Estimation of Viewed Images Using Individual and Shared Brain Responses

Higashi, Takaaki; Maeda, Kazuo; Ogawa, Takahiro; Haseyama, Miki

doi:10.1109/gcce50665.2020.9291742

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…This relationship suggests that a CNN mimics the visual perception system in the human brain, and visual features extracted by a CNN are essential in estimating visual perception. Previous studies have attempted to estimate CNN-based visual features of images using fMRI data collected while subjects see the images [8,[31][32][33][34]. In a previous study [8], the authors constructed a decoder that learns the relationship between each subject's fMRI data and the visual features of a seen image, and the decoder can estimate the visual features of the seen image from the fMRI data.…”

Section: Introductionmentioning

confidence: 99%

Brain Decoding of Multiple Subjects for Estimating Visual Information Based on a Probabilistic Generative Model

Higashi

Maeda

Ogawa

et al. 2022

Sensors

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Brain decoding is a process of decoding human cognitive contents from brain activities. However, improving the accuracy of brain decoding remains difficult due to the unique characteristics of the brain, such as the small sample size and high dimensionality of brain activities. Therefore, this paper proposes a method that effectively uses multi-subject brain activities to improve brain decoding accuracy. Specifically, we distinguish between the shared information common to multi-subject brain activities and the individual information based on each subject’s brain activities, and both types of information are used to decode human visual cognition. Both types of information are extracted as features belonging to a latent space using a probabilistic generative model. In the experiment, an publicly available dataset and five subjects were used, and the estimation accuracy was validated on the basis of a confidence score ranging from 0 to 1, and a large value indicates superiority. The proposed method achieved a confidence score of 0.867 for the best subject and an average of 0.813 for the five subjects, which was the best compared to other methods. The experimental results show that the proposed method can accurately decode visual cognition compared with other existing methods in which the shared information is not distinguished from the individual information.

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Section: Introductionmentioning

confidence: 99%

Brain Decoding of Multiple Subjects for Estimating Visual Information Based on a Probabilistic Generative Model

Higashi

Maeda

Ogawa

et al. 2022

Sensors

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“…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%

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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Question Answering for Estimation of Seen Image Contents from Multi-subject fMRI Responses

Takada

Togo

Ogawa

et al. 2020

2020 IEEE 9th Global Conference on Consumer Electronics (GCCE)

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Estimation of Viewed Images Using Individual and Shared Brain Responses

Cited by 3 publications

References 6 publications

Brain Decoding of Multiple Subjects for Estimating Visual Information Based on a Probabilistic Generative Model

Brain Decoding of Multiple Subjects for Estimating Visual Information Based on a Probabilistic Generative Model

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

Question Answering for Estimation of Seen Image Contents from Multi-subject fMRI Responses

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