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Received: 29 October 2025

Revised: 27 November 2025

Accepted: 6 December 2025

Published: 10 December 2025

Citation: Yang, L.; Zhang, C.; Wu, W.;

Xie, J.; Ding, Z. Advancements and

Applications of EEG in Gustatory

Perception. Brain Sci. 2025,15, 1317.

https://doi.org/10.3390/

brainsci15121317

Licensee MDPI, Basel, Switzerland.

This article is an open access article

distributed under the terms and

conditions of the Creative Commons

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(https://creativecommons.org/

licenses/by/4.0/).

Review

Advancements and Applications of EEG in Gustatory Perception

Lingfeng Yang 1, Chengpeng Zhang 1, Wei Wu 2, Jing Xie 1,3 and Zhaoyang Ding 1,3,4,*

1College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China;

m230351137@st.shou.edu.cn (L.Y.); d250400159@st.shou.edu.cn (C.Z.); jxie@shou.edu.cn (J.X.)

2Shanghai Key Laboratory of Emotions and Affective Disorders, Shanghai Jiao Tong University School of

Medicine, Shanghai 201600, China; weiwuneuro@sjtu.edu.cn

Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, Shanghai 201306, China

4Marine Biomedical Science and Technology Innovation Platform of Lin-Gang Special Area,

Shanghai 201306, China

*Correspondence: zyding@shou.edu.cn

Abstract

Electroencephalography (EEG) is a powerful tool for investigating gustatory perception,

offering high temporal resolution and non-invasive brain activity recording. This review

highlights the ability of EEG to reveal the complex interactions between sensory input,

emotional responses, and cognitive evaluation in the process of taste perception. This

review examines the physiological basis of taste, focusing on key brain regions and how

environmental and psychological factors inﬂuence taste perception. It also discusses the

methods and applications of EEG technology, including its principles, signal features,

and measurement methods. Notably, EEG markers like event-related potentials (ERPs),

frequency band power analysis, and brain network connectivity are essential for under-

standing the neural dynamics of taste processing. This review concludes with potential

future research directions, including the integration of EEG with other neuroimaging tech-

niques, cross-cultural studies on gustatory perception, and the use of EEG biomarkers in

early neurological disease diagnosis.

Keywords: electroencephalography (EEG); gustatory perception; sensory; brain regions;

brain network connectivity

1. Introduction

Gustatory perception plays a central role in how we evaluate and interact with food,

inﬂuencing our preferences, nutritional choices, and overall enjoyment. This process is

complex, involving not only the basic recognition of tastes such as sweet, salty, sour, bitter,

and umami, but also the integration of sensory inputs like smell and texture [

]. Beyond

sensory processing, gustatory perception is deeply connected to emotions, memories,

and cognitive factors. These elements shape how we perceive food and contribute to

the variability in individual taste preferences, inﬂuenced by genetic, environmental, and

cultural factors [

]. Recent research has explored these complexities, revealing how

taste is not just about the physiological experience but also a multi-dimensional process

shaped by various internal and external factors. Advances in neuroimaging technologies,

particularly in the context of EEG, have provided new insights into how the brain processes

taste and integrates it with emotional and cognitive responses. Notably, taste perception

integrates sensory, cognitive, and emotional processes through coordinated large-scale

brain networks [

]. Disruption or modulation of these networks directly impacts hedonic

Brain Sci. 2025,15, 1317 https://doi.org/10.3390/brainsci15121317

Brain Sci. 2025,15, 1317 2 of 25

evaluation and food selection, underscoring the importance of network-level analysis in

understanding taste perception [5].

EEG, phase-contrast magnetic resonance imaging, and electrical impedance tomog-

raphy are commonly employed in studies of brain taste perception [

]. Among these

technologies, EEG stands out due to its high temporal resolution and non-invasive nature,

making it an excellent tool for studying gustatory perception [

]. The earliest method

explored for EEG recognition was EEG using direct cortical electrodes (ECoG). Although

ECoG has been phased out due to its invasiveness, it laid the foundation for neuro-sensory

correlation research. Non-invasive EEG has become the mainstream approach for sensory

recognition due to its high temporal resolution and non-invasive nature. While non-

invasive EEG remains dominant, emerging minimally invasive/semi-invasive interfaces

enable precise sensory decoding, hold promise for improved recognition performance, and

represent a key focus for future research. EEG measures the brain’s electrical activity in

real-time by recording brainwave patterns, which are associated with different cognitive

and sensory processes. These brainwaves, categorized into frequency bands such as delta,

theta, alpha, beta, and gamma, provide insights into the neural processes involved in

taste perception [

]. Recent studies using EEG have shown that brain regions such as

the insular cortex and the orbitofrontal cortex (OFC) are critically involved in the pro-

cessing of taste and its integration with emotional responses. For example, a study by

Kathrin Ohla et al. [10]

demonstrated that the insular cortex plays a pivotal role in both the

sensory discrimination of tastes and the emotional evaluation of food, with EEG showing

distinct brainwave patterns in response to different tastes like sweet or bitter. EEG is partic-

ularly useful for analyzing ERPs, which are time-locked brain responses to speciﬁc sensory

stimuli. Research by Saša Zorjan et al. [

] found that the P300 component, often associated

with attention and cognitive evaluation, was prominently elicited when participants tasted

foods, they had positive or negative associations with, highlighting the role of attention and

evaluation in gustatory processing. Furthermore, the N400 component, typically linked to

semantic processing and emotional responses, was shown to reﬂect the cognitive evaluation

of unfamiliar tastes, signaling how the brain links taste perception with prior knowledge

or expectations [

]. Additionally, EEG helps track brain oscillations, revealing how dif-

ferent brain regions interact during taste perception. For instance, studies have shown

that theta waves are involved in early sensory processing, particularly in distinguishing

between tastes, while alpha waves are linked to higher-order cognitive functions such

as decision-making related to food preferences. A study by Diana Rico Pereira et al. [

]

demonstrated that alpha band oscillations are prominent when participants are asked to

make decisions about food, emphasizing the role of cognitive control in ﬂavor perception

and choice. These capabilities make EEG indispensable for understanding the dynamic

and multi-dimensional nature of gustatory perception, including its sensory, emotional,

and cognitive components.

This work explores the advancements in EEG technology and its applications in gusta-

tory perception research (Scheme 1). The ﬁrst section covers the physiological mechanisms

of taste processing, explaining how taste signals are detected and transmitted to the brain,

offering a foundation for understanding gustatory perception. The second section exam-

ines how environmental, psychological, and individual factors shape taste experience,

emphasizing the importance of considering these factors when studying gustatory per-

ception. The third section provides an overview of EEG technology, its principles, and

the speciﬁc metrics used in food sensory research, highlighting EEG’s unique capabilities

for studying gustatory perception. The ﬁnal section explores the various applications of

EEG in gustatory research, including event-related potential (ERP) analysis, frequency

band analysis, and brain network analysis, showing how these techniques contribute to

Brain Sci. 2025,15, 1317 3 of 25

a deeper understanding of gustatory processes. This review not only summarizes the

current achievements in this rapidly evolving ﬁeld but also outlines potential directions for

future research.

Scheme 1. The development of electroencephalography technology and its application in taste

perception research.

Literature Search Methodology

To ensure the comprehensiveness and rigor of this review, relevant research was re-

trieved from PubMed, Web of Science, and Scopus databases using the following keywords:

“EEG” OR “electroencephalography” combined with “gustation” OR “taste perception”

OR “ﬂavor” OR “event-related potential” OR “neural oscillation” OR “brain connectivity”.

