Curvature Manipulation Of The Spectrum Of Valence-Arousal-Related Fmri Dataset Using Gaussian-Shaped Fast Fourier Transform And Its Application To Fuzzy Kansei Adjectives Modeling

Keywords

Adaptive Neuro Fuzzy Inference System; Gaussian-shaped Fast Fourier Transform; KANSEI adjectives; Power Spectrum Density; Valence-Arousal

Abstract

Valence-Arousal is regarded as a reflection of KANSEI adjectives, which is the core concept in the theory of emotional dimensions for brain recognition. This paper presents a novel method for determining the characteristics of Valence-Arousal-based timing signals using Power Spectrum Density (PSD) of fMRI images, and Gaussian filtering, segmenting, and Gaussian-shaped Fast Fourier Transform (FFT) will be applied for reprocessing fMRI images; the timing characteristics of the fMRI image signals were extracted under short-term emotional picture stimuli (within 6. s). To reduce the computational complexity, a cubic curve fitting method was used to smooth the Valence-Arousal timing curve, and the coefficients of the fitted curve, the mean, and the standard deviation were derived from the Gaussian-shaped Affective Norm English Words (ANEW) system, subsequently, these parameters were selected to create a 4-INPUT 2-OUTPUT Takagi-Sugeno (T-S) type Adaptive Neuro Fuzzy Inference System (ANFIS). In the experimental study, an fMRI data-set was acquired for KANSEI-"kindness" picture stimuli and the FIS prediction was 0.05 less than the Root Mean Square Error (RMSE) after 24/18 iteration epochs for Valence/Arousal. These experiments showed that the proposed method effectively simplified high complexity when calculating fMRI images. The cubic curve fitting method extracted the characteristics of the Valence-Arousal time series-based curves effectively and established the KANSEI adjective content more accurately by comparing with the ANEW system of Valence-Arousal values. The proposed curve generation methods for the Valence-Arousal response of KANSEI adjectives will be a potential application for attention-oriented product design fields.

Publication Date

1-22-2016

Publication Title

Neurocomputing

Volume

174

Number of Pages

1049-1059

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.neucom.2015.10.025

Socpus ID

84949638739 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84949638739

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