Spatiotemporal dynamics of odor representations in the human brain revealed by EEG decoding

Exploring the feasibility of olfactory brain-computer interfaces

In this study, we explore the feasibility of single-trial predictions of odor registration in the brain using olfactory bio-signals. We focus on two main aspects: input data modality and the processing model. For the first time, we assess the predictability of odor registration from novel electrobulbogram (EBG) recordings, both in sensor and source space, and compare these with commonly used electroencephalogram (EEG) signals. Despite having fewer data channels, EBG shows comparable performance to EEG. We also examine whether breathing patterns contain relevant information for this task. By comparing a logistic regression classifier, which requires hand-crafted features, with an end-to-end convolutional deep neural network, we find that end-to-end approaches can be as effective as classic methods. However, due to the high dimensionality of the data, the current dataset is insufficient for either classifier to robustly differentiate odor and non-odor trials. Finally, we identify key challenges in olfactory BCIs and suggest future directions for improving odor detection systems.

What emotions are elicited by smells in Japanese people? Emotional measurement using a universal scale in Japanese

Odors can elicit emotions, and cultural differences exist in the emotions elicited. To enable cross-cultural comparisons, the Universal Emotion and Odor Scales (EOS), a psychological scale consisting of affective terms in multiple languages, has been developed and used to measure emotions. However, it does not include the Japanese language. In addition, similar surveys only examined the pleasantness or unpleasantness of odors among Japanese people. No studies examined their relationship with specific emotions. We created a Japanese version of the EOS (the Japanese version of the Geneva Emotion and Odor Scale; J-GEOS), which could be compared across cultures. In addition, we conducted an experiment to examine the relationship between odors and emotions among Japanese participants. The J-GEOS was created by translating the existing multilingual EOS into Japanese. It was also used to examine emotional attraction to 10 odors in 200 participants, which included older adults. This study showed that the J-GEOS could be used to describe emotions elicited by odors via further specific affective terms. We expect that the J-GEOS could be widely used as a comparative tool between various cultures to understand the psychological characteristics of olfaction among the Japanese.

Flavor Interactions in Wine: Current Status and Future Directions From Interdisplinary and Crossmodal Perspectives

Wine flavor complexity arises from interactions among over 1000 volatile compounds, with esters, thiols, and terpenes being key contributors. This review comprehensively examines the complex interactions among aroma compounds in wine, emphasizing their role in shaping wine flavor profiles. These interactions, occurring at the physicochemical, olfactory receptor, and neural levels, can either enhance or mask sensory perceptions, significantly influencing wine quality. Non-volatile compounds, such as polyphenols and polysaccharides, also play a critical role by modulating aroma volatility through interactions like π-π stacking and hydrophobic bonding. The review evaluates current methodologies, including sensory analysis and instrumental techniques, and emphasizes the need for advanced models to predict interaction outcomes. Notably, within a 2%-10% ethanol concentration range, "positive odor intensity" and "positive aroma persistence" increase with ethanol levels, illustrating the complex relationship between matrix components and sensory perception. Crossmodal interactions between aroma and taste further complicate flavor perception, with aromas like vanilla enhancing sweetness perception in white wines. However, research on red wines remains inconclusive, suggesting the need for further investigation. Future research should focus on real wine matrices, leveraging machine learning and interdisciplinary approaches to model complex interactions. Expanding studies to other alcoholic beverages and exploring the physiological mechanisms of crossmodal interactions could enhance flavor design and sensory optimization in the food industry, offering significant academic and practical value.

Odour-Specific Identification Impairment Is Associated With Cognitive Dysfunction in Older Adults: A Contemporary Community-Based Study

BACKGROUND

To delay or prevent the development of MCI, identifying a potential target is essential. Olfactory dysfunction has been linked to MCI. However, it remains unclear to what extent odour-specific identification impairment affects domain-specific cognition. Therefore, we aimed to investigate the association of olfactory dysfunction and odour-specific identification impairment with cognitive domains in older adults.

