odor identity, whereas concentration is reflected in latency shifts and evolving ensemble patterns [2]. Specifically, the piriform cortex uses a dual-component code where an initial subset of neurons responds rapidly and largely independent of concentration (carrying odor identity), while a later subset responds with latency shifts as concentration changes, thus conveying odor intensity [2]. Such temporal coding principles have also been described in insects and fish [7,16], suggesting that dynamic timing, rather than rate alone, underlies magnitude representations across species.

Our findings extend this framework to humans, demonstrating that subjective intensity, rather than physical concentration, is reflected in early human OB–PC communication via gamma-band oscillations around 200 ms post-stimulus. This timing coincides with the time window when concentration becomes decodable in the rodent PC. Similarly, prior work using the electrobulbogram method has shown that odor identity can be decoded from OB-PC gamma connectivity at earlier latencies (100 ms) [11]. Together, these results suggest that the human olfactory system employs a temporally staged sequence, where identity and intensity are communicated along distinct OB–PC gamma-band channels in rapid succession. Crucially, the later beta-band feedback (1 s post-stimulus) from PC to OB, strongly tied to perceived intensity, may act to refine or reinforce this perceptual representation. This interpretation is consistent with the known role of the piriform cortex in integrating sensory input with contextual and cognitive signals, and with theories that beta oscillations coordinate top-down modulation in olfactory circuits [14,17,18]. Together, these findings support a temporally resolved model in which subjective odor intensity emerges through iterative feedforward and feedback interactions within the human OB–PC loop.

Our observation that gamma oscillations mediate bottom-up transfer while beta oscillations mediate top-down feedback resonates strongly with theoretical and experimental work in animal models. It has long been proposed that fast gamma oscillations primarily reflect local circuit processing within the OB and the initial feedforward relay of olfactory information, whereas slower beta oscillations engage a larger loop including the olfactory cortex and potentially higher-order areas [17,19]. For example, OB beta oscillations are typically weak or absent under anesthesia or when cortical feedback is disrupted, while gamma oscillations persist, indicating that beta rhythms critically depend on an intact OB-PC loop [19,20]. Our data, obtained in humans, demonstrates significant Granger-causal influences from OB to PC in the high gamma band, and conversely from PC to OB in the beta band during intensity processing, supporting this model.

This sequential processing of odor intensity mirrors a two-stage scheme proposed in olfaction and other sensory systems [11,16,18,21,22], wherein an initial feedforward sweep is followed by feedback that fine-tunes the representation, which fits with the timing of odor-evoked gamma and beta oscillations found in human intracranial PC recordings [23]. This OB-PC loop could potentially be viewed through a predictive coding lens [24], where the cortex sends predictions to early sensory areas, and discrepancies (prediction errors) drive updates of internal representations to refine perception [25,26]. The observed gamma/beta cycle could implement such a mechanism, iteratively minimizing the mismatch between expected and actual intensity.

Importantly, the association between gamma and feedforward processing, and beta and feedback processing, should be regarded as a functional heuristic rather than a strict rule. We therefore base our interpretation not on frequency alone, but on the combined evidence of temporal ordering, directionality, behavioral relevance, and consistency with known olfactory circuitry. Within these constraints, the OB–PC dynamics observed here provide converging evidence for recurrent feedforward and feedback interactions underlying subjective odor intensity processing, paralleling communication principles described in other sensory systems [27].

Evidence that the internal representation of the OB is updated is found in single-trial analysis of our data showing intermittent oscillations [13,28,29], and from the observation that the rate and timing of bursts in the beta and gamma bands are systematically related to perceived intensity. Specifically, OB gamma-band bursts showed a biphasic positive relationship with higher perceived intensity (an initial surge at ~0.8–1.1 s, followed by suppression, then a later surge at ~1.35–1.45 s). Crucially, PC beta-band bursts occurred in nearly the same early time window (~0.8–1.1 s) as the OB’s initial gamma burst surge. This temporal coincidence, coupled with our finding that beta phase in the PC modulated OB gamma amplitude (PAC) during this period, suggests a sequential interaction: as the PC evaluates intensity (reflected in a PC beta burst), its feedback triggers or “gates” OB gamma bursts, thereby updating the OB’s representation in discrete epochs. This aligns with emerging views of cortical function where information is processed in transient “packets” or bursts, facilitating efficient communication and plasticity. Gamma bursts are thought to reflect synchronized spiking in local networks, while beta bursts often involve more distributed networks [17,29]. Thus, a PC beta burst, possibly influenced by higher-order inputs, could prime piriform ensembles to send temporally precise volleys of activity that manifest as synchronized gamma bursts in the OB, ensuring robust and adaptive population coding of intensity. Such within-sniff updates, occurring over hundreds of milliseconds, are sufficiently fast to influence perception in real time.

Interpreting frequency-specific and directional interactions from noninvasive EEG signals requires care. Granger causality captures directed statistical dependencies between reconstructed signals rather than direct biological causation, and spectral peaks in Fourier-based analyses can reflect transient or nonsinusoidal events rather than sustained oscillations [30–32]. We therefore do not claim to have demonstrated causal neural mechanisms in a strict biological sense. What we do claim is that the pattern of effects, replicable across directed connectivity, phase–amplitude coupling, and single-trial burst dynamics, and consistent with established gamma and beta circuit mechanisms in animal models of olfaction [33–35] is precisely what one would predict from an OB–PC circuit implementing feedforward and feedback processing of odor intensity. The convergence of independent measures, rather than any single analysis, constitutes the evidential basis for our interpretation. Together, these observations support the presence of temporally structured, frequency-specific interactions within the OB–PC circuit that are consistent with known anatomy and task timing.

A critical question is whether effects show anatomical specificity. We therefore repeated the analyses in the primary visual cortex and the posterior cingulate cortex. No comparable intensity-related effects were observed under identical statistical criteria, arguing against a global anticipatory or task-structure account. Combined with the directionality and cross-frequency coupling observed within the OB–PC loop, these findings support the interpretation that late beta activity reflects recurrent refinement of perceived intensity rather than nonspecific expectancy effects. However, this does not exclude the involvement of content-specific expectancy mechanisms. Accordingly, the late beta activity may reflect an interaction between perceptual refinement and task-related processes within the olfactory system. Future studies using passive odor presentation or delayed-response paradigms will be important to disentangle these components.

In summary, the human olfactory bulb and piriform cortex engage in a dynamic, frequency-specific dialogue to represent perceived odor intensity. In this view, even the most fundamental aspect of perception becomes an active, dynamic, and contextually informed process. Initial bottom-up gamma oscillations convey early sensory information, which is subsequently refined by top-down beta-band feedback from the cortex, implemented through PAC and coordinated neural bursts. This framework highlights the primacy of subjective perception in shaping early olfactory processing and suggests that, similar to other sensory systems, the olfactory bulb and piriform cortex rely on iterative network interactions to construct a stable and behaviorally relevant representation of the world.

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