Electroencephalogram alterations during perception of pleasant and unpleasant odors

Yazdani, Amirmehdi; Kroupi, Eleni; Vesin, Jean‐Marc; Ebrahimi, Touradj

doi:10.1109/qomex.2012.6263860

Cited by 30 publications

(13 citation statements)

References 20 publications

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“…From the experimental data, in presence of stimulus it is seen that alpha and beta band power significantly rise with time, whereas, delta and theta band power have almost oscillatory changes. This observation proves the validation of the previous studies [31], reporting the fluctuations of alpha and beta bands for left-right motor imagery. Depending upon this study, a band pass filter with pass band frequencies of 8-30Hz is designed and the signal is filtered using this filter to remove noise and other artifacts.…”

Section: B3 Frequency Band Selectionsupporting

confidence: 91%

Adaptive Parameterized AdaBoost Algorithm with application in EEG Motor Imagery Classification

Das

Sadhu

Konar

et al. 2015

2015 International Joint Conference on Neural Networks (IJCNN)

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Among different machine learning algorithms AdaBoost is a classification technique, which improves the classification accuracy by increasing the weights of the misclassified data. To overcome the problem of misclassification in Real AdaBoost algorithm, of the already classified samples, concept of margin is employed in the Parameterized AdaBoost algorithm. The new parameter, introduced in Parameterized AdaBoost, corresponding to the margin is chosen randomly between 0 to 1. However, the margin value is different for different classification problem. Hence, in this paper, the parameter corresponding to the margin is adapted by learning the parameter value with the help of Differential Evolutionary algorithm corresponding to the optimal classification accuracy. Experiment for the support of the proposed Adaptive Parameterized Ada Boost Algorithm has been conducted with different standard database given by VCI Machine Learning Repository. In addition, an application of Adaptive Parameterized AdaBoost is performed in EEG Motor Imagery Classification. Finally the EEG Motor Imagery Classified data (Left/Right) is tested in a robot.

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Section: B3 Frequency Band Selectionsupporting

confidence: 91%

Adaptive Parameterized AdaBoost Algorithm with application in EEG Motor Imagery Classification

Das

Sadhu

Konar

et al. 2015

2015 International Joint Conference on Neural Networks (IJCNN)

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“…[10][11][12][13] Furthermore, there is unilateral right frontal activation during perception of unpleasant odor. [14] It seems that peppermint odor was perceived as a pleasant odor in participantt with this finding (asymmetry) leading to greater activation of the left hemisphere.…”

Section: Discussionmentioning

confidence: 93%

Odor-induced modulation of electroencephalogram waves in healthy controls

Ojha¹,

Singh²,

Thacker³

2017

Natl J Physiol Pharm Pharmacol

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Background: It has been found in some of the electrophysiological studies that odor does affect the rhythm of brain waves. However, there is a lot of inconsistency in terms of the reported findings. Some have reported an increase in alpha activity, whereas others observed increase in theta activity and no change in alpha. Aims and Objectives: The purpose of this study is to observe what changes occur in electroencephalogram (EEG) in healthy participants on administration of two types of odor (lavender and peppermint). Materials and Methods: This study was conducted on 25 healthy, right-handed male and female volunteers. Baseline EEG for 20 min was recorded, followed by test recording for 9 min comprising inhalation of odor 1 (lavender) for 3 min, no odor (for 3 min), and odor 2 (peppermint) for 3 min. Odors were administered through an odor delivery system, which comprised an inlet and outlet rubber tubes connected to a container for placing odoriferous substance. Results: EEG was inspected qualitatively for abnormalities in prominent locations and hemisphere. Both the odors elicited significant EEG abnormalities when compared to baseline recording, which served as control. Odor 1 and 2 both elicited diffuse slowing, focal slowing, and sharp waves mainly in temporal region and partly in centro-parietal and frontotemporal region, with more right hemispherical involvement, and increased theta rhythm. Odor 2 also produced left hemisphere dominant asymmetry. Conclusions: Both lavender and peppermint odor inhalation produces EEG abnormalities which are transient, predominantly affect right hemisphere, and temporal region. Lavender has relatively more relaxing effect on brain compared to peppermint.

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“…The testing described in [59] involved 5 participants, all right handed males between the ages of 26 and 32 years. Assessors were requested to rate odors in terms of pleasantness (unpleasant to extremely pleasant -scale 0-10) and intensity (no odor present to intolerable for intensity -scale 0-10).…”

Section: Existing State Of the Art Reviewmentioning

confidence: 99%

“…Assessors were requested to rate odors in terms of pleasantness (unpleasant to extremely pleasant -scale 0-10) and intensity (no odor present to intolerable for intensity -scale 0-10). Four scents were presented: "valerian, lotus flower, rosewater and fermented goat cheese" [59].…”

Section: Existing State Of the Art Reviewmentioning

confidence: 99%

A Tutorial for Olfaction-Based Multisensorial Media Application Design and Evaluation

et al. 2017

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Recently, multimedia researchers have added several so called new media to the traditional multimedia components (e.g. olfaction, haptic and gustation). Evaluating multimedia user perceived Quality of Experience (QoE) is already non-trivial and the addition of multisensorial media components increases this challenge. No standardized methodology exists to conduct subjective quality assessments of multisensorial media applications. To date researchers have employed different aspects of audiovisual standards to assess user QoE of multisensorial media applications and thus, a fragmented approach exists. In this paper, the authors highlight issues researchers face from numerous perspectives including applicability (or lack of) existing audiovisual standards to evaluate user QoE and lack of result comparability due to varying approaches, specific requirements of olfactory-based multisensorial media applications, and novelty associated with these applications. Finally, based on the diverse approaches in the literature and the collective experience of authors, this paper provides a tutorial and recommendations on the key steps to conduct olfactory-based multisensorial media QoE evaluation.• Information system ➝ Database management system ➝ Information systems applications ➝ Multimedia information systems.

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Electroencephalogram alterations during perception of pleasant and unpleasant odors

Cited by 30 publications

References 20 publications

Adaptive Parameterized AdaBoost Algorithm with application in EEG Motor Imagery Classification

Adaptive Parameterized AdaBoost Algorithm with application in EEG Motor Imagery Classification

Odor-induced modulation of electroencephalogram waves in healthy controls

A Tutorial for Olfaction-Based Multisensorial Media Application Design and Evaluation

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