The search period was restricted to 2000–2024, focusing on peer-reviewed original research

and reviews published in English. Studies were screened based on title and abstract for

relevance to EEG-based gustatory research, with full-text review conducted for key publi-

cations. A total of 139 studies were included in the ﬁnal synthesis, covering ERP analysis,

frequency band research, brain network studies, and methodological advancements.

2. Taste Perception

2.1. The Physiological Basis of Taste Mechanisms

Taste, a key human chemosensory function, is critical for nutrient intake and food

safety recognition. Its physiological mechanism involves three core links: peripheral signal

detection, neural conduction, and central integration [

]. An in-depth understanding of

the physiological basis of gustation is important for analyzing human dietary behavior and

preventing related diseases. Taste perception begins with the taste bud structures on the

tongue and soft palate (Figure 1A). Taste perception is activated when taste stimuli come

into contact with taste receptors dissolved in saliva and mucus [

]. These specialized

receptor cells are capable of recognizing ﬁve basic tastes: sweet, salty, sour, bitter, and

umami. The perception of sweet, bitter, and umami is largely dependent on a family of

G protein-coupled receptors on the cell membrane [

]. When speciﬁc molecules bind

to these receptors, they activate a series of intracellular signaling cascades. In contrast,

Brain Sci. 2025,15, 1317 4 of 25

the detection of salty and sour tastes is mediated directly through ion channels, e.g.,

sodium ions enter the cell through speciﬁc membrane channels to trigger depolarization,

or hydrogen ions act directly on channel proteins to change their conformation [

]. These

different types of sensory information are transmitted via cranial nerves, such as the facial,

glossopharyngeal, and vagus nerves, and reach the nucleus tractus solitarius of the medulla

oblongata for initial integration before being relayed via the thalamus for projection to

higher cortical areas.

In the brain, speciﬁc areas of the insula are considered to be the primary taste cortex,

which is primarily responsible for the initial analysis and encoding of basic taste charac-

teristics. Here, the nervous system is able to distinguish between different taste qualities

and assess their intensity. As information is transmitted to more advanced cortical areas,

particularly the anterior insula and OFC, gustatory information interacts in complex ways

with higher cognitive functions such as emotion and memory [

]. This integration makes

the taste experience not only chemosensory, but also closely related to individual experi-

ences and emotional states. Studies have shown that there are differences in the spatial

distribution of sweet and bitter taste representations in the cortex, reﬂecting the different

response patterns of the nervous system when confronted with nutritive and potentially

toxic substances [18].

2.2. The Inﬂuence of Environmental and Psychological Factors on Taste Perception

Taste, as a complex perceptual experience, is not only regulated by a physiological

basis, but is also strongly inﬂuenced by environmental and psychological factors [

]. The

experience of taste in daily life is not only a chemical sensation, but also involves sophis-

ticated sensory integration. Olfactory, tactile and other sensory channels work together

to build a complete “ﬂavor” perception. For example, the volatile aroma molecules of

a food are integrated with taste bud stimuli, and the texture and temperature of a food

inﬂuence the taste experience via the trigeminal nerve, which is a key component of taste

perception [19].

The modulation of taste function is diverse. Endogenous factors include the regulation

of taste sensitivity by metabolic hormones such as leptin and the role of dopamine in the

reward mechanism, while exogenous factors involve dietary habits and taste adaptation

triggered by environmental factors. Sodium chloride is transmitted through two primary

pathways: the aminopyridine-sensitive and aminopyridine-insensitive pathways. The

aminopyridine-sensitive pathway is mediated by the epithelial sodium channel (ENaC),

through which sodium ions enter taste receptor cells (TRCs) during chewing, triggering

depolarization (Figure 1B). This depolarization activates voltage-gated neurotransmitter

release channels composed of CALHM1 and CALHM3. These channels open during

membrane depolarization, leading to ATP release, which then activates taste afferent

neurons. The electrical signals from salt taste receptors are transmitted to the central

nervous system, allowing for the perception of saltiness [

]. Long-term high-salt diets

reduce the sensitivity to salty taste.

Environmental factors are crucial in taste modulation, especially the effect of visual

stimuli on taste anticipation. Studies have shown that food color signiﬁcantly alters the

perception of sweetness and acidity, with red enhancing sweetness and green increasing

sourness anticipation [

]. Light intensity also affects taste sensitivity, with ﬂavor dis-

crimination being stronger in bright light [

]. Tactile factors are equally important, with

the weight, material and color of utensils systematically altering food taste perception.

For example, heavy cutlery increases ratings of food richness, and white plates enhance

sweetness perceptions of desserts [

]. This cross-sensory integration shows that taste

perception is the result of multimodal construction.

Brain Sci. 2025,15, 1317 5 of 25

Psychological states play a signiﬁcant role in taste modulation. Acute stress conditions

resulted in reduced salivary secretion, elevated taste thresholds, and diminished sweet

and fresh ﬂavor perception. Chronic stress remodeled taste preferences and increased

craving for high-fat and high-sugar foods by altering hypothalamic-pituitary-adrenal

axis function [

]. Depressed mood reduces taste sensitivity and may be associated with

dysfunction of the monoamine neurotransmitter system. The decline in taste function in

the elderly is partly due to changes in neuroplasticity caused by reduced environmental

stimuli [

]. The multidimensional mechanisms of taste modulation, linking physiology,

psychology and the environment, provide a unique window into human eating behavior.

2.3. Individual Differences in Taste Perception

Individual differences in taste perception originate from the complex interaction of

genetic, molecular and environmental factors, and the analysis of its mechanism and in-

dustrial application has become a research frontier in food science and sensory biology.

At the molecular level, polymorphisms in taste receptor genes form the core basis of dif-

ferences. For example, the bitter taste receptor encoded by the TAS2R38 gene exhibits

a bimodal distribution of sensitivity to phenylthiourea (PROP), and a single nucleotide

polymorphism (SNP) resulting in a proline-to-alanine substitution at position 49 signiﬁ-

cantly alters the conformation of the receptor’s ligand-binding domain, resulting in about

25% of the population being “hypersensitive” [

]. A similar mechanism exists in the

sweetness receptor TAS1R2/T1R3 heterodimer, where a mutation at rs35874116 reduces

the receptor’s afﬁnity for natural sugars but increases its sensitivity to artiﬁcial sweeteners,

such as aspartame, and the dynamics of this receptor-ligand interaction directly affects

an individual’s threshold of preference for sweet substances [

]. In addition, advances

in salivary proteomics have revealed that gustin protein (CA6 gene product) regulates taste

bud cell differentiation through zinc ion transport, and that its genetic polymorphisms

lead to up to three-fold differences in salivary zinc concentration, signiﬁcantly affecting the

density of bacillary papillae and taste acuity [29].

The cascading effects of the above molecular networks are manifested at the mac-

robehavioral level in signiﬁcant sensory phenotypic differentiation. PROP hypersensitive

individuals have a 40–60% lower aversion threshold to glucosinolate metabolites from

cruciferous plants, and this bitter taste avoidance behavior is negatively correlated with

vegetable intake, which may affect the composition of the gut ﬂora and the risk of metabolic

diseases [

]. Carriers of the sweet taste receptor variant had a 15–20% elevated threshold

of pleasurable response to sucrose, but showed hypersensitivity to artiﬁcial sweeteners,

a perceptual difference that led to a preference for sugar-substituted foods, but may trigger

an energy compensation effect [

]. Notably, the oral microbiome is involved in taste

modulation through metabolic interventions; for example, Streptococcus spp. can reduce

nitrate to nitrite and alter the activity of ion channels in the tongue epithelium, whereas

certain strains of Clostridium spp. can catabolize sulphur-containing amino acids to generate

volatile hydrogen sulphide, which indirectly enhances the activation efﬁciency of bitter

taste receptors [32].

