mgr inż. Adrian Kastrau
Employment
- Senior ML Engineer at Kinesso
Research fields
Business contact
- Location
- Al. Zwycięstwa 27, 80-219 Gdańsk
- Phone
- +48 58 348 62 62
- biznes@pg.edu.pl
Social media
Contact
- adrkastr@student.pg.edu.pl
Publication showcase
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Psychophysiological strategies for enhancing performance through imagery – skin conductance level analysis in guided vs. self-produced imagery
Athletes need to achieve their optimal level of arousal for peak performance. Visualization or mental rehearsal (i.e., Imagery) often helps to obtain an appropriate level of activation, which can be detected by monitoring Skin Conductance Level (SCL). However, different types of imagery could elicit different amount of physiological arousal. Therefore, this study aims: (1) to investigate differences in SCL associated with two instructional...
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Exploring the influence of personal factors on physiological responses to mental imagery in sport
Imagery is a well-known technique in mental training which improves performance efficiency and influences physiological arousal. One of the biomarkers indicating the amount of physiological arousal is skin conductance level (SCL). The aim of our study is to understand how individual differences in personality (e.g. neuroticism), general imagery and situational sport anxiety are linked to arousal measuring with SCL in situational...
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Neural Oscillation During Mental Imagery in Sport: An Olympic Sailor Case Study
The purpose of the current study was to examine the cortical correlates of imagery depending on instructional modality (guided vs. self-produced) using various sports-related scripts. According to the expert-performance approach, we took an idiosyncratic perspective analyzing the mental imagery of an experienced two-time Olympic athlete to verify whether different instructional modalities of imagery (i.e., guided vs. self-produced)...
Obtained scientific degrees/titles
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2019-07-23
Obtained science degree
mgr inż. Technical Physics (Physical sciences)
Master's thesis
Title: Emotional states recognition using the EEG signal analysis and machine learning approach
Supervisor: dr inż. Patryk Jasik
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