Single-Trial Cognitive Stress Classification Using Portable Wireless Electroencephalography
(2019) In Sensors (Basel, Switzerland) 19(3).- Abstract
This work used a low-cost wireless electroencephalography (EEG) headset to quantify the human response to different cognitive stress states on a single-trial basis. We used a Stroop-type color⁻word interference test to elicit mild stress responses in 18 subjects while recording scalp EEG. Signals recorded from thirteen scalp locations were analyzed using an algorithm that computes the root mean square voltages in the theta (4⁻8 Hz), alpha (8⁻13 Hz), and beta (13⁻30 Hz) bands immediately following the initiation of Stroop stimuli; the mean of the Teager energy in each of these three bands; and the wideband EEG signal line-length and number of peaks. These computational features were extracted from the EEG signals on thirteen electrodes... (More)
This work used a low-cost wireless electroencephalography (EEG) headset to quantify the human response to different cognitive stress states on a single-trial basis. We used a Stroop-type color⁻word interference test to elicit mild stress responses in 18 subjects while recording scalp EEG. Signals recorded from thirteen scalp locations were analyzed using an algorithm that computes the root mean square voltages in the theta (4⁻8 Hz), alpha (8⁻13 Hz), and beta (13⁻30 Hz) bands immediately following the initiation of Stroop stimuli; the mean of the Teager energy in each of these three bands; and the wideband EEG signal line-length and number of peaks. These computational features were extracted from the EEG signals on thirteen electrodes during each stimulus presentation and used as inputs to logistic regression, quadratic discriminant analysis, and k-nearest neighbor classifiers. Two complementary analysis methodologies indicated classification accuracies over subjects of around 80% on a balanced dataset for the logistic regression classifier when information from all electrodes was taken into account simultaneously. Additionally, we found evidence that stress responses were preferentially time-locked to stimulus presentation, and that certain electrode⁻feature combinations worked broadly well across subjects to distinguish stress states.
(Less)
- author
- Blanco, Justin A. LU ; Vanleer, Ann C. ; Calibo, Taylor K. and Firebaugh, Samara L.
- organization
- publishing date
- 2019-01-25
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Biomedical signal processing, Brain–computer interface, Cognitive stress, Electroencephalography, Stroop test
- in
- Sensors (Basel, Switzerland)
- volume
- 19
- issue
- 3
- article number
- 499
- publisher
- MDPI AG
- external identifiers
-
- pmid:30691041
- scopus:85060647597
- ISSN
- 1424-8220
- DOI
- 10.3390/s19030499
- language
- English
- LU publication?
- yes
- id
- 8fb84764-1748-42a7-b3df-1929eb8fa2a4
- date added to LUP
- 2019-02-05 11:06:37
- date last changed
- 2024-04-01 19:13:14
@article{8fb84764-1748-42a7-b3df-1929eb8fa2a4,
abstract = {{<p>This work used a low-cost wireless electroencephalography (EEG) headset to quantify the human response to different cognitive stress states on a single-trial basis. We used a Stroop-type color⁻word interference test to elicit mild stress responses in 18 subjects while recording scalp EEG. Signals recorded from thirteen scalp locations were analyzed using an algorithm that computes the root mean square voltages in the theta (4⁻8 Hz), alpha (8⁻13 Hz), and beta (13⁻30 Hz) bands immediately following the initiation of Stroop stimuli; the mean of the Teager energy in each of these three bands; and the wideband EEG signal line-length and number of peaks. These computational features were extracted from the EEG signals on thirteen electrodes during each stimulus presentation and used as inputs to logistic regression, quadratic discriminant analysis, and k-nearest neighbor classifiers. Two complementary analysis methodologies indicated classification accuracies over subjects of around 80% on a balanced dataset for the logistic regression classifier when information from all electrodes was taken into account simultaneously. Additionally, we found evidence that stress responses were preferentially time-locked to stimulus presentation, and that certain electrode⁻feature combinations worked broadly well across subjects to distinguish stress states.</p>}},
author = {{Blanco, Justin A. and Vanleer, Ann C. and Calibo, Taylor K. and Firebaugh, Samara L.}},
issn = {{1424-8220}},
keywords = {{Biomedical signal processing; Brain–computer interface; Cognitive stress; Electroencephalography; Stroop test}},
language = {{eng}},
month = {{01}},
number = {{3}},
publisher = {{MDPI AG}},
series = {{Sensors (Basel, Switzerland)}},
title = {{Single-Trial Cognitive Stress Classification Using Portable Wireless Electroencephalography}},
url = {{http://dx.doi.org/10.3390/s19030499}},
doi = {{10.3390/s19030499}},
volume = {{19}},
year = {{2019}},
}