The industry has developed precision solutions based on this. The food industry

uses Genome-wide association studies (GWAS) to screen SNP loci associated with taste

and design graded formulations for different haplotype groups, for example, developing

glucosinase inhibitors to reduce the bitter taste of vegetables for individuals carrying the

TAS2R38 mutation [

]. In the pharmaceutical ﬁeld, the use of nanoliposome encapsulation

to mask the bitter components of drugs such as ibuprofen has been designed based on

the simulation of the kinetics of hTAS2R10 receptor activation. The nutrition industry

has integrated multi-omics to establish a taste-metabolism association model, designing

Brain Sci. 2025,15, 1317 6 of 25

low-sugar, high-umami dietary plans for individuals at high risk of diabetes. This approach

leverages the high sensitivity of the umami receptors T1R1/T1R3 to monosodium glutamate

to compensate for the sensory satisfaction of taste (Figure 1C).

Cutting-edge research is currently making breakthroughs from multiple dimensions,

challenging existing scientiﬁc paradigms. Based on AlphaFold2, the structural prediction

of taste receptors combined with molecular dynamics simulations has successfully elu-

cidated the allosteric binding mechanism of sweeteners with the T1R2/T1R3 receptors,

guiding the design of innovative sweet molecules that selectively activate speciﬁc receptor

subtypes [

]. In the ﬁeld of neuroregulation, studies have found that transcranial direct

current stimulation (tDCS) can temporarily enhance the response of the insular cortex

to umami signals, increasing umami intensity perception by more than 30%, providing

a new strategy for the intervention of anorexia nervosa [

]. In synthetic biology, efforts

are underway to engineer oral symbiotic bacteria to express bitter receptor antagonistic

peptides or sweet-enhancing proteins, achieving dynamic regulation of taste phenotypes

through microbiome-host interactions [

]. These groundbreaking advancements not only

deepen the theoretical framework of taste biology but also signal the industrial prospects

of personalized sensory experience customization, driving the food, medical, and health

industries into a new era of precision intervention.

Figure 1. (A) Different sensitivity taste maps of the tongue [

]. (B) Amiloride-sensitive pathway in

taste receptor cells [

]. (C) Taste perception triad: anterior nasal cavity, posterior nasal cavity, and

taste pathway: a. gustatory pathway, b. orthonasal pathway, c. retronasal pathway [37].

3. Technical Characteristics and Sensory Research of EEG

3.1. Basic Principles of EEG

Electroencephalography (EEG) is a non-invasive technique for recording the electrical

activity of cortical neurons in the brain, with its core principles spanning neurophysiology

and biomedical engineering [

]. In 1929, German psychiatrist Hans Berger successfully

captured brain electrical signals on the human scalp for the ﬁrst time—this discovery not

only conﬁrmed the observability of brain electrical activity but also marked the beginning

of exploring brain functions via electrophysiological methods.

Over nearly a century of development, EEG technology has evolved from initial

single-channel recordings to a multimodal neuroimaging tool with millisecond-level tem-

poral resolution, playing an irreplaceable role in clinical neurology, cognitive science, and

Brain Sci. 2025,15, 1317 7 of 25

brain–computer

interfaces. Its cortical electrical activity originates from the coordinated

work of approximately 14 billion neurons: when neurons transmit information via synapses,

the opening of ion channels on the postsynaptic membrane triggers the spatiotemporal

integration of local potentials [

]. Among these, pyramidal cells, due to the directional

consistency of their dendritic arrangement, are the primary contributors to EEG signals [

The postsynaptic potentials generated by the ﬁring of such cell populations are transmit-

ted through the skull-scalp medium by volume conduction, ultimately forming a weak

electric ﬁeld on the scalp surface with an amplitude of 5–100 microvolts. According to

the quasi-static electromagnetic ﬁeld theory, the propagation of these electrical signals

follows a simpliﬁed form of Maxwell’s equations, with their attenuation closely related

to the conductive properties of cerebrospinal ﬂuid, skull, and scalp. The high electrical

resistance of the skull (approximately 0.0042 S/m) results in a signal attenuation of more

than 90%.

Modern EEG adopts the international 10–20 electrode standard [

], using

19–256

sil-

ver chloride electrodes (3 cm

density) to cover the whole brain, based on precise pro-

portional division of cranial anatomical landmarks (Figure 2A). For signal acquisition: an

instrumentation ampliﬁer with CMRR > 110 dB eliminates environmental interference;

0.5–35

Hz bandpass ﬁlter removes baseline drift and EMG noise; microvolt-level signals

are digitized via a 24-bit analog-to-digital converter.

Raw EEG often has physiological artifacts (e.g., 100–200

V eye movement signals [

])

and environmental interference (e.g., 50 Hz power noise), which require noise reduction

via ICA or adaptive notch ﬁltering [43].

3.2. EEG Signal Characteristics and Measurements

EEG signal analysis centers on understanding its physical properties and physio-

logical signiﬁcance, with frequency features being key to distinguishing neural activity

states (Figure 2B). Delta waves (0.5–4 Hz, deep sleep) reﬂect cortex-thalamus synchronized

ﬁring; theta waves (4–8 Hz, light sleep/meditation) link to hippocampal memory [

Alpha waves (8–12 Hz) are most prominent during relaxed wakefulness with closed eyes,

especially over the occipital lobe, and immediately diminish upon eye opening, a phe-

nomenon referred to as “Alpha block” [

]. Beta waves (13–30 Hz), active in the frontal

lobe and motor cortex, indicate increased cognitive engagement and are observed during

higher-order processing tasks. Gamma waves (30–100 Hz), involving coordinated activity

across multiple brain regions, serve as neural markers for perceptual integration [

]. Due

to their small amplitude (typically < 10

V), precise detection of gamma waves requires

high-quality electrodes and ampliﬁers.

In terms of signal amplitude, normal EEG activity typically ranges between 10–100

However, during epileptic seizures, the amplitude can escalate to millivolt levels, reﬂecting

pathological neural activity [

]. Neural ﬁring synchronization directly correlates with

EEG amplitude—greater synchronization enhances electrical ﬁeld summation. Precise

EEG measurement requires proper electrode selection: silver/silver chloride (Ag/AgCl)

electrodes (stable ~0.222 V potential) are the gold standard, with impedance needing to be

<5 k

Ω

to ensure signal ﬁdelity. For the international 10–20 electrode system, inter-electrode

distance is ~10% of head circumference, enabling cross-laboratory data comparability.

Additionally, 256-channel high-density EEG improves spatial resolution to ~5 mm, though

this remains lower than fMRI’s millimeter-level precision [49].

A complete EEG system employs a three-stage signal processing chain: the front-end

ampliﬁer requires a common-mode rejection ratio (CMRR) of over 100 dB to eliminate

environmental interference, with a typical input impedance of 1 G

Ω

; analog-to-digital

conversion (ADC) uses 24-bit chips and a sampling rate of 200–1000 Hz for high-resolution

Brain Sci. 2025,15, 1317 8 of 25

signal capture [

]. And in subsequent signal processing, a 0.5 Hz high-pass ﬁlter

is ﬁrst used to remove baseline drift, followed by a 50/60 Hz notch ﬁlter to suppress

power-line interference.

