Human-Centric Emotion Estimation Based on Correlation Maximization Considering Changes With Time in Visual Attention and Brain Activity

Moroto, Yuya; Maeda, Kazuo; Ogawa, Takahiro; Haseyama, Miki

doi:10.1109/access.2020.3036908

Cited by 5 publications

(3 citation statements)

References 51 publications

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“…For example, in previous studies, i.e., [ 44 , 45 , 46 ], a local Fisher discriminant analysis [ 47 ]-based approach was adopted to perform supervised dimensionality reduction on fNIRS data. However, in [ 30 ], dimensionality reductions of fNIRS data were conducted based on PCA. Since this experiment investigated whether there was a correlation between each dimension for each feature, we calculated fNIRS features

.…”

Section: Correlation Analysis Between Heterogeneous Datamentioning

confidence: 99%

“…Additionally, several studies estimated the interests of content involving human behavior [ 19 , 20 ] and personalized saliency (and its prediction) using gaze data [ 21 , 22 , 23 ]. Furthermore, the analysis of behavior, gaze data, and brain activity contribute to the solutions to several tasks, such as brain decoding [ 24 , 25 , 26 , 27 ] and certain applications [ 28 , 29 , 30 , 31 , 32 ]. Onuma et al [ 31 , 32 ] showed that human brain activity calculated from functional near-infrared spectroscopy (fNIRS) is related to the taste of food [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

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Trial Analysis of the Relationship between Taste and Biological Information Obtained While Eating Strawberries for Sensory Evaluation

Maeda

Togo

Ogawa

et al. 2022

Sensors

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This paper presents a trial analysis of the relationship between taste and biological information obtained while eating strawberries (for a sensory evaluation). This study used the visual analog scale (VAS); we collected questionnaires used in previous studies and human brain activity obtained while eating strawberries. In our analysis, we assumed that brain activity is highly correlated with taste. Then, the relationships between brain activity and other data, such as VAS and questionnaires, could be analyzed through a canonical correlation analysis, which is a multivariate analysis. Through an analysis of brain activity, the potential relationship with "taste" (that is not revealed by the initial simple correlation analysis) can be discovered. This is the main contribution of this study. In the experiments, we discovered the potential relationship between cultural factors (in the questionnaires) and taste. We also found a strong relationship between taste and individual information. In particular, the analysis of cross-loading between brain activity and individual information suggests that acidity and the sugar-to-acid ratio are related to taste.

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.…”

Section: Correlation Analysis Between Heterogeneous Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trial Analysis of the Relationship between Taste and Biological Information Obtained While Eating Strawberries for Sensory Evaluation

Maeda

Togo

Ogawa

et al. 2022

Sensors

Self Cite

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show abstract

“…Specifically, we used a matrix factorization-based approach to address missing brain activity information, confirming its efficacy in complementing missing biological data, including behavior [ 11 ] and brain [ 12 ] data. In order to identify crucial brain regions for classifying F759 and wild-type mice, we employed canonical correlation analysis, a method widely used for analyzing various brain activity information [ 13 , 14 , 15 , 16 , 17 ]. Specifically, we used supervised multiview canonical correlation analysis (sMVCCA) [ 18 ], which is capable of handling multiple types of information.…”

Section: Introductionmentioning

confidence: 99%

Trial Analysis of Brain Activity Information for the Presymptomatic Disease Detection of Rheumatoid Arthritis

Maeda,

Ogawa,

Kayama

et al. 2024

Bioengineering

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This study presents a trial analysis that uses brain activity information obtained from mice to detect rheumatoid arthritis (RA) in its presymptomatic stages. Specifically, we confirmed that F759 mice, serving as a mouse model of RA that is dependent on the inflammatory cytokine IL-6, and healthy wild-type mice can be classified on the basis of brain activity information. We clarified which brain regions are useful for the presymptomatic detection of RA. We introduced a matrix completion-based approach to handle missing brain activity information to perform the aforementioned analysis. In addition, we implemented a canonical correlation-based method capable of analyzing the relationship between various types of brain activity information. This method allowed us to accurately classify F759 and wild-type mice, thereby identifying essential features, including crucial brain regions, for the presymptomatic detection of RA. Our experiment obtained brain activity information from 15 F759 and 10 wild-type mice and analyzed the acquired data. By employing four types of classifiers, our experimental results show that the thalamus and periaqueductal gray are effective for the classification task. Furthermore, we confirmed that classification performance was maximized when seven brain regions were used, excluding the electromyogram and nucleus accumbens.

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Human Emotion Recognition Using Multi-Modal Biological Signals Based On Time Lag-Considered Correlation Maximization

Moroto

Maeda

Ogawa

et al. 2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Human-Centric Emotion Estimation Based on Correlation Maximization Considering Changes With Time in Visual Attention and Brain Activity

Cited by 5 publications

References 51 publications

Trial Analysis of the Relationship between Taste and Biological Information Obtained While Eating Strawberries for Sensory Evaluation

Trial Analysis of the Relationship between Taste and Biological Information Obtained While Eating Strawberries for Sensory Evaluation

Trial Analysis of Brain Activity Information for the Presymptomatic Disease Detection of Rheumatoid Arthritis

Human Emotion Recognition Using Multi-Modal Biological Signals Based On Time Lag-Considered Correlation Maximization

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