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

Xia, Xiuxin; Liu, Xiaotong; Zheng, Wenbo; Jia, Xiaofei; Wang, Bo; Men, Hong; Men, Hong

doi:10.1007/s13042-023-01797-7

Cited by 9 publications

(3 citation statements)

References 40 publications

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“…Fortunately, advancements in science and technology have led to the miniaturization and increased flexibility of bulky and expensive equipment (Boto et al., 2018; Casson, 2019; Valliappan et al., 2020). Moreover, as machine learning becomes more integrated into food sensory and consumer studies (Lee et al., 2023), there is a growing belief that artificial intelligence techniques, in the future, will play a pivotal role in correlating data obtained from instruments and human testing, to provide consumers’ true sensory perceptions related to food products conveniently and accurately (Fuentes et al., 2021; Nunes et al., 2023; Xia et al., 2023).…”

Section: Challenges and Prospectsmentioning

confidence: 99%

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

Zhao,

Ye,

Deng

et al. 2024

Comp Rev Food Sci Food Safe

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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.

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Section: Challenges and Prospectsmentioning

confidence: 99%

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

Zhao,

Ye,

Deng

et al. 2024

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

show abstract

“…The components of the EEG acquisition system include EEG electrodes, preprocessing circuits, analog-to-digital converters (ADC), and EEG signal control and feedback [23][24][25][26]. The EEG acquisition system must ensure high accuracy and low-noise measurements while prioritizing user safety and comfort [27,28].…”

Section: Composition Of Eeg Signal Acquisition and Feedback Devicesmentioning

confidence: 99%

Application and Development of EEG Acquisition and Feedback Technology: A Review

Qin,

Zhang,

Zhang

et al. 2023

Biosensors

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This review focuses on electroencephalogram (EEG) acquisition and feedback technology and its core elements, including the composition and principles of the acquisition devices, a wide range of applications, and commonly used EEG signal classification algorithms. First, we describe the construction of EEG acquisition and feedback devices encompassing EEG electrodes, signal processing, and control and feedback systems, which collaborate to measure faint EEG signals from the scalp, convert them into interpretable data, and accomplish practical applications using control feedback systems. Subsequently, we examine the diverse applications of EEG acquisition and feedback across various domains. In the medical field, EEG signals are employed for epilepsy diagnosis, brain injury monitoring, and sleep disorder research. EEG acquisition has revealed associations between brain functionality, cognition, and emotions, providing essential insights for psychologists and neuroscientists. Brain–computer interface technology utilizes EEG signals for human–computer interaction, driving innovation in the medical, engineering, and rehabilitation domains. Finally, we introduce commonly used EEG signal classification algorithms. These classification tasks can identify different cognitive states, emotional states, brain disorders, and brain–computer interface control and promote further development and application of EEG technology. In conclusion, EEG acquisition technology can deepen the understanding of EEG signals while simultaneously promoting developments across multiple domains, such as medicine, science, and engineering.

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“…Crossover research between the medical and machine learning domains is flourishing [8][9][10]. Although there are examples of analyses using machine learning in biomedical measurements during odor exposure exist [11,12], they have not been conducted for EGGs. In this study, Copyright c Society for Science on Form, Japan.…”

Section: Introductionmentioning

confidence: 99%

Study on the Classification of Electrogastrograms Using Artificial Intelligence Analysis in Frequency Space

Takai,

Sugie,

Matsuura

et al. 2023

FORMA

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Olfaction is generally evaluated using sensory evaluation. Objective evaluation has recently been performed using electrogastrogram (EGG). In this study, EGGs were measured with and without exposure to the lavender odor, and power spectra were obtained from the frequency analysis of the EGGs. Principal component analysis (PCA) was conducted to extract dimension-compressed data in the frequency space. A linear support vector machine (SVM) was then applied to classify the EGGs into those observed in the odorless condition and those recorded with exposure to the lavender odor. Binary classification by the SVM was evaluated by the correct rate and F value that were 100% resulted from this artificial intelligence analysis for all participants.

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

Cited by 9 publications

References 40 publications

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

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

Application and Development of EEG Acquisition and Feedback Technology: A Review

Study on the Classification of Electrogastrograms Using Artificial Intelligence Analysis in Frequency Space

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