Quantitative and qualitative indicators of developing anticipation skills in wrestling athletes

Actual problems of modern psychology are fundamental studies of the phenomenon of anticipation, the search for new methodological approaches to the study of its mechanisms and processes of development are considered. There is a particular interest in the anticipation issue in sport, where the probabilistic forecast of the situation is crucial for winning. The paper presents the results of testing methods for anticipation skills in wrestling. The main objective of the study was to find psycho-physiological and behavioral indicators to quantitatively and qualitatively evaluate the degree of anticipation. The skills formation procedure was based on the reinforcement of the correct choice of behaviour in simulated situations of decision-making using multiple choices technique. Stimuli were videos of simulated situations in wrestling. Simultaneous recording of oculomotor activity and registration of multi-channel electroencephalogram (EEG) was carried out. The results showed the effectiveness of the proposed method. After completing training the number of errors and the decision taking time span reduced. Expert assessment of the main qualifying factors showed a significant increase of the test group. On the psychophysiological level, there is a reduction of oculomotor activity in selecting the right answers, reducing the number of fixations, the number of fixations and saccades reverse on the text of questions and answers. Analysis of EEG parameters showed a gradual decrease in the index of brain activation when analysing simulated situations and dynamic movement of the peaks of activity in the frontal areas of the temporal and occipital areas during the series of trainings

The Psychophysiological Diagnostics of the Functional State of the ATHLE TE. Preliminary Data

The original experimental scheme was developed to investigate athletes’ functionalstates (FS) dynamics. The procedure allowed modeling various FS importantfor predicting the professional success of athletes: psychological and physiologicalstress, fatigue, and optimal FS (OFS). There were two main criteria fordifferentiation of the FS under study: efficiency rates and the psychological andphysiological costs of the achieved efficiency level. Analysis of the FS-dependentpsychophysiological changes showed significant interindividual differences on anumber of parameters. Thus, no single indicator could be used as effective diagnosticsfor the FS criteria. A minimum number of indicators need to be recordedincluded cardiovascular indicators (heart rate, ECG), respiration, muscle tension(EMG), and brain activity (EEG) in the range of alpha and beta waves. The mainproblem can be artifacts induced by movement and muscle tension. The specialprocedure for artifact rejection and reduction of the artifacts was developed. Itallowed recording EEG, ECG, and EOG signals simultaneously. Another problemwas related to the development of the mathematical algorithm to analyze individualdata and differentiate patterns of the signals recorded from the athletes.An original approach to differentiate the FS – the k-means clustering algorithm –was offered based on seven psychophysiological indicators. Results of clusteringshowed that the k-means algorithm for seven-component vectors allows onewith confidence to differentiate state of quiet wakefulness, states of psychologicaland physiological stress. As the number of parameters used is attenuatedfrom seven to four (without the EEG parameters) the accuracy of distinguishing FS is significantly reduced. To construct a complete and accurate differentiationof an athlete’s FS one should collect some statistical data on the dynamics ofeach FS in different time periods of the person’s life – in the process of training,after successful competition, and after losing competition

utomated real-time classification of functional states: the significance of individual tuning stage

Automated classification of a human functional state is an important problem, with applications including stress resistance evaluation, supervision over operators of critical infrastructure, teaching and phobia therapy. Such classification is particularly efficient in systems for teaching and phobia therapy that include a virtual reality module, and provide the capability for dynamic adjustment of task complexity. In this paper, a method for automated real-time binary classification of human functional states (calm wakefulness vs. stress) based on discrete wavelet transform of EEG data is considered. It is shown that an individual tuning stage of the classification algorithm — a stage that allows the involvement of certain information on individual peculiarities in the classification, using very short individual learning samples, significantly increases classification reliability. The experimental study that proved this assertion was based on a specialized scenario in which individuals solved the task of detecting objects with given properties in a dynamic set of flying objects