scholarly journals Decoding Three Different Preference Levels of Consumers Using Convolutional Neural Network: A Functional Near-Infrared Spectroscopy Study

2021 ◽  
Vol 14 ◽  
Author(s):  
Kunqiang Qing ◽  
Ruisen Huang ◽  
Keum-Shik Hong

This study decodes consumers' preference levels using a convolutional neural network (CNN) in neuromarketing. The classification accuracy in neuromarketing is a critical factor in evaluating the intentions of the consumers. Functional near-infrared spectroscopy (fNIRS) is utilized as a neuroimaging modality to measure the cerebral hemodynamic responses. In this study, a specific decoding structure, called CNN-based fNIRS-data analysis, was designed to achieve a high classification accuracy. Compared to other methods, the automated characteristics, constant training of the dataset, and learning efficiency of the proposed method are the main advantages. The experimental procedure required eight healthy participants (four female and four male) to view commercial advertisement videos of different durations (15, 30, and 60 s). The cerebral hemodynamic responses of the participants were measured. To compare the preference classification performances, CNN was utilized to extract the most common features, including the mean, peak, variance, kurtosis, and skewness. Considering three video durations, the average classification accuracies of 15, 30, and 60 s videos were 84.3, 87.9, and 86.4%, respectively. Among them, the classification accuracy of 87.9% for 30 s videos was the highest. The average classification accuracies of three preferences in females and males were 86.2 and 86.3%, respectively, showing no difference in each group. By comparing the classification performances in three different combinations (like vs. so-so, like vs. dislike, and so-so vs. dislike) between two groups, male participants were observed to have targeted preferences for commercial advertising, and the classification performance 88.4% between “like” vs. “dislike” out of three categories was the highest. Finally, pairwise classification performance are shown as follows: For female, 86.1% (like vs. so-so), 87.4% (like vs. dislike), 85.2% (so-so vs. dislike), and for male 85.7, 88.4, 85.1%, respectively.

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2957 ◽  
Author(s):  
Gihyoun Lee ◽  
Sang Jin ◽  
Jinung An

In this paper, a new motion artifact correction method is proposed based on multi-channel functional near-infrared spectroscopy (fNIRS) signals. Recently, wavelet transform and hemodynamic response function-based algorithms were proposed as methods of denoising and detrending fNIRS signals. However, these techniques cannot achieve impressive performance in the experimental environment with lots of movement such as gait and rehabilitation tasks because hemodynamic responses have features similar to those of motion artifacts. Moreover, it is difficult to correct motion artifacts in multi-measured fNIRS systems, which have multiple channels and different noise features in each channel. Thus, a new motion artifact correction method for multi-measured fNIRS is proposed in this study, which includes a decision algorithm to determine the most contaminated fNIRS channel based on entropy and a reconstruction algorithm to correct motion artifacts by using a wavelet-decomposed back-propagation neural network. The experimental data was achieved from six subjects and the results were analyzed in comparing conventional algorithms such as HRF smoothing, wavelet denoising, and wavelet MDL. The performance of the proposed method was proven experimentally using the graphical results of the corrected fNIRS signal, CNR that is a performance evaluation index, and the brain activation map.


2020 ◽  
Vol 4 (1) ◽  
pp. 47-52
Author(s):  
Fairuz Mohd Nasir ◽  
Hiroshi Watabe

Functional near-infrared spectroscopy (fNIRS) is an optical imaging tool to study brain activities. Moreover, many researchers combined fNIRS with other modalities to gain a better understanding of the brain. This paper provides an overview of the combination of fNIRS with other imaging modalities in the detection and measurement of the cerebral hemodynamic. Cerebral haemodynamic such as the cerebral blood flow (CBF), cerebral blood volume (CBV) and cerebral blood oxygenation (CBO) are the important parameters in many neuroimaging studies. Cerebral hemodynamic had been studied by various medical imaging modalities.  Initially, Xenon enhanced Computed Tomography (Xenon CT), Computed Tomography (CT) perfusion; Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET) are used to measure the cerebral hemodynamic. Recently, fNIRS is used to optically observe the changes in cerebral haemodynamic during brain activities and the combination of fNIRS with other modalities also become an interest to study the relations within brain activities and the cerebral hemodynamic. Therefore, this paper provides an overview of existing multimodal fNIRS in detection of cerebral haemodynamic changes and provides an important insight on how multimodal fNIRS aid in advancing modern investigations of human brain function.       Keywords: multimodal imaging, fNIRS-fMRI, fNIRS-PET, fNIRS-EEG


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