In EEG signal analysis, time-domain analysis often utilizes ERPs, with components

such as P300 being particularly informative [

]. Changes in the latency of P300 are

indicative of cognitive dysfunction. Frequency-domain analysis, on the other hand, uses

Fourier transform to calculate the power spectral density of EEG signals, providing insights

into brain activity across different frequency bands [

]. In recent years, time-frequency

analysis methods, such as wavelet transforms, have gained prominence for capturing

non-stationary features in EEG, which is crucial for real-time monitoring and dynamic

brain activity analysis.

Despite signiﬁcant progress in EEG signal analysis, several challenges remain in prac-

tical applications. For instance, EMG artifacts in the 20–300 Hz range overlap with the

gamma band, requiring surface electromyography (sEMG) reference signals for effective

artifact removal [

]. Movement artifacts are particularly pronounced in mobile EEG sys-

tems, with the overlap between movement-induced interference and brainwave frequencies

posing a signiﬁcant challenge [

]. Recent solutions have included motion compensation

algorithms assisted by inertial measurement units (IMUs) to adjust for movement-related

artifacts and enhance signal quality [56].

In clinical diagnostics, automated epileptiform discharge detection has over 90% sen-

sitivity, though false positives remain a key issue. Brain–computer interface (BCI) systems

require even higher signal quality—typical SSVEP-based BCI systems achieve an infor-

mation transmission rate of 60 bits/min [

]. Yet, as application demands grow, further

system performance improvements are needed.

Future directions in EEG research include the development of ﬂexible dry electrode

technology, wireless data acquisition systems, and integration with other modalities such

as functional near-infrared spectroscopy (fNIRS) [

]. These advancements are poised

to propel EEG technology from laboratory settings to everyday monitoring applications,

opening up new opportunities in remote health monitoring, smart homes, and brain

health management.

3.3. EEG Indicators Applicable to Food Sensory Research

In sensory food research, EEG provides objective evidence for understanding con-

sumers’ neural cognitive responses to food. It captures the brain’s electrophysiological

reactions when participants taste different foods. These responses reﬂect true preferences,

reveal hidden biases and deep sensory mechanisms, and offer a more accurate, objective

sensory evaluation tool than subjective self-reports.

When tasting food, the brain produces speciﬁc electrophysiological responses in

both temporal and frequency domains (Figure 2C). Within 100–300 ms of taste stimulus

presentation, early ERP components (N1, P1) emerge ﬁrst, closely tied to rapid processing

in the primary taste cortex [

]. Studies show sweet stimuli often increase P1 amplitude,

while bitter stimuli enhance N1; these differences reﬂect the brain’s ability to distinguish

taste qualities and their associated neural responses, providing key neurophysiological

data on how taste stimuli quickly trigger brain perception.

As the tasting process continues, the P300 component typically emerges within the

300–500

millisecond time window, which is closely associated with the allocation of cog-

nitive resources. In blind taste tests, foods preferred by participants tend to evoke larger

P300 amplitudes, suggesting that the brain allocates more cognitive resources to these

stimuli [60]

. This ﬁnding not only provides evidence of the neural basis for food prefer-

ences but also highlights the critical role of attention in the sensory evaluation process.

Brain Sci. 2025,15, 1317 9 of 25

Furthermore, the late LPP (Late Positive Potential) component typically emerging

around 700 ms is closely tied to emotional experience intensity. Pleasant food experiences

usually boost LPP amplitude, mirroring emotional responses to food; this component is

widely used in emotion research to clarify how foods trigger emotional resonance and

shape consumer choices [61].

Meanwhile, when tasting experience contradicts expectations (e.g., a low-sugar drink

tasting less sweet than anticipated), a distinct FRN (Feedback Related Negativity) compo-

nent appears within 200–350 ms. As a negative wave reﬂecting the brain’s rapid evaluation

of prediction errors, FRN not only reveals how the brain processes expectation-actual

experience discrepancies but also provides neurophysiological evidence for studying ex-

pectation’s role in food selection [62].

Complex taste perception, such as that elicited by multi-component dishes, is not

a mere summation of basic taste modalities (sweet, sour, salty, bitter, umami) followed

by generalization, but a dynamic neural integration process. EEG studies have revealed

that while basic tastes activate relatively distinct cortical regions (e.g., insula, orbitofrontal

cortex), complex taste stimuli trigger synergistic activation of these regions plus additional

neural networks involved in sensory integration, memory, and context processing. For

instance, the interaction between sweet and umami in savory dishes modulates theta

and gamma band oscillations in the insula, reﬂecting non-linear neural encoding beyond

simple additive processing. This integration enables the perception of unique “gustatory

gestalts” (e.g., the umami-rich complexity of broth or the balanced sweetness-sourness of

fruit sauces) that cannot be replicated by individual basic tastes. Such ﬁndings highlight

the sophistication of complex taste perception and underscore the value of EEG’s high

temporal resolution in decoding the dynamic neural mechanisms underlying this process.

In frequency domain analysis, the activity of the Alpha band is signiﬁcantly associated

with emotional states. Research shows that when participants experience pleasant taste

stimuli, particularly in the prefrontal cortex, the power of Alpha waves in the left hemi-

sphere signiﬁcantly decreases [

]. This asymmetrical pattern is considered a neural marker

of positive emotional responses. Conversely, unpleasant tastes may lead to an increase

in Alpha waves, reﬂecting the neural mechanisms of negative emotional responses [

These ﬁndings provide strong evidence for the complex relationship between emotion and

taste perception.

Gamma band activity is also associated with higher-order cognitive processing [

Stronger gamma oscillations occur when tasting foods with complex or innovative ﬂavors,

reﬂecting the brain’s integration of novel, complex sensory inputs. Such ﬂavor complexity

and novelty drive the brain to allocate extra cognitive resources, boosting gamma activity,

this offers key insights for exploring ﬂavor complexity’s role in food development and

consumer preference research [66].

Beta band activity also matters for sensory food experiences. Studies show tasting

foods with rich ﬂavor layers signiﬁcantly enhances beta waves, signaling the brain’s need

to mobilize more cognitive resources to process complex sensory information. This un-

derscores how food complexity affects cognitive resource allocation, providing theoretical

support for sensory complexity research in food design [67].

Meanwhile, an increase in Theta waves may reﬂect cognitive conﬂict during decision-

making processes, particularly in situations involving the dilemma between health and

taste [

]. For example, when consumers face the trade-off between healthiness and

tastiness, an increase in Theta wave activity may indicate the brain’s processing of the

conﬂict between emotional and rational decisions.

By integrating these temporal and frequency EEG markers, researchers can build more

comprehensive neurocognitive models to predict actual consumer choice behavior. The

Brain Sci. 2025,15, 1317 10 of 25

current research trend is to develop more convenient EEG acquisition systems, such as

wireless dry electrode devices, which make it possible to conduct research in real-world

consumption environments [

]. At the same time, the introduction of machine learning

methods has greatly improved the efﬁciency of EEG signal analysis, allowing for more

accurate classiﬁcation of consumer preferences and behavior patterns.

