This work reports on physiological electroencephalographic (EEG) correlates in cognitive and emotional processes within the discrimination between synthetic and real faces visual stimuli. Human perception of manipulated data has been addressed in the literature from several perspectives. Researchers have investigated how the use of deep fakes alters people's ability in face-processing tasks, such as face recognition. Although recent studies showed that humans, on average, are still able to correctly recognize synthetic faces, this study investigates whether those findings still hold considering the latest advancements in AI-based, synthetic image creation. Specifically, 18-channels EEG signals from 21 healthy subjects were analyzed during a visual experiment where synthetic and actual emotional stimuli were administered. According to recent literature, participants were able to discriminate the real faces from the synthetic ones, by correctly classifying about 77% of all images. Preliminary encouraging results showed statistical significant differences in brain activation in both stimuli (synthetic and real) classification and emotional response.

Download full-text PDF

Source
http://dx.doi.org/10.1109/EMBC40787.2023.10340895DOI Listing

Publication Analysis

Top Keywords

synthetic real
12
real faces
8
synthetic
7
electroencephalographic correlates
4
correlates synthetic
4
real
4
emotional
4
real emotional
4
emotional face
4
face stimulation
4

Similar Publications

The I38T substitution in the influenza virus polymerase-acidic (PA) subunit is a resistance marker of concern for treatment with the antiviral baloxavir marboxil (BXM). Thus, monitoring PA/I38T mutations is of clinical importance. Here, we developed three rapid and sensitive assays for the detection and monitoring of the PA/I38T mutation.

View Article and Find Full Text PDF

Accurate estimation of interfacial tension (IFT) between nitrogen and crude oil during nitrogen-based gas injection into oil reservoirs is imperative. The previous research works dealing with prediction of IFT of oil and nitrogen systems consider synthetic oil samples such n-alkanes. In this work, we aim to utilize eight machine learning methods of Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-nearest Neighbors (KNN), Ensemble Learning (EL), Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) to construct data-driven intelligent models to predict crude oil - nitrogen IFT based upon experimental data of real crude oils samples encountered in underground oil reservoirs.

View Article and Find Full Text PDF

Androgens induce renal synthesis of urinary lipocalin-family protein, a potential inter-sexual transmitter in viviparous rockfish.

Biochim Biophys Acta Gen Subj

January 2025

Division of Marine Life Science, Faculty of Fisheries Sciences, Hokkaido University, 3-1-1 Minato, Hakodate, Hokkaido 041-8611, Japan. Electronic address:

In viviparous black rockfish (Sebastes schlegelii), the kidney of reproductive-phase males actively produces lipocalin-type prostaglandin D synthase homolog (LPGDSh) protein, which is presumably involved in intersexual communication when emitted in the urine. The present study was undertaken to discover whether androgens and their nuclear receptors (Ars) are engaged in regulation of renal LPGDSh protein synthesis in black rockfish. Quantitative real-time polymerase chain reaction, in conjunction with immunohistochemistry and highly sensitive enzyme-linked immunosorbent assay, revealed that intra-abdominal administration of a synthetic androgen, 17α-methyltestosterone (MT), to juvenile black rockfish induced their renal expression of LPGDSh transcript and protein.

View Article and Find Full Text PDF

Candida lusitaniae is one of the fungal species which causes serious health illnesses including peritonitis, vaginitis and fungemia, among others. Several antifungal drugs have been designed to tackle its infections but their efficacy is still questionable due to their associated side effects. Hence, there is a need to design those drugs which possess comparatively higher degree of therapeutic potential.

View Article and Find Full Text PDF

Using deep learning to shorten the acquisition time of brain MRI in acute ischemic stroke: Synthetic T2W images generated from b0 images.

PLoS One

January 2025

Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.

Objective: This study aimed to assess the feasibility of the deep learning in generating T2 weighted (T2W) images from diffusion-weighted imaging b0 images.

Materials And Methods: This retrospective study included 53 patients who underwent head magnetic resonance imaging between September 1 and September 4, 2023. Each b0 image was matched with a corresponding T2-weighted image.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!