Suk-Chung Yoon

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With the explosive growth of the size of databases, many knowledge discovery applications deal with large quantities of data. There is an urgent need to develop methodologies which will allow the applications to focus search to a potentially interesting and relevant portion of the data, which can reduce the computational complexity of the knowledge(More)
Common Spatial Patterns (CSP) is a widely used spatial filtering technique for electroencephalography (EEG)-based brain-computer interface (BCI). It is a two-class supervised technique that needs subject-specific training data. Due to EEG nonstationarity, EEG signal may exhibit significant intra- and inter-subject variation. As a result, spatial filters(More)
In this paper, we present a method to utilize semantic constraints that play an important role in search space reduction and termination of query evaluation in object-oriented databases. Our approach consists of three successive refinement steps: rule generation, semantic knowledge compilation, and semantic reformulation. In the rule generation step, we(More)
In this paper, we introduce a partially automated method for generating intensional answers at multiple abstraction levels for a query, which can help database users find more interesting and desired answers. Our approach consists of three phases: preprocessing, query execution, and answer generation. In the preprocessing phase, we build a set of concept(More)
Common Spatial Patterns (CSP) is a widely used spatial filtering method for electroencephalogram (EEG)-based brain computer interface (BCI). It is a supervised technique that needs subject specific training data. Due to the non-stationary nature of EEG, EEG signal may exhibit significant inter- and intra-subject variation. Consequently, spatial filters(More)
Key-phrase extraction plays a useful a role in research areas of Information Systems (IS) like digital libraries. Short metadata like key phrases are beneficial for searchers to understand the concepts found in the documents. This paper evaluates the effectiveness of different supervised learning techniques on biomedical full-text: Sequential Minimal(More)
BRAIN computer interface (BCI) is a communication technique that aims to detect and identify brain intents and translate them into machine commands to control the operation of electrical and/or mechanical devices. Electroencephalography (EEG) is a widely used imaging technique for noninvasive BCI. Due to EEG non-stationarity, which is typically caused by(More)
Extracting reliable and discriminative features remains a critical challenge in the development of brain computer interface (BCI) techniques. Common spatial patterns (CSP) is frequently used for spatial filtering and feature extraction in electroencephalography (EEG)-based BCI. It performs a supervised and subject-specific learning of EEG data acquired in(More)
Brain computer interface (BCI) is a technology that enables a user to interact with the outside world by measuring and analysing signals associated with neural activity, and mapping an identified neural activity pattern to a behavior or action. In this work, an BCI system was developed where the operation of a quadcopter is controlled by identified brain(More)