Knowledge extraction, modeling and formalization: EEG case study

TL;DR

This paper applies Formal Concept Analysis to EEG data, specifically sleep spindles, by designing a new discretization procedure and FCA experiment architecture, advancing data mining techniques in neuroscience.

Contribution

It introduces a novel discretization method and experimental framework for applying FCA to EEG sleep spindle pattern mining.

Findings

Discretization procedure tailored for EEG data.

Architecture of FCA experiment for sleep spindle detection.

Reflections on related research in the field.

Abstract

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.