Background and Objective. Paul Ekman has demonstrated that we can estimate emotions on the basis of individual facial muscles movements. However, there is a lack of information in the literature about correlation between eye movements and facial emotions. Our objective was to find out whether emotion could be also visible in the dynamical properties of the eye movements. Methods. We have performed our experiment in two sessions related to different video presentations: 1) a reference session with all emotions presentation, and 2) the main session showing presentation of some specific emotions. During both sessions, we have video recordings of the subjects’ face expressions (FE) and eye movements (EM). We have calculated parameters of FE and EM dynamical changes and content of the noise. On this basis, by using time changes of EM, we have predicted 6 different face emotions. Results. We have recorded face expressions of 49 subjects who had the strongest responses to Happiness and Contempt facial emotions. We found statistically significant differences in parameters’ values describing FE and EM between the reference and the main sessions trials. Parameters connected to the Chaos showed highly positive correlations with Happiness, while both the Linear and the Noise components were mostly negatively correlated with this emotion. We achieved highest results with help of the K-Nearest Neighbors algorithm obtaining: Accuracy of 0.89 (+/- 0.01) with the ROC-AUC score of 0.88 (F1 = 0.89), Precision = 0.85, Sensitivity / Recall = 0.93, Specificity = 0.82. Conclusions. We have observed that when the intensity of the Happiness increases, the eye movements become more chaotic and behave less noisy. We see possibilities for use of presented methods as a support for predictions in face expressions, when the lower part of the face is partially hidden, e.g. by a protective mask worn during the COVID epidemic. This could also be a method of confirming the authenticity of the Happiness mimicry, because it would be difficult to voluntarily correlate eye movements with certain levels of the Chaos.

Parkinson’s disease (PD) is the second after Alzheimer’s most popular neurodegenerative disease (ND). Cures for both NDs are currently unavailable. OBJECTIVE: The purpose of our study was to predict the results of different PD patients’ treatments in order to find an optimal one. METHODS: We have compared rough sets (RS) and others, in short, machine learning (ML) models to describe and predict disease progression expressed as UPDRS values (Unified Parkinson’s Disease Rating Scale) in three groups of Parkinson’s patients: 23 BMT (Best Medical Treatment) patients on medication; 24 DBS patients on medication and on DBS therapy (Deep Brain Stimulation) after surgery performed during our study; and 15 POP (Postoperative) patients who had had surgery earlier (before the beginning of our research). Every PD patient had three visits approximately every six months. The first visit for DBS patients was before surgery. On the basis of the following condition attributes: disease duration, saccadic eye movement parameters, and neuropsychological tests: PDQ39 (Parkinson’s Disease Questionnaire - disease-specific health-related quality-of-life questionnaire), and Epworth Sleepiness Scale tests we have estimated UPDRS changes (as the decision attribute). RESULTS: By means of RS rules obtained for the first visit of BMT/DBS/POP patients, we have predicted UPDRS values in the following year (two visits) with global accuracy of 70% for both BMT visits; 56% for DBS, and 67%, 79% for POP second and third visits. The accuracy obtained by ML models was generally in the same range, but it was calculated separately for different sessions (MedOFF/MedON). We have used RS rules obtained in BMT patients to predict UPDRS of DBS patients; for the first session DBSW1: global accuracy was 64%, for the second DBSW2: 85% and the third DBSW3: 74% but only for DBS patients during stimulation-ON. ML models gave better accuracy for DBSW1/W2 session S1(MedOFF): 88%, but inferior results for session S3 (MedON): 58% and 54%. Both RS and ML could not predict UPDRS in DBS patients during stimulation-OFF visits because of differences in UPDRS. By using RS rules from BMT or DBS patients we could not predict UPDRS of POP group, but with certain limitations (only for MedON), we derived such predictions for the POP group from results of DBS patients by using ML models (60%). SIGNIFICANCE: Thanks to our RS and ML methods, we were able to predict Parkinson’s disease (PD) progression in dissimilar groups of patients with different treatments. It might lead, in the future, to the discovery of universal rules of PD progression and optimise the treatment.

