Knowledge extraction, modeling and formalization: EEG case study
This work addresses a domain-specific problem in EEG analysis for sleep research, but it is incremental as it adapts an existing FCA model to a new application.
The paper tackles the problem of mining electroencephalographic (EEG) recordings for sleep spindle patterns by applying Formal Concept Analysis (FCA), resulting in the specification of a discretization procedure and an experimental architecture for this domain.
Formal Concept Analysis (FCA) is a well-established method for data analysis which finds many applications in data mining. Its extension on complex data representation formats brought a wave of new applications to the problems such as gene expression mining, prediction of toxicity of chemical compounds or clustering of sequences in process event logs. Insipired from this work our research inherits their model and designs an experiment for mining electroencephalographic recordings for patterns of sleep spindles. The contribution of this paper lies in the specification of desritizition procedure and the architecture of FCA experiment. We also provide some reflection on the related research papers.