EEG-Based Classification of Olfactory Response to Pleasant Stimuli

Abbasi, Nida Itrat; Bose, Rohit; Bezerianos, Anastasios; Thakor, Nitish V.; Dragomir, Andrei

doi:10.1109/embc.2019.8857673

Cited by 19 publications

(21 citation statements)

References 24 publications

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“…The scope of previous research lacks the distinct combination of (a) comparing pleasantness levels within the positive segment of the hedonic spectrum (i.e., high vs. low pleasantness as opposed to pleasant vs. unpleasant) and (b) approaching the topic from the standpoint of functional connectivity (e.g., employing the wPLI) and quantifiable graph theoretic metrics. Previous work by our lab [ 23 , 62 ] aimed at using EEG to compare within pleasantness levels (a), yet lacks the functional connectivity approach (b). To address this gap and add to the body of previously published work, we performed experiments in which participants were exposed repeatedly to exclusively pleasant olfactory stimuli, with neural responses being recorded using EEG.…”

Section: Discussionmentioning

confidence: 99%

“…Following each trial, subjects were asked to instantly rate the pleasantness and intensity of the presented stimulus on a scale of 0 to 10. The inter-trial interval was kept at 2 min and—to avoid odor masking—coffee beans were presented to the subjects after each trial, following completion of stimulus rating [ 23 ]. The behavioral questionnaire scores were then considered to identify the two fragrances with the highest and lowest pleasantness ratings, respectively.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Moreover, such studies often rely on analyzing neural activation patterns of individual brain areas [ 18 , 19 ], with a dearth of publications directly examining functional connectivity among them. The majority of previous efforts utilizing EEG [ 20 , 21 , 22 , 23 ] also lack this focus. However, deeper insights into olfactory hedonic processing can be drawn via network-based connectivity analyses that complement prior activation studies.…”

Section: Introductionmentioning

confidence: 99%

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Odor Pleasantness Modulates Functional Connectivity in the Olfactory Hedonic Processing Network

Kepler

Seet

Hamano³

et al. 2022

Brain Sciences

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Olfactory hedonic evaluation is the primary dimension of olfactory perception and thus central to our sense of smell. It involves complex interactions between brain regions associated with sensory, affective and reward processing. Despite a recent increase in interest, several aspects of olfactory hedonic evaluation remain ambiguous: uncertainty surrounds the communication between, and interaction among, brain areas during hedonic evaluation of olfactory stimuli with different levels of pleasantness, as well as the corresponding supporting oscillatory mechanisms. In our study we investigated changes in functional interactions among brain areas in response to odor stimuli using electroencephalography (EEG). To this goal, functional connectivity networks were estimated based on phase synchronization between EEG signals using the weighted phase lag index (wPLI). Graph theoretic metrics were subsequently used to quantify the resulting changes in functional connectivity of relevant brain regions involved in olfactory hedonic evaluation. Our results indicate that odor stimuli of different hedonic values evoke significantly different interaction patterns among brain regions within the olfactory cortex, as well as in the anterior cingulate and orbitofrontal cortices. Furthermore, significant hemispheric laterality effects have been observed in the prefrontal and anterior cingulate cortices, specifically in the beta ((13–30) Hz) and gamma ((30–40) Hz) frequency bands.

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Section: Discussionmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Odor Pleasantness Modulates Functional Connectivity in the Olfactory Hedonic Processing Network

Kepler

Seet

Hamano³

et al. 2022

Brain Sciences

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“…Among existing neuroimaging modalities, electroencephalogram (EEG) is the most commonly used technique to measure and record brain activity during olfactory perception due to its advantages of noninvasiveness, fine temporal resolution, and portability [6,7]. At present, various classification systems based on olfactory EEG signals have been developed to recognize odor categories [8][9][10][11], odor-induced pleasantness [5,[12][13][14][15], and odor concentrations [16][17][18]. Most previous studies adopt EEG acquisition devices with many channels or electrodes to collect olfactory EEG data [7, 9-14, 17, 18].…”

Section: Introductionmentioning

confidence: 99%

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

2022

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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 (RG) 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 (BHS) algorithm, including an opposition-based learning strategy (OBL) for generating high-quality initial population, an adaptive parameter strategy (APS) for improving search capability, and a bitwise operation strategy (BOS) 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.

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“…In the last years, several studies have been published showing how it is possible to detect hidden signs of memorization process and emotional engagement like pleasantness [ 63 , 64 , 65 , 66 ]. Such indexes have been successfully applied also for the evaluation of auditory [ 65 , 67 ], olfactory [ 32 , 68 ], and tasting [ 35 , 69 ] stimuli.…”

Section: Introductionmentioning

confidence: 99%

Forefront Users’ Experience Evaluation by Employing Together Virtual Reality and Electroencephalography: A Case Study on Cognitive Effects of Scents

et al. 2021

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Scents have the ability to affect peoples’ mental states and task performance with to different extents. It has been widely demonstrated that the lemon scent, included in most all-purpose cleaners, elicits stimulation and activation, while the lavender scent elicits relaxation and sedative effects. The present study aimed at investigating and fostering a novel approach to evaluate users’ experience with respect to scents’ effects through the joint employment of Virtual Reality and users’ neurophysiological monitoring, in particular Electroencephalography. In particular, this study, involving 42 participants, aimed to compare the effects of lemon and lavender scents on the deployment of cognitive resources during a daily life experience consisting in a train journey carried out in virtual reality. Our findings showed a significant higher request of cognitive resources during the processing of an informative message for subjects exposed to the lavender scent with respect to the lemon exposure. No differences were found between lemon and lavender conditions on the self-reported items of pleasantness and involvement; as this study demonstrated, the employment of the lavender scent preserves the quality of the customer experience to the same extent as the more widely used lemon scent.

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EEG-Based Classification of Olfactory Response to Pleasant Stimuli

Cited by 19 publications

References 24 publications

Odor Pleasantness Modulates Functional Connectivity in the Olfactory Hedonic Processing Network

Odor Pleasantness Modulates Functional Connectivity in the Olfactory Hedonic Processing Network

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

Forefront Users’ Experience Evaluation by Employing Together Virtual Reality and Electroencephalography: A Case Study on Cognitive Effects of Scents

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