In the future, the integration of multimodal neuroimaging technologies, including EEG

and other brain imaging techniques such as fMRI and near-infrared spectroscopy (NIRS),

will further deepen our understanding of food sensory experiences. This multidimensional

approach will provide more scientiﬁc and systematic evidence for the food industry’s

product development, consumer behavior prediction, and marketing strategies, thereby

promoting innovation and development in the food sector.

Figure 2. (A) Equivalent circuit model of electrode-skin interface for different electrodes [

]. (B) Inte-

grative framework of EEG technology from electrode placement to diverse applications [

]. (C) Time-

domain level information of the brain to the different taste stimuli [72].

4. Application of EEG in Taste Research

4.1. Analysis of ERPs Induced by Taste Stimuli

ERPs represent ﬁnely timed neural responses to sensory, cognitive, or motor events,

captured through EEG. These voltage ﬂuctuations provide millisecond-level resolution,

making them invaluable for dissecting the temporal dynamics of brain processes. Unlike

other neuroimaging techniques, such as fMRI or PET scans, ERPs offer exceptional temporal

precision, allowing researchers to track brain activity as it unfolds in real time [

]. This

temporal accuracy is crucial for understanding the brain’s processing of complex stimuli,

such as taste, which involves rapid, dynamic neural interactions. ERPs are characterized

by their latency (the time after stimulus onset), amplitude (signal strength), and polarity

(positive or negative deﬂection). P1, N1, P2, and P300 reﬂect different stages of cognitive

processing and provide key insights into the neural mechanisms underlying perception,

attention, and memory (Table 1) [74].

Table 1. Analysis of ERPs induced by taste stimuli.

Components Taste References

ERPs P1, P3 Sweet Fluid [75]

P1, N1, P2, P3 Orange and Apple/Blackcurrant [76]

P1, N1 Sour, Salty and Metallic Tastes [10]

P1, N1 Strawberry and Lily [77]

P1, N2 Yogurt [78]

P1, P3 Green-pea Puree, Salty Solution, Evian Water [79]

P1, N1 Sweet, Sour [80]

P1, N1, P2, N2 Caffeine [81]

Brain Sci. 2025,15, 1317 11 of 25

Table 1. Cont.

Components Taste References

P1, N1, P3 Alcohol [82]

N1, P2, LPP Brownie, Cheeseburger, Cucumber, Rice [83]

N2, P3 High-caloric and Low-caloric [84]

P3, LPP High-caloric and Low-caloric [85]

The application of ERPs to gustatory stimuli offers unique insights into taste percep-

tion, a process inherently involving multisensory integration. Flavor arises not only from

taste but also from the interplay of smell, texture, and even visual cues. This makes the

study of gustatory perception more complex than other sensory modalities such as vision

or hearing, where sensory processing tends to be more isolated [

]. Gustatory perception

involves a wide range of neural and cognitive processes, from basic sensory detection to

higher-order evaluations of pleasure or aversion. The ERP method provides an effective

means of dissecting these processes in detail, capturing the temporal dynamics of how taste

is perceived and evaluated in real time [74].

Gustatory ERP research requires specialized methodologies due to the nature of taste

stimuli. Unlike visual or auditory stimuli, which are easily presented through images or

sounds, taste stimuli must be delivered using precise delivery systems to ensure consistent

timing and volume. Gustatometers are devices designed for this purpose, allowing for

the controlled administration of taste stimuli [

]. These devices are capable of delivering

a wide range of taste qualities, including sweet, salty, sour, bitter, and umami, while

also regulating the concentration of each stimulus [

]. The precision of gustatometers is

essential for maintaining the consistency of experimental conditions, which is crucial when

studying the neural responses to taste.

One commonly used experimental paradigm in gustatory ERP research is the oddball

paradigm, in which a rare taste stimulus (e.g., sweet) is presented among more frequent ones

(e.g., salty) [

]. This design allows researchers to examine how the brain processes

unexpected or novel stimuli. Oddball paradigms are effective for studying attention and

cognitive processing because they elicit a strong response from the brain, particularly in

components like the P300, which is associated with attention and memory updating [

In addition to the oddball paradigm, researchers often incorporate rinsing with water

between trials to prevent sensory adaptation. This is important because repeated exposure

to the same stimulus can lead to a decrease in the brain’s response to that stimulus, which

could confound the results.

Gustatory ERPs unfold in a predictable sequence, with each phase reﬂecting different

neural operations. The early components, occurring within 200 milliseconds of stimulus

presentation, are thought to correspond to the initial sensory encoding of the taste. These

components, including the P1 (50–100 ms) and N1 (100–150 ms), originate in the primary

taste cortex, which includes regions such as the insula and the frontal operculum [

]. The

P1 and N1 components reﬂect the brain’s early detection and categorization of taste stimuli.

The N1, in particular, is sensitive to the categorization of basic tastes, such as sweet, salty,

or bitter, and is thought to reﬂect the brain’s initial classiﬁcation of the stimulus.

As the ERP response progresses, mid-latency components, occurring between 200

and 400 milliseconds, become more prominent. These components, including the P2, are

thought to correspond to the hedonic evaluation of the taste. The P2 component is partic-

ularly sensitive to the emotional or evaluative aspect of taste, with pleasant tastes (such

as sweetness) eliciting larger P2 amplitudes than aversive tastes (such as bitterness) [

This suggests that the P2 component may be linked to reward processing in the brain,

particularly in areas such as the OFC, which plays a key role in the evaluation of rewards

Brain Sci. 2025,15, 1317 12 of 25

and the hedonic aspects of sensory experiences [

]. Thus, the P2 component provides

important information about the subjective experience of taste, as it reﬂects how the brain

assigns value to a particular taste stimulus.

Later components of the ERP, including the P300 (300–600 ms) and LPP, reflect higher-

order cognitive processes such as attention, memory updating, and subjective pleasantness

judgments. The P300 component, in particular, is associated with attentional processes and

cognitive evaluation [

]. It is often used as a marker for assessing the brain’s response to

unexpected or novel stimuli, making it particularly useful in oddball paradigms. The LPP,

which occurs later in the ERP response, is thought to reflect more sustained processing, such as

emotional or evaluative judgments, and may be linked to conscious awareness of taste [

Several factors modulate gustatory ERP responses, making it essential to consider

various aspects of stimulus delivery and individual differences. Stimulus properties, such

as concentration and taste quality, have a direct impact on both amplitude and latency of

ERP components. For example, bitter stimuli tend to evoke larger N1 and P2 responses

due to their innate aversiveness [

]. This heightened response is thought to reﬂect the

brain’s increased sensitivity to potentially harmful or unpleasant tastes. On the other hand,

higher concentrations of taste stimuli generally amplify all ERP components, as the brain’s

response to the stimulus is intensiﬁed with increased sensory input.

In addition to stimulus properties, individual differences also play a crucial role

in shaping gustatory ERP responses. Genetic variations, such as those seen in super-

tasters, individuals with heightened taste sensitivity, are associated with enhanced P2

amplitudes [96]

. Supertasters have more taste buds and may experience ﬂavors more

intensely than non-supertasters. This heightened sensitivity is reﬂected in the larger P2

responses to pleasant tastes, such as sweetness [

]. Age-related changes in taste processing

also inﬂuence ERP responses, with older individuals often showing reduced P3 responses

to taste stimuli. This may reﬂect developmental shifts in taste preference, as aging can lead

to changes in the neural systems that govern taste perception [99].

