Abstract
This paper describes the current state of the work aimed towards an affective application of BCI to the task of complex data visual exploration. The developed technological approach exploits the idea of supporting tacit and complex domain-specific knowledge acquisition during the examination of visual images built using large input data sets. The presented experimental research on the complex network data exploration process shows the capabilities of the presented approach through the analysis of a user’s affective state estimation.
Highlights
Contemporary scientific tasks deal with a huge amount of complex data
A strong push for the development of visual analysis technologies was given by the appearance of virtual reality, which enables advanced an interactive visual exploration of complex spatiotemporal datasets
The work presented in this paper is devoted to the development of technology which augments the visual exploration of complex scientific data with affective brain-computer interface (BCI)
Summary
Contemporary scientific tasks deal with a huge amount of complex data. These data sets present results of observations, analysis and simulation. Experts can perform analysis without explicit attention to all the aspects of the analysed scene by using some tacit knowledge and their own experience In this case it is very difficult to identify all the aspects of the decision even by interviewing the expert. A part of expert tacit knowledge is lost To overcome these issues, a visualisation system should have information about the objects within the visual scene which are most important for the user within a particular task. In this article the idea of using the affective state of experts, estimated by brain-computer interface for support of complex scientific data exploration, is discussed We believe that such implementation can significantly enrich the virtual reality technology by enabling tacit knowledge acquisition and using them to make the process of data analysis more effective
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