Olfactory EEG Research Articles

FBANet: An Effective Data Mining Method for Food Olfactory EEG Recognition.

At present, the sensory evaluation of food mostly depends on artificial sensory evaluation and machine perception, but artificial sensory evaluation is greatly interfered with by subjective factors, and machine perception is difficult to reflect human feelings. In this article, a frequency band attention network (FBANet) for olfactory electroencephalogram (EEG) was proposed to distinguish the difference in food odor. First, the olfactory EEG evoked experiment was designed to collect the olfactory EEG, and the preprocessing of olfactory EEG, such as frequency division, was completed. Second, the FBANet consisted of frequency band feature mining and frequency band feature self-attention, in which frequency band feature mining can effectively mine multiband features of olfactory EEG with different scales, and frequency band feature self-attention can integrate the extracted multiband features and realize classification. Finally, compared with other advanced models, the performance of the FBANet was evaluated. The results show that FBANet was better than the state-of-the-art techniques. In conclusion, FBANet effectively mined the olfactory EEG data information and distinguished the differences between the eight food odors, which proposed a new idea for food sensory evaluation based on multiband olfactory EEG analysis.

Olfactory EEG induced by odor: Used for food identification and pleasure analysis

As the need for food authenticity verification increases, sensory evaluation of food odors has become widely recognized. This study presents a theory based on electroencephalography (EEG) to create an Olfactory Perception Dimensional Space (EEG-OPDS), using feature engineering and ensemble learning to establish material and emotional spaces based on odor perception and pleasure. The study examines the intrinsic connection between these two spaces and explores the mechanisms of integration and differentiation in constructing the OPDS. This method effectively visualizes various types of food odors while identifying their perceptual intensity and pleasantness. The average classification accuracy for odor recognition in an eight-category experiment is 96.1%. Conversely, the average classification accuracy for sensory pleasantness recognition in a two-category experiment is 98.8%. The theoretical approach proposed in this study, based on olfactory EEG signals to construct an OPDS, captures the subtle perceptual differences and individualized pleasantness responses to food odors.

Advancing cross-subject olfactory EEG recognition: A novel framework for collaborative multimodal learning between human-machine

Odor sensory evaluation is broadly applied in food, clothing, cosmetics, and other fields. Traditional artificial sensory evaluation has poor repeatability, and the machine olfaction represented by the electronic nose (E-nose) is difficult to reflect human feelings. Olfactory electroencephalogram (EEG) contains features associated with human olfactory preference, which has unique advantages in odor sensory evaluation. However, the difficulty of cross-subject olfactory EEG recognition dramatically limits its application. In this paper, a human–machine collaborative multimodal learning method is proposed for cross-subject olfactory preference recognition. Firstly, the olfactory EEG and E-nose multimodal data acquisition and preprocessing paradigms are established. Secondly, a complementary multimodal data mining strategy is proposed to effectively mine the individual and common features representing subjects' olfactory preferences from multimodal data. Finally, the cross-subject olfactory preference recognition is achieved in 24 subjects by fusing the extracted common and individual features, and the recognition effect is superior to the state-of-the-art recognition methods. Furthermore, the advantages of the proposed method in cross-subject olfactory preference recognition indicate its potential for practical odor evaluation applications.

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.

Olfactory response is a potential sign of consciousness: electroencephalogram findings.

This study aimed to explore whether olfactory response can be a sign of consciousness and represent higher cognitive processing in patients with disorders of consciousness (DoC) using clinical and electroencephalogram data. Twenty-eight patients with DoC [13 vegetative states (VS)/unresponsive wakefulness syndrome (UWS) and 15 minimally conscious states (MCS)] were divided into two groups: the presence of olfactory response (ORES) group and the absence of olfactory response (N-ORES) group according to behavioral signs from different odors, i.e., vanillin, decanoic acid, and blank stimuli. We recorded an olfactory task-related electroencephalogram (EEG) and analyzed the relative power and functional connectivity at the whole-brain level in patients with DoC and healthy controls (HCs). After three months, the outcomes of DoC patients were followed up using the coma recovery scale-revised (CRS-R). A significant relationship was found between olfactory responses and the level of consciousness (χ2(1) = 6.892, p = 0.020). For olfactory EEG, N-ORES patients showed higher theta functional connectivity than ORES patients after stimulation with vanillin (p = 0.029; p = 0.027). Patients with N-ORES showed lower alpha and beta relative powers than HCs at the group level (p = 0.019; p = 0.033). After three months, 62.5% (10/16) of the ORES patients recovered consciousness compared to 16.7% (2/12) in the N-ORES group. The presence of olfactory response was significantly associated with an improvement in consciousness (χ2(1) = 5.882, p = 0.023). Olfactory responses should be considered signs of consciousness. The differences in olfactory processing between DoC patients with and without olfactory responses may be a way to explore the neural correlates of olfactory consciousness in these patients. The olfactory response may help in the assessment of consciousness and may contribute to therapeutic orientation.