METHODS

In 1084 community-dwelling older adults from the Chinese Multi-Provincial Cohort Study, olfactory function was assessed using the modified Sniffin' Sticks identification test, and impaired odour identification was defined as an incorrect identification of one odour. Olfactory dysfunction was defined as three or more odours. Cognition was assessed using MOCA, comprised of six cognitive domains. MCI was defined as an education-modified MOCA score of < 26.

RESULTS

Overall, 35.6% of participants had olfactory dysfunction, and 60.1% had MCI. Participants with olfactory dysfunction had a higher risk of MCI and exhibited lower global cognitive function than those without olfactory dysfunction. Notably, impaired odour identification of fish (OR = 1.48, 95% CI: 1.03-2.13) and leather (OR = 1.45, 95% CI: 1.09-1.92) was significantly associated with the risk of MCI. Furthermore, impaired odour identification of all odours except rose was significantly associated with global cognitive function. Participants with impaired odour identification of fish and leather had significantly poorer memory than unimpaired participants.

CONCLUSIONS

Our findings demonstrated that impaired identification of specific odours increased the risk of MCI and domain-specific cognitive dysfunction, suggesting that odour identification impairment may thus be a potential target for future MCI/dementia intervention studies.

Beyond Aromas: Exploring the Development and Potential Applications of Electroencephalography in Olfactory Research-From General Scents to Food Flavor Science Frontiers

Olfaction is crucial to our dietary choices and significantly influences our emotional and cognitive landscapes. Understanding the underlying neural mechanisms is pivotal, especially through the use of electroencephalography (EEG). This technology has strong temporal resolution, allowing it to capture the dynamics of neural responses to odors, bypassing the need for subjective interpretations. The application of EEG in food flavor research is still relatively new, but it has great potential. This review begins with an examination of general scent stimulation, charts the advances in using EEG to understand odor perception, and explores its future in food flavor science. By analyzing EEG's ability to detect distinct patterns and strengths in brain activity, we can elucidate the perceptual, affective, and cognitive frameworks associated with food odors. Event-related potentials and oscillatory activities, markers of central olfactory processing, provide insights into the neural architecture of olfaction. These markers are instrumental in assessing the influence of food odors on health, emotions, and decision-making processes. We argue that EEG's application in olfaction research holds considerable promise for the food industry to innovate products that are not only healthier but also more appealing, thereby promoting human well-being.

Unveiling brain response mechanisms of citrus flavor perception: An EEG-based study on sensory and cognitive responses

Citrus flavors are globally popular in food industry, yet research on the perceptual preferences of various citrus flavors is limited. Based on the subjective sensory evaluation, this study introduces a novel sensory analysis approach, using electroencephalography (EEG), to objectively measure the sensory and cognitive responses to nine citrus flavors, including d-limonene, concentrated (H-) and original essential oils of sweet orange (SEO), bergamot EO (BEO), lemon EO (LEO), and grapefruit EO (GEO). Results revealed that δ (0.5-4 Hz) and α (8-13 Hz) waves activity predominated in brain responses to citrus flavor, with greater activity observed in frontal and central regions compared to other areas. Sniffing citrus EOs triggered more complex and dynamic electrical activity than d-limonene, indicated by higher power density across all frequency bands (0.1-30 Hz). Interestingly, while the original citrus EOs were associated with higher self-reported acceptability, the concentrated forms elicited greater brain responses. Specifically, H-SEO and L-LEO eliciting significantly greater δ and α wave activity in the prefrontal region than their original forms (P < 0.05). A preliminary correlation was observed between brain laterality in α waves power and acceptability scores of citrus flavor, with δ waves power in the prefrontal region further demonstrating an effective reflection of self-reported acceptability scores for SEO and LEO stimuli. This is the first EEG-based study to compare brain responses to different citrus flavors, providing important implications for the food industry in optimizing product formulations and enhancing consumer experiences.

