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BIARAM: A process for analyzing correlated brain regions using association rule mining.

Pankush Kalgotra1, Ramesh Sharda2

  • 1Graduate School of Management, Clark University, United States.

Computer Methods and Programs in Biomedicine
|June 16, 2018
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Summary

Association rule mining (ARM) reveals simultaneous brain region activation. This network approach identified stronger frontal lobe and sublobar/insula correlations after interruptions, offering new insights into brain responses.

Keywords:
Association rule miningElectroencephalogram (EEG)InterruptionsNeuroimaging

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Area of Science:

  • Neuroscience
  • Data Mining
  • Biomedical Engineering

Background:

  • Analyzing correlated brain activity offers superior insights compared to individual region analysis.
  • Network approaches for brain activity analysis are gaining interest.
  • Association rule mining (ARM), a data mining technique, is proposed for analyzing simultaneous brain region activation.

Purpose of the Study:

  • Introduce ARM for analyzing simultaneous brain region activation.
  • Describe a process for converting neural images into analyzable datasets.
  • Suggest applying ARM for insights into brain responses to stimuli like technological interruptions.

Main Methods:

  • Analyzed electroencephalogram (EEG) data from 46 participants.
  • Converted brain waves to images using standardized low-resolution brain electromagnetic tomography (sLORETA).
  • Reorganized data into a transactional format for ARM analysis.

Main Results:

  • Compared ARM results with conventional neuroimaging analysis methods.
  • Observed a stronger correlation between frontal lobe and sublobar/insula regions post-interruption.
  • This correlated activity was not apparent through independent region analysis.

Conclusions:

  • Introduced ARM as a novel method for analyzing multiple brain images simultaneously.
  • Suggests the biomedical community can utilize ARM for deeper understanding of stimuli-affected correlated brain regions.