Cognitive load assessment through EEG: A dataset from arithmetic and Stroop tasks

Area of Science:

• Neuroscience
• Cognitive Science
• Biomedical Engineering

Background:

• Mental stress significantly impacts cognitive function and well-being.
• Accurate, non-invasive methods for detecting stress levels are crucial for personalized healthcare and human-computer interaction.
• Existing datasets may lack the specific focus on cognitive load-induced stress patterns required for advanced algorithm development.

Purpose of the Study:

• To introduce a novel, curated dataset of electroencephalogram (EEG) recordings specifically designed for analyzing mental stress.
• To provide a valuable resource for researchers developing algorithms to detect and classify stress levels based on cognitive load.
• To facilitate advancements in brain-computer interfaces (BCIs) and non-invasive stress monitoring tools.

Main Methods:

• Collected EEG signals from 15 healthy subjects (8 female, 7 male; mean age 21.5 years) during cognitive tasks (Stroop test, arithmetic problems).
• Utilized an 8-channel OpenBCI Cyton board to record frontal lobe EEG activity at a 250 Hz sampling rate.
• Categorized recordings into four stress levels: normal, low, mid, and high, with tasks lasting 10-20 seconds over three trials per subject.

Main Results:

• Successfully curated a dataset of EEG recordings capturing brain responses to varying cognitive loads and induced mental stress.
• The dataset includes distinct EEG patterns associated with normal, low, mid, and high stress levels during specific cognitive tasks.
• Demonstrated the potential of the dataset to differentiate between various mental stress states through EEG signal analysis.

Conclusions:

• The presented EEG dataset is a valuable resource for advancing research in stress detection and classification.
• This dataset supports the development of sophisticated algorithms for non-invasive stress monitoring and personalized healthcare solutions.
• The findings contribute to a deeper understanding of EEG correlates of cognitive load and mental stress, with implications for BCIs and cognitive well-being.