Dataset Information

Facial electromyography and subjective liking data from 70 New Zealand participants in response to food images and chocolate samples.

ABSTRACT: This article describes a dataset of facial electromyography and subjective liking data from 70 New Zealand participants used in the study "An unfamiliar presence reduces facial disgust responses to food stimuli" by Nath, Cannon and Philipp [1]. Participants' facial muscle activity from zygomaticus major, corrugator supercilii, and levator labii superioris was recorded as they viewed and rated food images, and tasted samples of chocolate. Half of the participants were seated alone, and the other half was seated in the presence of the researcher. The data allows for investigations into the effect of social context on hedonic ratings and facial responses to food, and an exploration into the individual factors contributing to differences in facial reactivity. The data includes raw EMG files generated by Acqknowledge 4.2, raw subjective liking files generated by PsychoPy, a table of participant information, the food images stimuli, the PsychoPy code used for stimuli presentation, and the R scripts used to filter, aggregate and analyse the data.

SUBMITTER: Nath EC

PROVIDER: S-EPMC6976919 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Facial electromyography and subjective liking data from 70 New Zealand participants in response to food images and chocolate samples.

Nath Elizabeth C EC Cannon Peter R PR Philipp Michael C MC

Data in brief 20200111

This article describes a dataset of facial electromyography and subjective liking data from 70 New Zealand participants used in the study "An unfamiliar presence reduces facial disgust responses to food stimuli" by Nath, Cannon and Philipp [1]. Participants' facial muscle activity from zygomaticus major, corrugator supercilii, and levator labii superioris was recorded as they viewed and rated food images, and tasted samples of chocolate. Half of the participants were seated alone, and the other ...[more]

PMID: 31993472

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Project description:In the present study, we applied Machine Learning (ML) methods to identify psychobiological markers of cognitive processes involved in the process of emotion elicitation as postulated by the Component Process Model (CPM). In particular, we focused on the automatic detection of five appraisal checks-novelty, intrinsic pleasantness, goal conduciveness, control, and power-in electroencephalography (EEG) and facial electromyography (EMG) signals. We also evaluated the effects on classification accuracy of averaging the raw physiological signals over different numbers of trials, and whether the use of minimal sets of EEG channels localized over specific scalp regions of interest are sufficient to discriminate between appraisal checks. We demonstrated the effectiveness of our approach on two data sets obtained from previous studies. Our results show that novelty and power appraisal checks can be consistently detected in EEG signals above chance level (binary tasks). For novelty, the best classification performance in terms of accuracy was achieved using features extracted from the whole scalp, and by averaging across 20 individual trials in the same experimental condition (UAR = 83.5 ± 4.2; N = 25). For power, the best performance was obtained by using the signals from four pre-selected EEG channels averaged across all trials available for each participant (UAR = 70.6 ± 5.3; N = 24). Together, our results indicate that accurate classification can be achieved with a relatively small number of trials and channels, but that averaging across a larger number of individual trials is beneficial for the classification for both appraisal checks. We were not able to detect any evidence of the appraisal checks under study in the EMG data. The proposed methodology is a promising tool for the study of the psychophysiological mechanisms underlying emotional episodes, and their application to the development of computerized tools (e.g., Brain-Computer Interface) for the study of cognitive processes involved in emotions.

Project description:Eliminating facial electromyographic (EMG) signal from the electroencephalogram (EEG) is crucial for the accuracy of applications such as brain computer interfaces (BCIs) and brain functionality measurement. Facial electromyography typically corrupts the electroencephalogram. Although it is possible to find in the literature a number of multi-channel approaches for filtering corrupted EEG, studies employing single-channel approaches are scarce. In this context, this study proposed a single-channel method for attenuating facial EMG noise from contaminated EEG. The architecture of the method allows for the evaluation and incorporation of multiple decomposition and adaptive filtering techniques. The decomposition method was responsible for generating EEG or EMG reference signals for the adaptive filtering stage. In this study, the decomposition techniques CiSSA, EMD, EEMD, EMD-PCA, SSA, and Wavelet were evaluated. The adaptive filtering methods RLS, Wiener, LMS, and NLMS were investigated. A time and frequency domain set of features were estimated from experimental signals to evaluate the performance of the single channel method. This set of characteristics permitted the characterization of the contamination of distinct facial muscles, namely Masseter, Frontalis, Zygomatic, Orbicularis Oris, and Orbicularis Oculi. Data were collected from ten healthy subjects executing an experimental protocol that introduced the necessary variability to evaluate the filtering performance. The largest level of contamination was produced by the Masseter muscle, as determined by statistical analysis of the set of features and visualization of topological maps. Regarding the decomposition method, the SSA method allowed for the generation of more suitable reference signals, whereas the RLS and NLMS methods were more suitable when the reference signal was derived from the EEG. In addition, the LMS and RLS methods were more appropriate when the reference signal was the EMG. This study has a number of practical implications, including the use of filtering techniques to reduce EEG contamination caused by the activation of facial muscles required by distinct types of studies. All the developed code, including examples, is available to facilitate a more accurate reproduction and improvement of the results of this study.

Dataset Information

Facial electromyography and subjective liking data from 70 New Zealand participants in response to food images and chocolate samples.

Publications

Facial electromyography and subjective liking data from 70 New Zealand participants in response to food images and chocolate samples.

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