Training Machine Learning Models to Detect Group Differences in Neurophysiological Data Using Recurrence Quantification Analysis Based Features

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

Physiological data have shown to be useful in tracking and differentiating cognitive processes in a variety of experimental tasks, such as numerical skills and arithmetic tasks. Numerical skills are critical because they are strong predictors of levels of ability in cognitive domains such as literacy, attention, and understanding contexts of risk and uncertainty. In this work, we examined frontal and parietal electroencephalogram signals recorded from 36 healthy participants performing a mental arithmetic task. From each signal, six RQA-based features (Recurrence Rate, Determinism, Laminarity, Entropy, Maximum Diagonal Line Length and, Average Diagonal Line Length) were extracted and used for classification purposes to discriminate between participants performing proficiently and participants performing poorly. The results showed that the three classifiers implemented provided an accuracy above 0.85 on 5-fold cross-validation, suggesting that such features are effective in detecting performance independently from the specific classifiers used. Compared to other successful methods, RQA-based features have the potential to provide insights into the nature of the physiological dynamics and the patterns that differentiate levels of proficiency in cognitive tasks.
Original languageEnglish
Title of host publication14th International Conference on Agents and Artificial Intelligence (ICAART)
Pages428-435
Number of pages8
Volume3
DOIs
Publication statusPublished - 2022
Event14th International Conference on Agents and Artificial Intelligence (ICAART) - online
Duration: 3 Feb 20225 Feb 2022
Conference number: 14th
https://icaart.scitevents.org/

Conference

Conference14th International Conference on Agents and Artificial Intelligence (ICAART)
Abbreviated titleICAART
Period3/02/225/02/22
Internet address

Keywords

  • Mathematical Skills
  • Cognitive Task
  • Machine Learning
  • Complex Systems
  • Recurrence Quantification Analysis

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