Identification of Food/Nonfood Visual Stimuli from Event-Related Brain Potentials

Abstract

Although food consumption is one of the most basic human behaviors, the factors underlying nutritional preferences are not yet clear. The use of classification algorithms can clarify the understanding of these factors. This study was aimed at measuring electrophysiological responses to food/nonfood stimuli and applying classification techniques to discriminate the responses using a single-sweep dataset. Twenty-one right-handed male athletes with body mass index (BMI) levels between 18.5% and 25% (mean age: 21.05 ± 2.5 ) participated in this study voluntarily. The participants were asked to focus on the food and nonfood images that were randomly presented on the monitor without performing any motor task, and EEG data have been collected using a 16-channel amplifier with a sampling rate of 1024 Hz. The SensoMotoric Instruments (SMI) iView XTM RED eye tracking technology was used simultaneously with the EEG to measure the participants’ attention to the presented stimuli. Three datasets were generated using the amplitude, time-frequency decomposition, and time-frequency connectivity metrics of P300 and LPP components to separate food and nonfood stimuli. We have implemented k -nearest neighbor (kNN), support vector machine (SVM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Bayesian classifier, decision tree (DT), and Multilayer Perceptron (MLP) classifiers on these datasets. Finally, the response to food-related stimuli in the hunger state is discriminated from nonfood with an accuracy value close to 78% for each dataset. The results obtained in this study motivate us to employ classifier algorithms using the features obtained from single-trial measurements in amplitude and time-frequency space instead of applying more complex ones like connectivity metrics.

Mind over Palate: Unveiling the Role of Neuromarketing in the Food Industry

“…Neuromarketing research has demonstrated that specific colours, shapes, packaging design and food presentation can activate distinct areas of the brain and elicit positive emotional responses in consumers, as well as increase their willingness to purchase and consume food (Clement et al, 2017;Moya et al, 2020;Oswald et al, 2022;Semenova et al, 2023). Through neuromarketing studies, brain activity can be analysed when individuals are exposed to visual food stimuli (Vu et al, 2016;Schlintl and Schienle, 2020;Khan and Lee, 2020;König et al, 2021;Güney et al, 2021;Adhikari, 2023). This allows researchers to identify which visual elements evoke positive reactions, stimulate appetite and enhance the appeal of food.…”

A P300-Detection Method Based on Logistic Regression and a Convolutional Neural Network

“…The P300 EEG signal is a complex combination of superimposed multi-band waveforms, and a number of classification methods have been used to decode the P300 signal. For example, principal component analysis (PCA) and local Fisher discriminant analysis (LFDA) are commonly used to reduce the dimensionality of features ( Bernat et al, 2007 ), while linear discriminant analysis (LDA) ( Dodia et al, 2019 ), support vector machine (SVM) ( Li et al, 2014 ), decision tree (DT) ( Guan et al, 2019 ), random forest (RF) ( Akram et al, 2015 ; Masud et al, 2018 ), ADB ( Hongzhi et al, 2012 ; Yildirim and Halici, 2014 ), and k-nearest neighbor (k-NN) methods ( Guney et al, 2021 ) are commonly used for P300 classification.…”