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Guo Y, Xia X, Shi Y, Ying Y, Men H. Olfactory EEG induced by odor: Used for food identification and pleasure analysis. Food Chem 2024; 455:139816. [PMID: 38816280 DOI: 10.1016/j.foodchem.2024.139816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 05/13/2024] [Accepted: 05/22/2024] [Indexed: 06/01/2024]
Abstract
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.
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Affiliation(s)
- Yuchen Guo
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Research Team, Northeast Electric Power University, Jilin 132012, China.
| | - Xiuxin Xia
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Research Team, Northeast Electric Power University, Jilin 132012, China.
| | - Yan Shi
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Research Team, Northeast Electric Power University, Jilin 132012, China
| | - Yuxiang Ying
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China
| | - Hong Men
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China.
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Morozova M, Bikbavova A, Bulanov V, Lebedev MA. An olfactory-based Brain-Computer Interface: electroencephalography changes during odor perception and discrimination. Front Behav Neurosci 2023; 17:1122849. [PMID: 37397128 PMCID: PMC10309181 DOI: 10.3389/fnbeh.2023.1122849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 06/01/2023] [Indexed: 07/04/2023] Open
Abstract
Brain-Computer Interfaces (BCIs) are devices designed for establishing communication between the central nervous system and a computer. The communication can occur through different sensory modalities, and most commonly visual and auditory modalities are used. Here we propose that BCIs can be expanded by the incorporation of olfaction and discuss the potential applications of such olfactory BCIs. To substantiate this idea, we present results from two olfactory tasks: one that required attentive perception of odors without any overt report, and the second one where participants discriminated consecutively presented odors. In these experiments, EEG recordings were conducted in healthy participants while they performed the tasks guided by computer-generated verbal instructions. We emphasize the importance of relating EEG modulations to the breath cycle to improve the performance of an olfactory-based BCI. Furthermore, theta-activity could be used for olfactory-BCI decoding. In our experiments, we observed modulations of theta activity over the frontal EEG leads approximately 2 s after the inhalation of an odor. Overall, frontal theta rhythms and other types of EEG activity could be incorporated in the olfactory-based BCIs which utilize odors either as inputs or outputs. These BCIs could improve olfactory training required for conditions like anosmia and hyposmia, and mild cognitive impairment.
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Affiliation(s)
- Marina Morozova
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Alsu Bikbavova
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
| | | | - Mikhail A. Lebedev
- Faculty of Mechanics and Mathematics, Moscow State University, Moscow, Russia
- Laboratory of Neurotechnology, I. M. Sechenov Institute of Evolutionary Physiology and Biochemistry, Saint-Petersburg, Russia
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Ninenko I, Kleeva DF, Bukreev N, Lebedev MA. An experimental paradigm for studying EEG correlates of olfactory discrimination. Front Hum Neurosci 2023; 17:1117801. [PMID: 37305363 PMCID: PMC10248234 DOI: 10.3389/fnhum.2023.1117801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 04/18/2023] [Indexed: 06/13/2023] Open
Abstract
Electroencephalography (EEG) correlates of olfaction are of fundamental and practical interest for many reasons. In the field of neural technologies, olfactory-based brain-computer interfaces (BCIs) represent an approach that could be useful for neurorehabilitation of anosmia, dysosmia and hyposmia. While the idea of a BCI that decodes neural responses to different odors and/or enables odor-based neurofeedback is appealing, the results of previous EEG investigations into the olfactory domain are rather inconsistent, particularly when non-primary processing of olfactory signals is concerned. Here we developed an experimental paradigm where EEG recordings are conducted while a participant executes an olfaction-based instructed-delay task. We utilized an olfactory display and a sensor of respiration to deliver odors in a strictly controlled fashion. We showed that with this approach spatial and spectral EEG properties could be analyzed to assess neural processing of olfactory stimuli and their conversion into a motor response. We conclude that EEG recordings are suitable for detecting active processing of odors. As such they could be integrated in a BCI that strives to rehabilitate olfactory disabilities or uses odors for hedonistic purposes.
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Affiliation(s)
- Ivan Ninenko
- Institute for Cognitive Neuroscience, HSE University, Moscow, Russia
- V. Zelman Center for Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Daria F. Kleeva
- V. Zelman Center for Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, Moscow, Russia
| | | | - Mikhail A. Lebedev
- Faculty of Mechanics and Mathematics, Lomonosov Moscow State University, Moscow, Russia
- Sechenov Institute of Evolutionary Physiology and Biochemistry of the Russian Academy of Sciences, Saint Petersburg, Russia
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Deng X, Wang Z, Liu K, Xiang X. A GAN model encoded by CapsEEGNet for visual EEG encoding and image reproduction. J Neurosci Methods 2023; 384:109747. [PMID: 36427669 DOI: 10.1016/j.jneumeth.2022.109747] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 10/27/2022] [Accepted: 11/11/2022] [Indexed: 11/25/2022]
Abstract
In last few decades, reading the human mind is an innovative topic in scientific research. Recent studies in neuroscience indicate that it is possible to decode the signals of the human brain based on the neuroimaging data. The work in this paper explores the possibility of building an end-to-end BCI system to learn and visualize the brain thoughts evoked by the stimulating images. To achieve this goal, it designs an experiment to collect the EEG signals evoked by randomly presented images. Based on these data, this work analyzes and compares the classification abilities by several improved methods, including the Transformer, CapsNet and the ensemble strategies. After obtaining the optimal method to be the encoder, this paper proposes a distribution-to-distribution mapping network to transform an encoded latent feature vector into a prior image feature vector. To visualize the brain thoughts, a pretrained IC-GAN model is used to receive these image feature vectors and generate images. Extensive experiments are carried out and the results show that the proposed method can effectively deal with the small sample data original from the less electrode channels. By examining the generated images coming from the EEG signals, it verifies that the proposed model is capable of reproducing the images seen by human eyes to some extent.