Recognition of odor and pleasantness based on olfactory EEG combined with functional brain network model

Recognition of odor and pleasantness based on olfactory EEG combined with functional brain network model

A novel channel selection scheme for olfactory EEG signal classification on Riemannian manifolds

Objective. The classification of olfactory-induced electroencephalogram (olfactory EEG) signals has potential applications in disease diagnosis, emotion regulation, multimedia, and so on. To achieve high-precision classification, numerous EEG channels are usually used, but this also brings problems such as information redundancy, overfitting and high computational load. Consequently, channel selection is necessary to find and use the most effective channels. Approach. In this study, we proposed a multi-strategy fusion binary harmony search (MFBHS) algorithm and combined it with the Riemannian geometry classification framework to select the optimal channel sets for olfactory EEG signal classification. MFBHS was designed by simultaneously integrating three strategies into the binary harmony search algorithm, including an opposition-based learning strategy for generating high-quality initial population, an adaptive parameter strategy for improving search capability, and a bitwise operation strategy for maintaining population diversity. It performed channel selection directly on the covariance matrix of EEG signals, and used the number of selected channels and the classification accuracy computed by a Riemannian classifier to evaluate the newly generated subset of channels. Main results. With five different classification protocols designed based on two public olfactory EEG datasets, the performance of MFBHS was evaluated and compared with some state-of-the-art algorithms. Experimental results reveal that our method can minimize the number of channels while achieving high classification accuracy compatible with using all the channels. In addition, cross-subject generalization tests of MFBHS channel selection show that subject-independent channels obtained through training can be directly used on untrained subjects without greatly compromising classification accuracy. Significance. The proposed MFBHS algorithm is a practical technique for effective use of EEG channels in olfactory recognition.

A triangular hashing learning approach for olfactory EEG signal recognition

Recognition of olfactory-induced electroencephalogram (EEG) signals can provide an effective means for the research on disorder diagnostics and human–machine interaction. A novel triangular hashing (TH) approach is proposed for EEG signal recognition. The TH approach consists of a triangular feature construction and a hash inspired coding idea, which makes effective use of the feature differences between EEG electrodes. Firstly, a triangular feature set with N layers is constructed based on power-spectral density (PSD) features extracted from N electrodes for each frequency band of each olfactory EEG sample. Subsequently, the electrode orders, i.e. the TH codes for each layer of the constructed feature set are obtained by arranging the feature values in ascending order. Finally, the prediction type of the testing sample is determined by finding the most similar TH codes between EEG types and the testing sample. Experimental results reveal that for the recognition of olfactory EEG signals acquired from eleven subjects, the proposed TH recognition approach yields the considerably high accuracy of 93.0%, significantly superior to the other eight traditional methods. Besides, the EEG dataset with 5005 samples used in this study is made public through the website presented in this paper. In this way, the proposed TH method combined with the published EEG dataset may provide new perspectives for further study in olfactory EEG research.

