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EN
Objectives: Optimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy. Methods: System of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI). Results: The designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects. Conclusions: It is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.
EN
The uncanny valley (UV) hypothesis suggests that the observation of almost human-like characters causes an increase of discomfort. We conducted a study using self-report questionnaire, response time measurement, and electrodermal activity (EDA) evaluation. In the study, 12 computer-generated characters (robots, androids, animated, and human characters) were presented to 33 people (17 women) to (1) test the effect of a background context on the perception of characters, (2) establish whether there is a relation between declared feelings and physiological arousal, and (3) detect the valley of the presented stimuli. The findings provide support for reverse relation between human-likeness and the arousal (EDA). Furthermore, a positive correlation between EDA and human-likeness appraisal reaction time upholds one of the most common explanations of the UV - the categorization ambiguity. The absence of the significant relationship between declared comfort and EDA advocates the necessity of physiological measures for UV studies.
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