Assessing the impact of artifact correction and artifact rejection on the performance of SVM-based decoding of EEG signals

Numerous studies have demonstrated that eyeblinks and other large artifacts can decrease the signal-to-noise ratio of EEG data, resulting in decreased statistical power for conventional univariate analyses. However, it is not clear whether eliminating these artifacts during preprocessing enhances the performance of multivariate pattern analysis (MVPA; decoding), especially given that artifact rejection reduces the number of trials available for training the decoder. This study aimed to evaluate the impact of artifact-minimization approaches on the decoding performance of support vector machines. Independent component analysis (ICA) was used to correct ocular artifacts, and artifact rejection was used to discard trials with large voltage deflections from other sources (e.g., muscle artifacts). We assessed decoding performance in relatively simple binary classification tasks using data from seven commonly-used event-related potential paradigms (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity), as well as more challenging multi-way decoding tasks, including stimulus location and stimulus orientation. The results indicated that the combination of artifact correction and rejection did not improve decoding performance in the vast majority of cases. However, artifact correction may still be essential to minimize artifact-related confounds that might artificially inflate decoding accuracy. Researchers who are decoding EEG data from paradigms, populations, and recording setups that are similar to those examined here may benefit from our recommendations to optimize decoding performance and avoid incorrect conclusions.

A Neural Circuit For Bergamot Essential Oil-Induced Anxiolytic Effects

Aromatic essential oils have been shown to relieve anxiety and enhance relaxation, although the neural circuits underlying these effects have remained unknown. Here, it is found that treatment with 1.0% bergamot essential oil (BEO) exerts anxiolytic-like effects through a neural circuit projecting from the anterior olfactory nucleus (AON) to the anterior cingulate cortex (ACC) in acute restraint stress model mice. Collectively, in vivo two-photon calcium imaging, viral tracing, and whole-cell patch clamp recordings show that inhalation exposure to 1.0% BEO can activate glutamatergic projections from the AON to GABAergic neurons in the ACC, which drives inhibition of local glutamatergic neurons (AONGlu→ACCGABA→Glu). Optogenetic or chemogenetic manipulation of this pathway can recapitulate or abolish the BEO-induced anxiolytic-like behavioral effects in mice with ARS. Beyond depicting a previously unrecognized pathway involved in stress response, this study provides a circuit mechanism for the effects of BEO and suggests a potential target for anxiety treatment.

Measurement and Analyses of Olfactory Event-Related Potentials

Olfactory event-related potentials (OERPs) are brain electrical activities time-locked to olfactory stimuli, detectable via scalp electrodes. They offer a noninvasive means to study cortical olfactory processing in humans. Previous research suggests that olfactory cortical processing occurring within several seconds after the onset of an odor can be tracked using OERPs. This enables the investigation of the temporal dynamics of neural activity, spanning from those associated with odor properties to subjects' states. Moreover, OERPs are influenced by diseases, including olfactory, neurological, or psychiatric conditions, and have the potential to serve as biomarkers. This chapter describes the measurement and analysis methods required to obtain OERPs, with a particular focus on odor delivery, aiming to provide a primer for those unfamiliar with OERP measurement.

The human olfactory bulb communicates perceived odor valence to the piriform cortex in the gamma band and receives a refined representation back in the beta band

A core function of the olfactory system is to determine the valence of odors. In humans, central processing of odor valence perception has been shown to take form already within the olfactory bulb (OB), but the neural mechanisms by which this important information is communicated to, and from, the olfactory cortex (piriform cortex, PC) are not known. To assess communication between the 2 nodes, we simultaneously measured odor-dependent neural activity in the OB and PC from human participants while obtaining trial-by-trial valence ratings. By doing so, we could determine when subjective valence information was communicated, what kind of information was transferred, and how the information was transferred (i.e., in which frequency band). Support vector machine (SVM) learning was used on the coherence spectrum and frequency-resolved Granger causality to identify valence-dependent differences in functional and effective connectivity between the OB and PC. We found that the OB communicates subjective odor valence to the PC in the gamma band shortly after odor onset, while the PC subsequently feeds broader valence-related information back to the OB in the beta band. Decoding accuracy was better for negative than positive valence, suggesting a focus on negative valence. Critically, we replicated these findings in an independent data set using additional odors across a larger perceived valence range. Combined, these results demonstrate that the OB and PC communicate levels of subjective odor pleasantness across multiple frequencies, at specific time points, in a direction-dependent pattern in accordance with a two-stage model of odor processing.

