Cognitive load assessment through EEG: a dataset from arithmetic and stroop tasks

Nirabi, Ali and Abd Rahman, Faridah and Habaebi, Mohamed Hadi and Sidek, Khairul Azami and Yusoff, Siti Hajar (2025) Cognitive load assessment through EEG: a dataset from arithmetic and stroop tasks. Data In Brief, 60 (3). pp. 1-10. ISSN 2352-3409

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Abstract

This study introduces a thoughtfully curated dataset compris- ing electroencephalogram (EEG) recordings designed to un- ravel mental stress patterns through the perspective of cogni- tive load. The dataset incorporates EEG signals obtained from 15 subjects, with a gender distribution of 8 females and 7 males, and a mean age of 21.5 years [ 1 ]. Recordings were collected during the subjects’ engagement in diverse tasks, including the Stroop color-word test and arithmetic problem- solving tasks. The recordings are categorized into four classes representing varying levels of induced mental stress: normal, low, mid, and high. Each task was performed for a duration of 10–20 s, and three trials were conducted for comprehen- sive data collection. Employing an OpenBCI device with an 8- channel Cyton board, the EEG captures intricate responses of the frontal lobe to cognitive challenges posed by the Stroop and Arithmetic Tests, recorded at a sampling rate of 250 Hz. The proposed dataset serves as a valuable resource for ad- vancing research in the realm of brain-computer interfaces and offers insights into identifying EEG patterns associated with stress. The proposed dataset serves as a valuable resource for re- searchers, offering insights into identifying EEG patterns that correlate with different stress states. By providing a solid foundation for the development of algorithms capable of detecting and classifying stress levels, the dataset supports innovations in non-invasive monitoring tools and contributes to personalized healthcare solutions that can adapt to the cognitive states of users. This study’s foundation is crucial for advancing stress classification research, with significant implications for cognitive function and well-being.

Item Type:	Article (Journal)
Uncontrolled Keywords:	EEG signals Mental stress Stress detection dataset Artificial intelligence Deep learning algorithms
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Dr. Mohamed Hadi Habaebi
Date Deposited:	07 Apr 2025 17:26
Last Modified:	07 Apr 2025 17:26
URI:	http://irep.iium.edu.my/id/eprint/120456

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