Clinically, gustatory ERPs have proven to be a valuable tool for studying neurological

and psychiatric conditions. For example, individuals with anorexia or obesity often exhibit

altered P3 responses to sweet stimuli, suggesting dysregulated reward processing [

100

The P3 component, which is related to the brain’s evaluation of stimuli and reward, can

be used to assess how these individuals process hedonic aspects of taste. In Alzheimer’s

disease, attenuated P1/N1 components may signal early gustatory dysfunction, as these

individuals may have difﬁculty detecting and categorizing tastes [

101

]. By examining

these ERP components, researchers can gain insight into the neural underpinnings of taste

perception in various clinical populations.

Beyond clinical applications, the food industry has also recognized the potential of

gustatory ERPs for predicting consumer preferences. Changes in ERP amplitude, partic-

ularly in components like the P2 and P300, have been shown to correlate with hedonic

evaluations of food products [

]. By measuring brain responses to novel food stimuli, the

food industry can gain valuable insights into consumer preferences and develop products

that are more likely to be well-received by the public. This has practical implications for

product development and marketing strategies.

4.2. Power Spectrum Analysis of Taste-Related Frequency Band Characteristics

In the context of gustatory processing, power spectrum analysis of oscillatory brain

activity provides valuable insights into the neural dynamics underlying taste perception.

Different frequency bands such as delta, theta, alpha, beta, and gamma oscillations are

thought to play distinct roles in various aspects of gustatory processing, including reward

evaluation, sensory discrimination, and multisensory integration [

]. These oscillations

Brain Sci. 2025,15, 1317 13 of 25

reﬂect the brain’s neural activity during taste experiences, offering a detailed temporal and

spatial map of how the brain processes and responds to taste stimuli. Each frequency band

has a unique contribution, and power spectrum analysis is instrumental in understanding

these contributions (Figure 3).

Figure 3. Different brainwave frequency ranges and their functions.

4.2.1. Delta and Theta Oscillations in Gustatory Processing

Delta (1–4 Hz) and theta (4–8 Hz) oscillations have been particularly associated with

reward processing and the hedonic evaluation of tastes [

102

]. Theta oscillations, in par-

ticular, are prominent in the frontal regions of the brain when participants are exposed to

pleasant taste stimuli, such as sweet solutions. Several studies have shown that pleasant

tastes evoke an increase in theta power, especially in regions like the anterior cingulate

cortex and the OFC, both key nodes in the brain’s reward network. These areas are deeply

involved in emotional processing, reward evaluation, and decision-making [

103

]. The

enhancement in theta oscillations in these areas during pleasant taste experiences indicates

their role in linking sensory input with emotional responses, particularly the perceived

reward value of the taste.

The synchronization of theta oscillations between these frontal regions has been found

to correlate with subjective ratings of pleasantness. This suggests that theta-band activity

may serve as a neural signature of ﬂavor reward value, encoding the subjective experience

of taste pleasure. Moreover, theta oscillations are linked to cognitive processes related

to attention and memory, further underscoring their role in the integration of sensory

information with higher-order evaluative processes [104].

In contrast to the well-documented association of theta oscillations with positive taste

experiences, delta waves (typically observed in deep sleep) are less directly implicated

in gustatory processing. However, delta oscillations may still contribute to fundamental

neural processes related to baseline or resting brain activity, particularly in the early stages

of sensory processing [105].

4.2.2. Alpha Oscillations and Taste Evaluation

Alpha oscillations (8–12 Hz), traditionally associated with inhibitory processes and

relaxation states, display a more complex and context-dependent pattern in gustatory

Brain Sci. 2025,15, 1317 14 of 25

research. In some studies, alpha power suppression has been observed in sensory areas

during taste stimulation, analogous to the alpha desynchronization observed in visual

processing during the presentation of stimuli [

106

]. This suppression is thought to reﬂect

a decrease in cortical inhibition as the brain processes the sensory input from taste stimuli.

However, other studies have reported enhanced alpha activity, particularly in frontal

regions, during tasks that involve taste evaluation or discrimination [

107

]. This enhance-

ment is thought to reﬂect top-down control mechanisms related to taste processing, such as

the active discrimination of taste qualities or hedonic judgment [

108

]. The apparent con-

tradiction between these ﬁndings may stem from the different functional roles of sensory

versus frontal alpha oscillations. Sensory alpha suppression may facilitate the encoding

of sensory input, while frontal alpha enhancement may support higher-level cognitive

processes, such as decision-making and evaluation [109].

The variability in alpha oscillations in gustatory processing is also inﬂuenced by

methodological differences across studies. These differences include variations in taste

delivery methods, experimental paradigms, and task demands, all of which can affect the

alpha response. Therefore, it is essential to carefully consider the context and experimental

design when interpreting the role of alpha oscillations in taste processing.

4.2.3. Beta Oscillations and Taste Quality Discrimination

Beta oscillations (13–30 Hz) in the gustatory system are strongly linked to taste quality

discrimination. Studies consistently show that basic tastes (sweet, salty, sour, bitter) trigger

distinct beta power modulation patterns over the brain’s central and frontal regions [

110

Such speciﬁcity makes beta waves promising for developing EEG-based taste classiﬁ-

cation algorithms, which could help identify and distinguish taste qualities to deepen

understanding of taste perception.

Beta oscillations are also sensitive to task demands and cognitive load during taste

processing. Research ﬁnds beta activity increases when participants actively discriminate

between tastes, suggesting it reﬂects the brain’s engagement in taste categorization and ac-

tive processing of taste quality. Additionally, beta power changes during ﬂavor expectation

and imagery, indicating it contributes not only to real taste experiences but also to taste

anticipation [

111

]. These ﬁndings highlight beta oscillations’ role in both immediate taste

sensation and cognitive processes of ﬂavor prediction and evaluation.

4.2.4. Gamma Oscillations and Multisensory Integration

Gamma oscillations (30–100 Hz), representing fast, synchronous neural ﬁring, have

been strongly associated with the multisensory integration of taste with other sensory

modalities, particularly olfaction [

112

]. When combined with olfactory or tactile inputs,

taste perception becomes more complex and uniﬁed, forming a coherent ﬂavor percept.

Gamma band activity is thought to play a key role in binding sensory information from

different modalities to create a uniﬁed perceptual experience of ﬂavor [113,114].

Studies using natural food stimuli, as opposed to simple taste solutions, consistently

report enhanced gamma power over sensory integration areas during ﬂavor perception.

These ﬁndings suggest that gamma oscillations are integral to the brain’s ability to integrate

taste, smell, and texture information, creating a uniﬁed ﬂavor experience. The timing and

spatial distribution of gamma responses provide further evidence that these oscillations

are involved in the rapid and dynamic integration of multisensory information, enabling

the brain to generate a coherent perception of ﬂavor [115].

Gamma oscillations have also been linked to attention and the conscious processing of

ﬂavor. Their role in multisensory integration suggests that gamma activity is crucial for

the brain to process and combine information from various sensory modalities, ultimately

Brain Sci. 2025,15, 1317 15 of 25

allowing us to perceive ﬂavor as a complex and integrated sensory experience [

116

]. Addi-

tionally, research has shown that the phase synchronization of gamma oscillations across

different sensory areas may facilitate cross-modal binding, allowing the brain to efﬁciently

process and interpret multisensory stimuli.

4.2.5. Methodological Challenges and Advances in Power Spectrum Analysis

Power spectrum analysis of taste-related brain activity faces unique methodological

challenges. Unlike visual or auditory stimuli, taste sensations develop slowly and persist

longer in the oral cavity—this temporal trait requires careful design of analysis time

windows, with most studies using extended epochs (5–10 s) to capture the full evolution of

taste-related oscillatory patterns. Such long windows are essential for tracking the onset,

peak, and offset of oscillations corresponding to different taste processing stages [72].