Odor and taste characteristics, transduction mechanism, and perceptual interaction in fermented foods: a review

Fermentation is a critical technological process for flavor development in fermented foods. The combination of odor and taste, known as flavor, is crucial in enhancing people's perception and psychology toward fermented foods, thereby increasing their acceptance among consumers. This review summarized the determination and key flavor compound screening methods in fermented foods and analyzed the flavor perception, perceptual interactions, and evaluation methods. The flavor compounds in fermented foods could be separated, purified, and identified by instrument techniques, and a molecular sensory science approach could identify the key flavor compounds. How flavor compounds bind to their respective receptors determines flavor perception, which is influenced by their perceptual interactions, including odor-odor, taste-taste, and odor-taste. Evaluation methods of flavor perception mainly include human sensory evaluation, electronic sensors and biosensors, and neuroimaging techniques. Among them, the biosensor-based evaluation methods could facilitate the investigation of the flavor transduction mechanism and the neuroimaging technique could explain the brain's signals that relate to the perception of flavor and how they compare to signals from other senses. This review aims to elucidate the flavor profile of fermented foods and highlight the significance of comprehending the interactions between various flavor compounds, thus improving the healthiness and sensory attributes.

Improving the Classification of Olfactory Brain-Computer Interface Responses by Combining EEG and EBG Signals

The sense of smell, or olfaction, can enhance brain-computer interfaces (BCIs). Different scents can be assigned to specific commands to allow users to interact with technology naturally, but challenges remain. Accurate odor delivery systems and robust algorithms for detecting and interpreting brain activity patterns are necessary. We propose combining electroencephalography (EEG) and electrobulbography (EBG) to improve classification accuracy. Our pilot study shows promising results for a new olfactory brain-computer interface (BCI) modality that combines common spatial pattern (CSP) filtration applied to EEG and EBG to classify responses to six scent stimuli in a classical oddball paradigm.

Contribution of low-level image statistics to EEG decoding of semantic content in multivariate and univariate models with feature optimization

Spatio-temporal patterns of evoked brain activity contain information that can be used to decode and categorize the semantic content of visual stimuli. However, this procedure can be biased by low-level image features independently of the semantic content present in the stimuli, prompting the need to understand the robustness of different models regarding these confounding factors. In this study, we trained machine learning models to distinguish between concepts included in the publicly available THINGS-EEG dataset using electroencephalography (EEG) data acquired during a rapid serial visual presentation paradigm. We investigated the contribution of low-level image features to decoding accuracy in a multivariate model, utilizing broadband data from all EEG channels. Additionally, we explored a univariate model obtained through data-driven feature selection applied to the spatial and frequency domains. While the univariate models exhibited better decoding accuracy, their predictions were less robust to the confounding effect of low-level image statistics. Notably, some of the models maintained their accuracy even after random replacement of the training dataset with semantically unrelated samples that presented similar low-level content. In conclusion, our findings suggest that model optimization impacts sensitivity to confounding factors, regardless of the resulting classification performance. Therefore, the choice of EEG features for semantic decoding should ideally be informed by criteria beyond classifier performance, such as the neurobiological mechanisms under study.

