A Novel Near-Infrared Spectroscopy Based Spatiotemporal Cognition Study of the Human Brain Using Clustering

Ahsan Abdullah*, Imtiaz Hussain Khan, Abdullah Basuhail, Amir Hussain

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

In this study, we investigate how the two hemispheres of the brain are involved spatiotemporally in a cognitive-based setup when people relate different colors with different concepts (for example, the color ‘blue’ associated with the word ‘dependable’ or ‘cheap’) objectively or subjectively. We developed an experimental setup using a 17-channel near-infrared spectroscopy (NIRS) device to measure the changes in brain hemoglobin concentration during a concept–color association task in a block design paradigm. The channel-wise activation data were recorded for 10 male students; after cleansing, the data were clustered using an indigenous clustering technique to identify channels having similar spatiotemporal activity. Data mining was imperative because of the big data generated by NIRS (ca. 0.1+ MB textual data captured per sec involving high volume and veracity), for which the traditional statistical techniques for data analysis could have failed to discover the patterns of interest. The results showed that it was possible to associate brain activities in the two hemispheres to study the association among linguistic concepts and colors, with most neural activity taking place in the right hemisphere of the brain characterized with intuition, subjectivity, etc. Thus, the study suggests novel application areas of neural activity analysis, such as color as marketing cue, response of obese versus lean to food intake, traditional versus neural data validation.

Original languageEnglish
Pages (from-to)693-705
Number of pages13
JournalCognitive Computation
Volume7
Issue number6
DOIs
Publication statusPublished - 25 Sept 2015
Externally publishedYes

Keywords

  • Big data
  • Brain hemispheres
  • Concept–color association
  • One-way clustering

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