Recognition of Odor and Pleasantness Based on Olfactory EEG Combined with Functional Brain Network Model

Recognition of Odor and Pleasantness Based on Olfactory EEG Combined with Functional Brain Network Model

Decoding olfactory EEG signals for different odor stimuli identification using wavelet-spatial domain feature

BackgroundDecoding olfactory-induced electroencephalography (olfactory EEG) signals has gained significant attention in recent years, owing to its potential applications in several fields, such as disease diagnosis, multimedia applications, and brain-computer interaction (BCI). Extracting discriminative features from olfactory EEG signals with low spatial resolution and poor signal-to-noise ratio is vital but challenging for improving decoding accuracy. New methodsBy combining discrete wavelet transform (DWT) with one-versus-rest common spatial pattern (OVR-CSP), we develop a novel feature, named wavelet-spatial domain feature (WSDF), to decode the olfactory EEG signals. First, DWT is employed on EEG signals for multilevel wavelet decomposition. Next, the DWT coefficients obtained at a specific level are subjected to OVR-CSP for spatial filtering. Correspondingly, the variance is extracted to generate a discriminative feature set, labeled as WSDF. ResultsTo verify the effectiveness of WSDF, a classification of olfactory EEG signals was conducted on two data sets, i.e., a public EEG dataset ‘Odor Pleasantness Perception Dataset (OPPD)’, and a self-collected dataset, by using support vector machine (SVM) trained based on different cross-validation methods. Experimental results showed that on OPPD dataset, the proposed method achieved a best average accuracy of 100% and 94.47% for the eyes-open and eyes-closed conditions, respectively. Moreover, on our own dataset, the proposed method gave a highest average accuracy of 99.50%. Comparison with existing methodsCompared with a wide range of EEG features and existing works on the same dataset, our WSDF yielded superior classification performance. ConclusionsThe proposed WSDF is a promising candidate for decoding olfactory EEG signals.

A Double-Square-Based Electrode Sequence Learning Method for Odor Concentration Identification Using EEG Signals

The aim of this study is to improve the recognition performance of olfactory electroencephalogram (EEG) signals induced by different odor concentrations, which is a new challenge in the field of olfactory EEG research. To do this, a novel double-square-based electrode sequence (DSES) learning method is elaborately proposed. First, two square-based feature sets, each with N levels, are constructed based on N power-spectral-density (PSD) features extracted from N EEG electrodes. Subsequently, the electrode sequence (ES) codes are obtained by arranging the N feature values of each level of each square feature set in ascending order. Finally, inspired by the k-nearest neighbor ( k-NN), a minimum inconsistency classification is presented to find the odor concentration class whose ES codes are the least inconsistent with those of the testing sample. In the experiment, rose and rotten odors, each with five concentrations, were used to induce olfactory EEG signals on 13 participants. Experimental results reveal that, using the proposed DSES method, considerably high classification accuracies of 94.2% and 92.9% on five different concentrations of the rose and rotten odors were obtained, respectively, significantly outperforming ten other methods. These results verify the superiority of the proposed DSES method in identifying different odor concentrations. In addition, the EEG dataset with 4550 samples is made public through the website presented in this study. In this way, the proposed DSES method combined with the published EEG dataset may further promote the development of olfactory EEG recognition research.

Olfactory EEG Signal Classification Using a Trapezoid Difference-Based Electrode Sequence Hashing Approach.

Olfactory-induced electroencephalogram (EEG) signal classification is of great significance in a variety of fields, such as disorder treatment, neuroscience research, multimedia applications and brain-computer interface. In this paper, a trapezoid difference-based electrode sequence hashing method is proposed for olfactory EEG signal classification. First, an -layer trapezoid feature set whose size ratio of the top, bottom and height is 1:2:1 is constructed for each frequency band of each EEG sample. This construction is based on optimized power-spectral-density features extracted from real electrodes and nonreal electrode's features. Subsequently, the real electrodes' sequence (ES) codes of each layer of the constructed trapezoid feature set are obtained by arranging the feature values in ascending order. Finally, the nearest neighbor classification is used to find a class whose ES codes are the most similar to those of the testing sample. Thirteen-class olfactory EEG signals collected from 11 subjects are used to compare the classification performance of the proposed method with six traditional classification methods. The comparison shows that the proposed method gives average accuracy of 94.3%, Cohen's kappa value of 0.94, precision of 95.0%, and F1-measure of 94.6%, which are higher than those of the existing methods.

