Visual Exploration of Complex Network Data Using Affective Brain-Computer Interface

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.

Authors and Affiliations

Sergey Kovalchuk, Denis Terekhov, Aleksey Bezgodov, Alexander Boukhanovsky

Keywords

Related Articles

Feature-based Sentiment Analysis for Slang Arabic Text

The increased number of Arab users on microblogging services who use Arabic language to write and read has triggered several researchers to study the posted data and discover the user’s opinion and feelings to support de...

MCMC Particle Filter Using New Data Association Technique with Viterbi Filtered Gate Method for Multi-Target Tracking in Heavy Clutter

 Improving data association technique in dense clutter environment for multi-target tracking used in Markov chain Monte Carlo based particle filter (MCMC-PF) are discussed in this paper. A new method named Viterbi f...

E-Learning Methodologies and Tools

E-learning is among the most important explosion propelled by the internet transformation. This allows users to fruitfully gather knowledge and education both by synchronous and asynchronous methodologies to effectively...

Understanding Social Network Usage: Impact of Co-Presence, Intimacy, and Immediacy

This study examines individuals’ intentions and behaviour on Social Networking Sites (SNSs). The study proposed model asserts that “Co-presence”, “Intimacy”, “Immediacy”, “Perceived Enjoyment”, and “Perceived ease of use...

Prediction of Naturally Fractured Reservoir Performance using Novel Integrated Workflow

Generation of naturally fractured reservoir subsurface fracture maps and prediction its production potential are considered complex process due to insufficient data available such as bore hole images, core data and prope...

Download PDF file
  • EP ID EP156792
  • DOI 10.14569/IJACSA.2013.040703
  • Views 70
  • Downloads 0

How To Cite

Sergey Kovalchuk, Denis Terekhov, Aleksey Bezgodov, Alexander Boukhanovsky (2013). Visual Exploration of Complex Network Data Using Affective Brain-Computer Interface. International Journal of Advanced Computer Science & Applications, 4(7), 21-27. https://europub.co.uk/articles/-A-156792