There are two very different approaches to understand functioning of the brain. First, there is a huge progress in the research of the neurological and neurophysiological properties of different brain substructures, circuits, networks, single cells, synapses and their molecular properties. It contributes to the progress of research in the fields of basic medical sciences and the dramatic increase in average life expectancy. On the another side that does not directly follows neurological developments, it is our introspection related to individual ways of thinking in order to solve different problems that also involve human creativity (cognitive theory of mind). We use many diverse ways of thinking, and they depend on different circumstances. Especially interesting are influences of intuition, feelings and emotions on our creativity, which is in a large part are also related to the social interactions (affective empathy). In this work, we formalise emotional scales and transfer of emotions between individuals (social emotional thinking). We also demonstrate a continuity of the emotion transfer mappings, and an importance of the interactions between emotional faces. It is not only human specific to show and to react to face emotions, but strong and wide human social interactions are based on the precise emotional social thinking. By measuring critical values of face deformations that may influence mutual emotions, we can test precision and tolerance of human visual and emotional systems. By introduction indiscernibility relations between individual reading of face parts deformation, we have used rough set theory to probe social emotional thinking. As one of us have demonstrated that the visual system has properties that follows rough set theory (cognitive theory of mind), this work extends this concept to the social emotional interactions (cognitive and affective theory of mind). As in modern world IT - information technology - has became driving factor in the process of globalisation by creating effective channels of information exchange; hence it becomes extremely important to analyse emotional meaning (cognitive empathy) for this vast information flow. By using as described here, rough set theory to determine, which parts of information have significant emotional influence, our model may give grounds to increase the collective well-being.

Video‐oculography (VOG) is a tool providing diagnostic information about the progress of the diseases that cause regression of the vergence eye movements, such as Parkinson's disease (PD). The majority of the existing systems are based on sophisticated infra‐red (IR) devices. In this study, the authors show that a webcam‐based VOG system can provide similar accuracy to that of a head‐mounted IR‐based VOG system. They also prove that the authors’ iris localisation algorithm outperforms current state‐of‐the‐art methods on the popular BioID dataset in terms of accuracy. The proposed system consists of a set of image processing algorithms: face detection, facial features localisation and iris localisation. They have performed examinations on patients suffering from PD using their system and a JAZZ‐novo head‐mounted device with IR sensor as reference. In the experiments, they have obtained a mean correlation of 0.841 between the results from their method and those from the JAZZ‐novo. They have shown that the accuracy of their visual system is similar to the accuracy of IR head‐mounted devices. In the future, they plan to extend their experiments to inexpensive high frame rate cameras which can potentially provide more diagnostic parameters.

During deep brain stimulation (DBS) treatment of Parkinson disease, the target of the surgery is the subthalamic nucleus (STN). This anatomical structure is small (9×7×4 mm) and poorly visible using Computer Tomography (CT) or Magnetic Resonance Imag

There exists a common view that the brain acts like a Turing machine: The machine reads information from an infinite tape (sensory data) and, on the basis of the machine's state and information from the tape, an action (decision) is made. The main problem with this model lies in how to synchronize a large number of tapes in an adaptive way so that the machine is able to accomplish tasks such as object classification. We propose that such mechanisms exist already in the eye. A popular view is that the retina, typically associated with high gain and adaptation for light processing, is actually performing local preprocessing by means of its center-surround receptive field. We would like to show another property of the retina: The ability to integrate many independent processes. We believe that this integration is implemented by synchronization of neuronal oscillations. In this paper, we present a model of the retina consisting of a series of coupled oscillators which can synchronize on several scales. Synchronization is an analog process which is converted into a digital spike train in the output of the retina. We have developed a hardware implementation of this model, which enables us to carry out rapid simulation of multineuron oscillatory dynamics. We show that the properties of the spike trains in our model are similar to those found in vivo in the cat retina.