Neuroanthropology: Olfactory Recognition of the Self / Non-self by the Ancestral MHC: An EEG Study

Humans naturally discriminate between different ethnic group members using visual and other sensory information, including the sense of smell. The genetic diversity of humans serves as biological and social sign stimuli in the neural self/non-self recognition process. As already evident, the major histocompatibility complex (MHC) encodes both an individual self and a shared ancestral self into the body odor. In a pilot study, we examine the brain-evoked responses to ancestral encoded body odors by conducting an olfactory EEG recording of four different groups: Germans, Taiwanese, Koreans, and Chinese. Results show that humans recognize in-group members as familiar and they exhibit practically identical rhythmic EEG patterns to one’s own odor as it pertains to the ancestral in-group odors. This reveals their genetic relatedness. It is hereby demonstrated that shared ethnicity results in behaviors (both group-protective and pro-social) that are important to intra- and inter-group dynamics.

A field-theoretic approach to understanding scale-free neocortical dynamics

A mesoscopic field-theoretic approach is compared with neural network and brain imaging approaches to understanding brain dynamics. Analysis of high spatiotemporal resolution rabbit electroencephalogram (EEG) reveals neural fields in the form of spatial patterns in amplitude (AM) and phase (PM) modulation of gamma and beta carrier waves that serve to classify EEGs from trials with differing conditioned stimuli (CS+/-). Paleocortex exemplified by olfactory EEG has one AM-PM pattern at a time that forms by an input-dependent phase transition. Neocortex shows multiple overlapping AM-PM patterns before and during presentation of CSs. Modeling suggests that neocortex is stabilized in a scale-free state of self-organized criticality, enabling cooperative domains to form virtually instantaneously by phase transitions ranging in size from a few hypercolumns to an entire hemisphere. Self-organized local domains precede formation of global domains that supervene and contribute global modulations to local domains. This mechanism is proposed to explain Gestalt formation in perception.

Correlations between unit firing and EEG in the rat olfactory system

The olfactory EEG of awake animals displaus oscillatory bursts of activity in the γ-(30–100 Hz) range. The bursts are correlated with inflow of air over the receptor layer in the nose. None of the inputs to the cortices that display these oscillations carries periodic signals in the γ-range. Thus these bursts are generated locally, either by neuronal feedback interactions or by coupling of oscillatory neurons. In the first case if the oscillations are generated by negative feedback, then two classes of cells must exist: excitatory neurons and inhibitory neurons with the same frequency of oscillation but with a quarter cycle phase lag by the inhibitory cells from the excitatory cells. On the other hand, if the EEG's result from coupling of cells that are intrinsically oscillatory, there should be a broad but monomodal distribution of phase values. In order to determine the origin of these bursts, we performed simultaneous recordings of EEG and multi-unit spikes in the 4 parts of the olfactory system (olfactory bulb, anterior olfactory nucleus, prepyriform cortex and lateral entorhinal area) of awake and motivated rats. For each sample, the EEG and the multi-unit spikes were recorded from the same local neighborhood. The multi-unit electrode recorded pulses from the principal output neurons of the respective cortical areas. In all locations tested, the oscillations in pulse probabilities of firing were found to have the same frequency as the dominant EEG frequency. In all 4 structures two sets of cells were found. One set displayed pulses in phase with the EEG and the other set displayed pulses that led or lagged the EEG by approximately 1/4 cycle. These data confirm the negative feedback interaction model rather than the coupled oscillator model for the generation of the bursts in the olfactory system. The relevance of these findings to other cortical systems, in casu the visual cortex is discussed.

Spatial patterns of visual cortical fast EEG during conditioned reflex in a rhesus monkey