Baseline correction is another key step in gustatory studies. Orofacial movements (e.g.,

swallowing, tongue movements) introduce notable artifacts into EEG signals, which may

contaminate oscillation analysis. To address this, researchers often use pre-stimulus base-

lines or inter-trial intervals for signal normalization, enabling more accurate measurement

of taste-related oscillatory power. These preprocessing steps help isolate genuine neural

activity from movement artifacts, ensuring observed oscillations reﬂect the brain’s response

to taste stimuli.

4.2.6. Individual Differences in Taste-Related Oscillations

Individual differences in taste-related oscillatory patterns have become an impor-

tant area of research [

]. Genetic variations in taste sensitivity, such as PROP (6-n-

propylthiouracil) taster status, have been shown to correlate with distinct power spectrum

proﬁles, particularly in the theta and gamma bands [

117

]. Supertasters, individuals with

heightened taste sensitivity, may exhibit enhanced theta or gamma power in response to

certain tastes, reﬂecting their heightened sensory and hedonic sensitivity [118].

Age-related differences in taste processing are also evident: children exhibit less

differentiated frequency responses to tastes than adults. These ﬁndings suggest oscillatory

biomarkers could eventually assess taste function across the lifespan, shedding light on

how taste perception evolves with age [119].

In conclusion, power spectrum analysis of oscillatory brain activity offers a powerful

tool for understanding the complex neural mechanisms of gustatory perception. Examining

the frequency bands involved in taste processing lets researchers reveal key insights into the

temporal dynamics of ﬂavor perception, multisensory integration, and reward processing.

4.3. Brain Network Analysis and Taste Perception

Brain network analysis is a pivotal tool in contemporary neuroscience, enabling re-

searchers to explore how distinct brain regions interact and function together. This method-

ology provides invaluable insights into how the brain coordinates complex processes,

including sensory perception, cognitive functions, and emotional responses [

120

]. Gusta-

tory perception, which refers to how the brain interprets taste stimuli, is a complex process

that requires the coordinated activation of several brain regions. Recent advancements

in brain network analysis, particularly in functional connectivity and network dynamics,

have signiﬁcantly enriched our understanding of how the brain processes taste information

and how different regions collaborate to generate the perception of ﬂavor [103].

4.3.1. Gustatory Perception and Brain Network Dynamics

Gustatory perception extends far beyond the activation of the primary gustatory

cortex, involving the integration of sensory inputs from multiple brain regions (Figure 4A).

Key areas involved in gustatory processing include the primary gustatory cortex (located

Brain Sci. 2025,15, 1317 16 of 25

in the insula), the thalamus, the OFC, the amygdala, and the prefrontal cortex [

121

]. These

regions contribute to various aspects of taste processing, such as the recognition of taste

quality, emotional responses to taste, and the cognitive evaluation of food desirability.

Functional connectivity in the brain refers to the temporal correlation of neural activ-

ities across different regions. During gustatory processing, the communication between

these regions is essential for the perception of taste. Brain network analysis has revealed

that the brain regions involved in gustatory perception form a complex functional network,

with dynamic interactions occurring between sensory and higher-order cognitive areas [

122

These interactions are crucial for understanding how we experience taste not just as a sensory

phenomenon but also as a multisensory, emotional, and cognitive experience.

4.3.2. The Role of the Gustatory Network in Taste Processing

The gustatory network is an integrated system that encompasses several stages of

processing [

123

]. Initial taste information is processed in the gustatory cortex, but higher-

order regions, such as the OFC and amygdala, play essential roles in evaluating the hedonic

value of tastes. The interaction between sensory and reward-related regions allows taste to

be experienced not only as a sensation but also as a subjective, emotionally charged event.

Recent studies employing fMRI and EEG have identiﬁed speciﬁc patterns of connec-

tivity between these brain regions at various stages of taste processing (Figure 4B). For

example, sensory processing areas in the insula are activated during the initial detection of

taste, while the OFC and amygdala contribute to evaluating the hedonic value of the taste,

helping to determine whether it is pleasurable or aversive [124].

Additionally, the prefrontal cortex is involved in the cognitive processing of taste,

such as anticipating the ﬂavor of food or making decisions about food choices [

125

]. These

ﬁndings suggest that gustatory processing is an active and dynamic interaction between

various brain regions, reﬂecting both sensory and cognitive dimensions of taste.

4.3.3. Brain Network Topology and Gustatory Perception

Brain network topology refers to the organization and connectivity of brain regions,

quantiﬁable via graph-theoretical methods. Network parameters such as node degree,

clustering coefﬁcient, and small-world properties are used to assess the efﬁciency and

organization of brain networks. Studies show the gustatory network is structured to

facilitate efﬁcient communication between sensory areas and higher-order regions [126].

For example, research has demonstrated that individuals with stronger connectivity

between the gustatory cortex and the OFC tend to report higher hedonic ratings for sweet

foods. This suggests that the functional connection between these regions plays a role in the

emotional evaluation of taste (Figure 4C). The OFC, known for its involvement in reward

processing, plays a pivotal role in modulating the subjective pleasantness of taste, further

highlighting the complexity of the brain’s gustatory network [103].

Recent advances in mapping structural and functional brain networks have greatly

advanced understanding of individual differences in taste perception. Structural brain

imaging reveals that variability in gustatory network organization explains differences in

taste sensitivity, preference, and food choices [

125

]. Those with a more efﬁcient gustatory

network marked by stronger connectivity between the gustatory cortex and reward-related

regions tend to be more sensitive to taste stimuli and derive greater pleasure from food.

4.3.4. Applications of Brain Network Analysis in Gustatory Research

Recent advances in the integration of brain network analysis, BCI technologies [

and artiﬁcial intelligence (AI) have signiﬁcantly contributed to the understanding of taste

perception. FMRI [

127

] and EEG have enabled detailed mapping of the dynamic inter-

actions between sensory and cognitive brain regions during gustatory processing. Brain

Brain Sci. 2025,15, 1317 17 of 25

network analysis reveals how taste perception involves complex functional connectivity

among the gustatory cortex, reward systems, and higher-order cognitive areas. These brain

regions dynamically reorganize based on sensory input and expectations. The real-time

monitoring capability of BCI systems facilitates the modulation of taste and sensory re-

habilitation by tracking neural responses [

103

]. Additionally, AI and deep learning

techniques have enhanced the classiﬁcation and prediction of individual taste preferences

by analyzing brain connectivity patterns, offering insights into personalized nutrition and

the diagnosis of taste disorders (Figure 4D) [128,129].

These technologies pave the way for optimizing food experiences and addressing

taste-related issues. By leveraging multimodal neuroimaging and computational models,

researchers can now explore how individual differences in brain network efﬁciency affect

taste sensitivity and food-related decision-making. This multidisciplinary approach repre-

sents a transformative step in gustatory research, enabling more precise, predictive, and

personalized interventions in taste perception and health optimization.

4.3.5. Future Directions in Brain Network Analysis and Gustatory Perception

While signiﬁcant progress has been made in understanding the brain networks in-

volved in gustatory perception, several challenges remain. One of the primary challenges

is the need for more advanced and standardized methods for analyzing brain networks in

the context of taste [

130

131

]. The use of EEG, fMRI, and other neuroimaging techniques

in gustatory research is still evolving, and there is much to be learned about how best to

capture and interpret brain network dynamics during taste processing [67,127].

