Training Machine Learning Models to Detect Group Differences in Neurophysiological Data using Recurrence Quantification Analysis based Features

Gianluca Guglielmo, Travis Wiltshire, Max Louwerse

2022

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.

Paper Citation

in Harvard Style

Guglielmo G., Wiltshire T. and Louwerse M. (2022). Training Machine Learning Models to Detect Group Differences in Neurophysiological Data using Recurrence Quantification Analysis based Features. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 428-435. DOI: 10.5220/0010832200003116

in Bibtex Style

@conference{icaart22,
author={Gianluca Guglielmo and Travis Wiltshire and Max Louwerse},
title={Training Machine Learning Models to Detect Group Differences in Neurophysiological Data using Recurrence Quantification Analysis based Features},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={428-435},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010832200003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Training Machine Learning Models to Detect Group Differences in Neurophysiological Data using Recurrence Quantification Analysis based Features
SN - 978-989-758-547-0
AU - Guglielmo G.
AU - Wiltshire T.
AU - Louwerse M.
PY - 2022
SP - 428
EP - 435
DO - 10.5220/0010832200003116