Electroencephalographic Correlates in Synthetic and Real Emotional Face Stimulation.

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.

Machine Learning Assessment of Spasmodic Dysphonia Based on Acoustical and Perceptual Parameters.

Adductor spasmodic dysphonia is a type of adult-onset focal dystonia characterized by involuntary spasms of laryngeal muscles. This paper applied machine learning techniques for the severity assessment of spasmodic dysphonia. To this aim, 7 perceptual indices and 48 acoustical parameters were estimated from the Italian word /a'jwɔle/ emitted by 28 female patients, manually segmented from a standardized sentence and used as features in two classification experiments. Subjects were divided into three severity classes (mild, moderate, severe) on the basis of the G (grade) score of the GRB scale. The first aim was that of finding relationships between perceptual and objective measures with the Local Interpretable Model-Agnostic Explanations method. Then, the development of a diagnostic tool for adductor spasmodic dysphonia severity assessment was investigated. Reliable relationships between G; R (Roughness); B (Breathiness); Spasmodicity; and the acoustical parameters: voiced percentage, F2 median, and F1 median were found. After data scaling, Bayesian hyperparameter optimization, and leave-one-out cross-validation, a k-nearest neighbors model provided 89% accuracy in distinguishing patients among the three severity classes. The proposed methods highlighted the best acoustical parameters that could be used jointly with GRB indices to support the perceptual evaluation of spasmodic dysphonia and provide a tool to help severity assessment of spasmodic dysphonia.

Eye-tracking correlates of the Implicit Association Test.

Raising awareness of environmental challenges represents an important issue for researchers and scientists. As public opinion remains ambiguous, implicit attitudes toward climate change must be investigated. A custom Single-Category Implicit Association Test, a version of the Implicit Association Test, was developed to assess climate change beliefs. It was administered to 20 subjects while eye movements were tracked using a smart glasses system. Eye gaze patterns were analysed to understand whether they could reflect implicit attitudes toward nature. Recurrence Quantification Analysis was performed to extract 13 features from the eye-tracking data, which were used to perform statistical analyses. Significant differences were found between target stimuli (words related to climate change) and bad attributes in reaction time, and between target stimuli and good attributes in diagonal length entropy, suggesting that eye-tracking may provide an alternative source of information to electroencephalography in modeling and predicting implicit attitudes.

Artificial Intelligence Procedure for the Screening of Genetic Syndromes Based on Voice Characteristics.

Perceptual and statistical evidence has highlighted voice characteristics of individuals affected by genetic syndromes that differ from those of normophonic subjects. In this paper, we propose a procedure for systematically collecting such pathological voices and developing AI-based automated tools to support differential diagnosis. Guidelines on the most appropriate recording devices, vocal tasks, and acoustical parameters are provided to simplify, speed up, and make the whole procedure homogeneous and reproducible. The proposed procedure was applied to a group of 56 subjects affected by Costello syndrome (CS), Down syndrome (DS), Noonan syndrome (NS), and Smith-Magenis syndrome (SMS). The entire database was divided into three groups: pediatric subjects (PS; individuals < 12 years of age), female adults (FA), and male adults (MA). In line with the literature results, the Kruskal-Wallis test and post hoc analysis with Dunn-Bonferroni test revealed several significant differences in the acoustical features not only between healthy subjects and patients but also between syndromes within the PS, FA, and MA groups. Machine learning provided a k-nearest-neighbor classifier with 86% accuracy for the PS group, a support vector machine (SVM) model with 77% accuracy for the FA group, and an SVM model with 84% accuracy for the MA group. These preliminary results suggest that the proposed method based on acoustical analysis and AI could be useful for an effective, non-invasive automatic characterization of genetic syndromes. In addition, clinicians could benefit in the case of genetic syndromes that are extremely rare or present multiple variants and facial phenotypes.