Another challenge lies in understanding the role of individual differences in gustatory

brain networks. While brain network analysis has provided valuable insights into how the

brain processes taste information, further research is required to determine how factors such

as genetics, age, and sensory sensitivity inﬂuence the structure and function of gustatory

networks [132].

Finally, there is a need for more research into the neural mechanisms underlying taste

disorders, such as ageusia (loss of taste) or dysgeusia (distorted taste) [

]. Understanding

how brain networks are altered in these conditions could lead to new therapeutic targets

for treating taste-related disorders.

Figure 4. (A) Main regions functionally connected to the OFC, including limbic, premotor, sensory and

other prefrontal areas [

133

]. (B) Dynamic causal modeling specification. Ins: insula, Amy: amygdala,

Brain Sci. 2025,15, 1317 18 of 25

NAc: nucleus accumbens, V1: primary visual cortex [

134

]. (C) Interaction between the gustatory

cortex and OFC under different stimuli [

135

]. (D) Multiple cognitive functions and brain-inspired AI

models integrated in BrainCog, along with their related brain areas and neural circuits [136].

5. Discussion

This review synthesizes 139 studies to delineate the relationship between electroen-

cephalography (EEG) and gustatory perception. Core ﬁndings conﬁrm that EEG indices,

including event-related potentials (ERPs), frequency bands, and brain connectivity, cor-

relate with taste perception, reﬂecting sequential neural processes: early ERPs (P1, N1)

support basic taste detection, mid-latency P2 mediates hedonic evaluation, and late compo-

nents (P300, LPP) underpin higher-order cognition. Frequency bands exhibit specialized

roles: theta for reward processing, alpha for emotional modulation, beta for taste quality

discrimination, and gamma for multisensory integration. Conclusive causal evidence

remains scarce; preliminary clues from tDCS/TMS or longitudinal studies suggest poten-

tial regulatory effects of speciﬁc brain regions (e.g., insula, orbitofrontal cortex) and EEG

indices on taste perception, but these ﬁndings are exploratory due to small sample sizes

and limited replication.

Key limitations and heterogeneity constrain the generalizability of these results.

Methodologically, EEG’s low spatial resolution and inconsistent use of the 10 to 20 electrode

system (only 6 of 15 relevant studies adopted it) hinder cross-study comparability of pari-

etal beta band results, while taste stimulation lacks standardization, 37% of studies failed to

control confounders such as temperature or texture. Sample biases are prominent: 56% of

studies included fewer than 50 participants, and 78% focused exclusively on healthy young

adults, excluding children, the elderly, and individuals with taste disorders. Additionally,

29% of studies overinterpreted correlations as causality, for example framing “theta band

power correlation with sweetness pleasantness” as “theta activity enhances sweetness

pleasure.” Heterogeneity primarily stems from non-standard EEG technical parameters

(e.g., variable P300 time windows: 300–500 ms vs. 350–650 ms) and inconsistent taste

stimulus protocols (gustatometer vs. manual delivery).

Critical misconceptions further complicate interpretation, particularly the conﬂation of

correlation with causation. One common issue is treating ERP changes as direct drivers of

taste perception rather than reﬂections of neural processing. Validating causal relationships

requires targeted study designs, such as cross-lagged models to analyze temporal predictive

links between EEG and taste indices, or combined neuroregulation-EEG recording (e.g.,

tDCS/TMS paired with real-time EEG to track neural-perceptual dynamics).

To advance the ﬁeld, a three-dimensional prospective framework addresses unmet

needs: (1) Basic mechanisms: Clarify neural circuits of taste processing and cross-modal

integration, with a key gap being the dynamic mechanisms of taste memory; (2) Tech-

nological innovation: Optimize EEG decoding algorithms (e.g., deep learning for taste

classiﬁcation) and develop low-cost dry electrodes to overcome barriers to real-world appli-

cation; (3) Clinical translation: Establish EEG diagnostic standards for taste disorders (e.g.,

ageusia) and validate BCI-based rehabilitation tools, with current gaps including the lack

of standardized thresholds for clinical use. Priority unexploited areas include exploring

EEG correlates of taste abnormalities in special populations (e.g., autism, diabetes) and

integrating EEG with fMRI or salivary metabolomics for spatiotemporal and multi-omic

taste mapping. Short-term efforts should focus on small-scale exploratory studies in under-

represented populations, while long-term goals involve translating validated EEG tools

into precision nutrition and clinical practice.

Brain Sci. 2025,15, 1317 19 of 25

6. Conclusions and Future Work

This review outlines recent advancements in EEG technology for studying the neural

mechanisms underlying gustatory perception. EEG, with its millisecond-level temporal res-

olution, offers unique advantages in uncovering the dynamic neural processes involved in

taste. This review integrates the physiological basis of taste, the inﬂuence of environmental

and psychological factors, and individual differences, emphasizing the complex neurobio-

logical nature of gustatory experiences. Key brain regions, including the insular gustatory

cortex, OFC, and anterior cingulate cortex, collaborate in processing taste, with EEG captur-

ing features such as ERPs, frequency domain oscillations, and brain network connectivity.

Theta and alpha oscillations are linked to sensory processing and decision-making, while

functional connectivity analysis reveals the critical role of the insula-prefrontal network.

The future development of EEG sensory evaluation must simultaneously address

core challenges and explore key directions: On the challenge front, it requires resolving

methodological limitations (insufﬁcient timing precision of taste instruments causing

signal-stimulus desynchronization, oral motor artifacts contaminating critical frequency

bands, baseline interference from salivary secretion, and reduced ecological validity due

to discrepancies between laboratory taste agents and real food ﬂavors), inter-individual

variability (genetic polymorphisms, age-related neural mechanism alterations, metabolic

state-induced EEG response heterogeneity), and research design/interpretation issues

(reliance on standardized core protocols for reproducibility, adequate sample sizes for

stable effect sizes, and extension to real-world dietary scenarios to enhance practical value).

To address these challenges and ﬁeld demands, future research should focus on: specialized

investigations (cross-cultural studies revealing dietary cultural differences, developmental

research analyzing changes in taste neural mechanisms), and clinical/cognitive applications

(taste EEG biomarkers for early diagnosis of neurological disorders, exploring emotion and

executive function regulation of taste perception). Leveraging computational neuroscience

will advance personalized taste neural decoding while incorporating social-environmental

factors to construct a comprehensive theoretical framework. Ultimately, this will provide

foundational support for food and nutrition innovation.

Author Contributions: L.Y.: Writing—review and editing, Writing—original draft, Methodology,

Data curation. C.Z.: Writing—original draft. W.W.: Writing—review and editing, Methodology. J.X.:

Writing—Supervision, Methodology, Conceptualization. Z.D.: Writing—review and editing, Supervi-

sion, Methodology, Conceptualization, Funding acquisition, Supervision, Project administration. All

authors have read and agreed to the published version of the manuscript.

Funding: This research was funded by the Shanghai Municipal Education Commission (AI for

Science program) and The APC was funded by the Shanghai Municipal Education Commission (AI

for Science program).

Data Availability Statement: No new data were created or analyzed in this study. Data sharing is

not applicable to this article.

Acknowledgments: Thanks to all authors for their contributions to this article.

Conﬂicts of Interest: The authors declare no conﬂicts of interest.

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