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Affiliation(s)
- Xin Deng
- Department of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 40065, China.
| | - Zhongyin Wang
- Department of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 40065, China.
| | - Ke Liu
- Department of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 40065, China.
| | - Xiaohong Xiang
- Department of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 40065, China.
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Hou HR, Han RX, Zhang XN, Meng QH. Pleasantness Recognition Induced by Different Odor Concentrations Using Olfactory Electroencephalogram Signals. SENSORS (BASEL, SWITZERLAND) 2022; 22:8808. [PMID: 36433405 PMCID: PMC9695492 DOI: 10.3390/s22228808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/08/2022] [Accepted: 11/11/2022] [Indexed: 06/16/2023]
Abstract
Olfactory-induced emotion plays an important role in communication, decision-making, multimedia, and disorder treatment. Using electroencephalogram (EEG) technology, this paper focuses on (1) exploring the possibility of recognizing pleasantness induced by different concentrations of odors, (2) finding the EEG rhythm wave that is most suitable for the recognition of different odor concentrations, (3) analyzing recognition accuracies with concentration changes, and (4) selecting a suitable classifier for this classification task. To explore these issues, first, emotions induced by five different concentrations of rose or rotten odors are divided into five kinds of pleasantness by averaging subjective evaluation scores. Then, the power spectral density features of EEG signals and support vector machine (SVM) are used for classification tasks. Classification results on the EEG signals collected from 13 participants show that for pleasantness recognition induced by pleasant or disgusting odor concentrations, considerable average classification accuracies of 93.5% or 92.2% are obtained, respectively. The results indicate that (1) using EEG technology, pleasantness recognition induced by different odor concentrations is possible; (2) gamma frequency band outperformed other EEG rhythm-based frequency bands in terms of classification accuracy, and as the maximum frequency of the EEG spectrum increases, the pleasantness classification accuracy gradually increases; (3) for both rose and rotten odors, the highest concentration obtains the best classification accuracy, followed by the lowest concentration.
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Affiliation(s)
- Hui-Rang Hou
- Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin 300350, China
| | - Rui-Xue Han
- Tianjin Navigation Instruments Research Institute, Tianjin 300131, China
| | - Xiao-Nei Zhang
- Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin 300350, China
| | - Qing-Hao Meng
- Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin 300350, China
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Signal analysis and classification of a novel active brain-computer interface based on four-category sequential coding. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Zhang X, Meng QH, Zeng M. A novel channel selection scheme for olfactory EEG signal classification on Riemannian manifolds. J Neural Eng 2022; 19. [PMID: 35732136 DOI: 10.1088/1741-2552/ac7b4a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 06/22/2022] [Indexed: 11/12/2022]
Abstract
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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Affiliation(s)
- Xiaonei Zhang
- Tianjin University, School of Electrical and Information Engineering, Tianjin, 300072, CHINA
| | - Qing-Hao Meng
- Tianjin University, School of Electrical and Information Engineering, Tianjin, 300072, CHINA
| | - Ming Zeng
- Tianjin University, School of Electrical and Information Engineering, Tianjin, 300072, CHINA
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Assessment and Scientific Progresses in the Analysis of Olfactory Evoked Potentials. Bioengineering (Basel) 2022; 9:bioengineering9060252. [PMID: 35735495 PMCID: PMC9219708 DOI: 10.3390/bioengineering9060252] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 05/27/2022] [Accepted: 06/09/2022] [Indexed: 12/25/2022] Open
Abstract
The human sense of smell is important for many vital functions, but with the current state of the art, there is a lack of objective and non-invasive methods for smell disorder diagnostics. In recent years, increasing attention is being paid to olfactory event-related potentials (OERPs) of the brain, as a viable tool for the objective assessment of olfactory dysfunctions. The aim of this review is to describe the main features of OERPs signals, the most widely used recording and processing techniques, and the scientific progress and relevance in the use of OERPs in many important application fields. In particular, the innovative role of OERPs is exploited in olfactory disorders that can influence emotions and personality or can be potential indicators of the onset or progression of neurological disorders. For all these reasons, this review presents and analyzes the latest scientific results and future challenges in the use of OERPs signals as an attractive solution for the objective monitoring technique of olfactory disorders.
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