TASA: Temporal Attention With Spatial Autoencoder Network for Odor-Induced Emotion Classification Using EEG

The olfactory system enables humans to smell different odors, which are closely related to emotions. The high temporal resolution and non-invasiveness of Electroencephalogram (EEG) make it suitable to objectively study human preferences for odors. Effectively learning the temporal dynamics and spatial information from EEG is crucial for detecting odor-induced emotional valence. In this paper, we propose a deep learning architecture called Temporal Attention with Spatial Autoencoder Network (TASA) for predicting odor-induced emotions using EEG. TASA consists of a filter-bank layer, a spatial encoder, a time segmentation layer, a Long Short-Term Memory (LSTM) module, a multi-head self-attention (MSA) layer, and a fully connected layer. We improve upon the previous work by utilizing a two-phase learning framework, using the autoencoder module to learn the spatial information among electrodes by reconstructing the given input with a latent representation in the spatial dimension, which aims to minimize information loss compared to spatial filtering with CNN. The second improvement is inspired by the continuous nature of the olfactory process; we propose to use LSTM-MSA in TASA to capture its temporal dynamics by learning the intercorrelation among the time segments of the EEG. TASA is evaluated on an existing olfactory EEG dataset and compared with several existing deep learning architectures to demonstrate its effectiveness in predicting olfactory-triggered emotional responses. Interpretability analyses with DeepLIFT also suggest that TASA learns spatial-spectral features that are relevant to olfactory-induced emotion recognition.

Representational maps in the brain: concepts, approaches, and applications

Neural systems have evolved to process sensory stimuli in a way that allows for efficient and adaptive behavior in a complex environment. Recent technological advances enable us to investigate sensory processing in animal models by simultaneously recording the activity of large populations of neurons with single-cell resolution, yielding high-dimensional datasets. In this review, we discuss concepts and approaches for assessing the population-level representation of sensory stimuli in the form of a representational map. In such a map, not only are the identities of stimuli distinctly represented, but their relational similarity is also mapped onto the space of neuronal activity. We highlight example studies in which the structure of representational maps in the brain are estimated from recordings in humans as well as animals and compare their methodological approaches. Finally, we integrate these aspects and provide an outlook for how the concept of representational maps could be applied to various fields in basic and clinical neuroscience.

An advance in novel intelligent sensory technologies: From an implicit-tracking perspective of food perception

Food sensory evaluation mainly includes explicit and implicit measurement methods. Implicit measures of consumer perception are gaining significant attention in food sensory and consumer science as they provide effective, subconscious, objective analysis. A wide range of advanced technologies are now available for analyzing physiological and psychological responses, including facial analysis technology, neuroimaging technology, autonomic nervous system technology, and behavioral pattern measurement. However, researchers in the food field often lack systematic knowledge of these multidisciplinary technologies and struggle with interpreting their results. In order to bridge this gap, this review systematically describes the principles and highlights the applications in food sensory and consumer science of facial analysis technologies such as eye tracking, facial electromyography, and automatic facial expression analysis, as well as neuroimaging technologies like electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and functional near-infrared spectroscopy. Furthermore, we critically compare and discuss these advanced implicit techniques in the context of food sensory research and then accordingly propose prospects. Ultimately, we conclude that implicit measures should be complemented by traditional explicit measures to capture responses beyond preference. Facial analysis technologies offer a more objective reflection of sensory perception and attitudes toward food, whereas neuroimaging techniques provide valuable insight into the implicit physiological responses during food consumption. To enhance the interpretability and generalizability of implicit measurement results, further sensory studies are needed. Looking ahead, the combination of different methodological techniques in real-life situations holds promise for consumer sensory science in the field of food research.

Insights into brain perceptions of the different taste qualities and hedonic valence of food via scalp electroencephalogram

Investigating brain activity is essential for exploring taste-experience related cues. The paper aimed to explore implicit (unconscious) emotional or physiological responses related to taste experiences using scalp electroencephalogram (EEG). We performed implicit measures of tastants of differing perceptual types (bitter, salty, sour and sweet) and intensities (low, medium, and high). The results showed that subjects were partially sensitive to different sensory intensities, i.e., for high intensities, taste stimuli could induce activation of different rhythm signals in the brain, with α and θ bands possibly being more sensitive to different taste types. Furthermore, the neural representations and corresponding sensory qualities (e.g., "sweet: pleasant" or "bitter: unpleasant") of different tastes could be discriminated at 250-1,500 ms after stimulus onset, and different tastes exhibited distinct temporal dynamic differences. Source localization indicated that different taste types activate brain areas associated with emotional eating, reward processing, and motivated tendencies, etc. Overall, our findings reveal a larger sophisticated taste map that accounted for the diversity of taste types in the human brain and assesses the emotion, reward, and motivated behavior represented by different tastes. This study provided basic insights and a perceptual foundation for the relationship between taste experience-related decisions and the prediction of brain activity.