A preliminary assay was made of the existence of time-space coherence patterns of fast EEG activity in the visual cortex of a Rhesus monkey. The primary intent of the present study was to evaluate the similarities and differences in relation to the olfactory bulb, where such coherences ave been described and have been demonstrated to be associated with behavior. Segments 1.5 s in duration were recorded simultaneously without averaging from 16 of 35 subdural electrodes fixed over the left occipital lobe in an array3.6cm× 2.8cm. Each segment was taken during the delivery of a visual conditioned stimulus (CS) and the performance of a conditioned response (CR) by a well-trained Rhesus monkey. The EEGs appeared chaotic with irregular bursts lasting 75–200 ms, resembling those in the olfactory EEG but with lower peak frequencies. Fourier spectra showed broad distributions of power resembling ‘ 1/fnoise’ with multiple peaks in the range of 20–40 Hz. Time intervals were selected where coherent activity seemed to be present at a number of electrodes. A dominant component waveform that was common to all channels was extracted by principal components analysis (PCA) of each segment. The distribution of the power of this component across the electrodes (the factor loadings) was used to describe the spatial pattern of the coherent cortical activity. Statistical analyses suggested that different patterns could be associated to the CS and the CR, as has been found in the olfactory system. These patterns remained stable over a 6 week recording interval. The patterns can be better discriminated, when the factor loadings of each channel are normalized to zero mean and unit variance, to discard a basic pattern of power distribution, which may reflect anatomical and electrode positioning factors that are related to behavioral information processing by the cortex. The wide spatial distribution of the common patterns found suggests that EEG patterns that manifest differing states of the visual cortex may also be accessible with scalp electrodes.

Relation of olfactory EEG to behavior: Time series analysis.

Relation of olfactory EEG to behavior: Time series analysis.

Relation of olfactory EEG to behavior: Time series analysis.

Oscillatory electroencephalographic bursts were measured from 64 electrodes implanted on the olfactory bulbs of rabbits. Oscillatory bursts that occurred before and during presentation of odorant conditioned stimuli (CSs) were selected in brief segments. Comparisons between the 64 traces and their spectra showed that, despite amplitude differences between channels, every burst had a common waveform over the entire array. The spectra showed 2 to 5 distinct peaks in each burst. Each trace was fitted with the sum of 5 cosines to express the burst in ten 8 X 8 matrices of amplitude and phase values at its peak frequencies. Two types of burst were identified. Those with dominant frequencies greater than 55 Hz had one narrow dominant spectral peak and reproducible spatial patterns of its amplitude within subgroups of bursts relating to control and odorant CS conditions. Those with dominant frequencies less than 55 Hz were disorderly; their spectra were broad, and their spatial patterns of amplitude did not reproduce within subgroups. A behavioral assay showed that the high- and not the low-frequency bursts contained odor-specific information.

Changes in spatial patterns of rabbit olfactory EEG with conditioning to odors.

ABSTRACTElectrode arrays (8 × 8, 3.5 × 3.5 mm) were implanted epidurally on the olfactory bulbs of rabbits for EEG recording. The rabbits were trained to give a conditioned response to a warning odor paired with an electric shock. EEGs were recorded and edited, and representative ERG bursts with odor and preceding the odor were selected for measurement. Each burst was displayed in a contour map of amplitude. The contour maps revealed active EEG foci in the bulb with size, shape and location unique to each rabbit. Changes in shape and location took place only during familiarization and during training, when a warning odor was paired with the aversive stimulus. The EEG spatial patterns did not change when visual or auditory stimuli were used as CS. EEG spatial patterns did not reflect conformal mapping of odor stimulus to neural activity response, but were determined by state variables of the animal related to olfactory conditioning history. The implications for human EEG are briefly discussed.

Avoidance Reactions of Juvenile Coho Salmon (Oncorhynchus kisutch) to Monocyclic Aromatics

Presmolt coho salmon (Oncorhynchus kisutch) tested in a Y-maze avoided a mixture of monocyclic aromatic hydrocarbons at concentrations of 3–4 mg/L while smolting coho salmon avoided concentrations of less than 2 mg/L. Three components (benzene, toluene, o-xylene) of the mixture were tested individually with presmolt salmon and each was avoided at a lower concentration than when compared with the total hydrocarbon concentration of the mixture; o-xylene was most repellent, causing statistically significant avoidance at a concentration of 0.2 mg/L. Electrophysiological recordings from the olfactory bulb indicated that short-term exposure of juvenile coho salmon to monocyclic aromatic hydrocarbons at 4 mg/L did not disrupt this chemosensory modality; the olfactory system was stimulated by an aromatic hydrocarbon mixture at concentrations correlating with observed avoidance behavior.Key words: salmon, behavior, avoidance, monocyclic aromatic hydrocarbons, Oncorhynchus, olfactory EEG