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Brain Activity is Influenced by How High Dimensional Data are Represented: An EEG Study of Scatterplot Diagnostic (Scagnostics) Measures

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Abstract

Visualization and visual analytic tools amplify one’s perception of data, facilitating deeper and faster insights that can improve decision making. For multidimensional data sets, one of the most common approaches of visualization methods is to map the data into lower dimensions. Scatterplot matrices (SPLOM) are often used to visualize bivariate relationships between combinations of variables in a multidimensional dataset. However, the number of scatterplots increases quadratically with respect to the number of variables. For high dimensional data, the corresponding enormous number of scatterplots makes data exploration overwhelmingly complex, thereby hindering the usefulness of SPLOM in human decision making processes. One approach to address this difficulty utilizes Graph-theoretic Scatterplot Diagnostic (Scagnostics) to automatically extract a subset of scatterplots with salient features and of manageable size with the hope that the data will be sufficient for improving human decisions. In this paper, we use Electroencephalogram (EEG) to observe brain activity while participants make decisions informed by scatterplots created using different visual measures. We focused on 4 categories of Scagnostics measures: Clumpy, Monotonic, Striated, and Stringy. Our findings demonstrate that by adjusting the level of difficulty in discriminating between data sets based on the Scagnostics measures, different parts of the brain are activated: easier visual discrimination choices involve brain activity mostly in visual sensory cortices located in the occipital lobe, while more difficult discrimination choices tend to recruit more parietal and frontal regions as they are known to be involved in resolving ambiguities. Our results imply that patterns of neural activity are predictive markers of which specific Scagnostics measures most assist human decision making based on visual stimuli such as ours.

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Acknowledgements

The authors would like to thank Prof. Chris Weaver at University of Oklahoma for his initial helpful guidance and encouragement in designing this study about Scagnostic measures. We would like to also thank Dr. Chelsea P Reichert for her support and guides in analyzing the brain data. Finally, we thank students Arezoo Bybordi, Marino Echavarria, Vincent Filardi, Kenneth Ng, Chhewang Sherpa and Yu Xuan Huang at The City College of New York who helped us in implementation, conducting and facilitating the user study.

Funding

This work was supported by a grant from the PSC CUNY 61256-00 49.

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Authors and Affiliations

  1. Verus Research, 6100 Uptown Blvd NE, Suite 260, Albuquerque, New Mexico, 87110, USA

    Ronak Etemadpour

  2. Radiology Department, UNM Health Sciences Center (UNM), Albuquerque, New Mexico, USA

    Ronak Etemadpour

  3. The City College of New York, 160 Convent Ave, New York, NY, 10031, USA

    Ronak Etemadpour & Sonali Shintree

  4. CUNY Advanced Science Research Center, Neuroscience Initiative, Graduate Center, New York, NY, USA

    A. Duke Shereen

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  1. Ronak Etemadpour
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Ronak Etemadpour as the first author contributed in conceptualization, designing the study, gaining fund for the project, conducting the study, performing data analysis, data curation, administrating the project, data interpretation and drafting the manuscript. SS performed the data analysis, coding and programming, and partially drafting the manuscript. AS supervised experimental design, training sessions of neuroimaging protocols, quality assurance, and critical review and drafting of manuscript.

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Correspondence to A. Duke Shereen.

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Etemadpour, R., Shintree, S. & Shereen, A.D. Brain Activity is Influenced by How High Dimensional Data are Represented: An EEG Study of Scatterplot Diagnostic (Scagnostics) Measures. J Healthc Inform Res 8, 19–49 (2024). https://doi.org/10.1007/s41666-023-00145-2

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