Trigeminal stimulation is required for neural representations of bimodal odor localization: A time-resolved multivariate EEG and fNIRS study

Whereas neural representations of spatial information are commonly studied in vision, olfactory stimuli might also be able to create such representations via the trigeminal system. We explored in two independent multi-method electroencephalography-functional near-infrared spectroscopy (EEG+fNIRS) experiments (n1=18, n2=14) if monorhinal odor stimuli can evoke spatial representations in the brain. We tested whether this representation depends on trigeminal properties of the stimulus, and if the retention in short-term memory follows the "sensorimotor recruitment theory", using multivariate representational similarity analysis (RSA). We demonstrate that the delta frequency band up to 5 Hz across the scull entail spatial information of which nostril has been stimulated. Delta frequencies were localized in a network involving primary and secondary olfactory, motor-sensory and occipital regions. RSA on fNIRS data showed that monorhinal stimulations evoke neuronal representations in motor-sensory regions and that this representation is kept stable beyond the time of perception. These effects were no longer valid when the odor stimulus did not sufficiently stimulate the trigeminal nerve as well. Our results are first evidence that the trigeminal system can create spatial representations of bimodal odors in the brain and that these representations follow similar principles as the other sensory systems.

Increased sensitivity to unpleasant odor following acute psychological stress

Previous studies have reported increased sensitivity to malodor after acute stress in humans. However, it is unclear whether stress-related "hypersensitivity" to odors depends on odor pleasantness. Forty participants (mean age 19.13 ± 1.14 years, 21 men and 19 women) completed a stress (Trier Social Stress Test, TSST) and a control session in randomized order. Detection threshold to three odors varying in pleasantness (pleasant: β-Citronellol; neutral: 2-Heptanol; unpleasant: 4-Methylpentanoic acid), odor discrimination, odor identification, sensitivity to trigeminal odor, and suprathreshold odor perception were assessed after participants' completion of the stress or the control tasks. Salivary cortisol, subjective stress, and heart rate were assessed throughout the experiment. After TSST, participants showed an increased sensitivity for the unpleasant odor. Moreover, there were correlations between stress-related salivary cortisol and the increased sensitivity for the unpleasant odor (r = 0.32, p = 0.05) and the neutral odor (r = 0.34, p < 0.05). Besides, salivary cortisol response was correlated to the increased odor discrimination performance (Δ stress - control) (r = 0.34, p < 0.05). The post-TSST perceived stress was correlated with decreased odor identification and decreased sensitivity to the unpleasant odor. After stress, participants rated lower pleasantness for β-Citronellol than the control condition. Overall, these results suggest the impact of acute psychological stress on odor sensitivity depends on the odor valence, and that the stress-related cortisol responses may play an important role in this effect.

The Grossberg Code: Universal Neural Network Signatures of Perceptual Experience

Two universal functional principles of Grossberg’s Adaptive Resonance Theory decipher the brain code of all biological learning and adaptive intelligence. Low-level representations of multisensory stimuli in their immediate environmental context are formed on the basis of bottom-up activation and under the control of top-down matching rules that integrate high-level, long-term traces of contextual configuration. These universal coding principles lead to the establishment of lasting brain signatures of perceptual experience in all living species, from aplysiae to primates. They are re-visited in this concept paper on the basis of examples drawn from the original code and from some of the most recent related empirical findings on contextual modulation in the brain, highlighting the potential of Grossberg’s pioneering insights and groundbreaking theoretical work for intelligent solutions in the domain of developmental and cognitive robotics.