DOI: 10.6084/m9.figshare.30003127.v1
title: iNCog-EEG (ideal vs. Noisy Cognitive EEG for Workload Assessment) Dataset

description: iNCog-EEG: A Multitasking EEG Dataset for Cognitive Workload Assessment with Controlled Task DifficultyThis dataset, named <b>iNCog-EEG (Ideal vs. Noisy Cognitive EEG for Workload Assessment)</b>, contains EEG recordings from 40 participants engaged in a multitiered multitasking protocol designed to emulate realistic cognitive workload environments. Unlike conventional controlled datasets, iNCog-EEG integrates both clean and artifact-contaminated EEG recordings, enabling researchers to evaluate algorithm robustness under ideal and noisy conditions.Key Characteristics<b>Noise Sources Captured (for 10 subjects):</b> Eye movements/blinks (EOG), muscle activity (EMG), cardiac activity (ECG), respiratory artifacts, and power line interference.<b>Hardware Used:</b> EEG signals were acquired using the <b>KT88-3200 digital EEG system</b> with 16 scalp electrodes at a <b>200 Hz sampling rate</b>, positioned according to the <b>international 10–20 system</b>.<b>Participants:</b> 40 healthy individuals. Ethical approval was granted by the <b>Institutional Review Board of Evercare Hospital, Dhaka Ltd. (IRB-1964105-1)</b>.<b>Ground Truth Labels:</b> Derived from task difficulty metadata and subjective feedback, yielding a <b>two-level annotation scheme</b>:<b>Binary classification:</b> No Workload (rest) vs. Workload (task phases)<b>Hierarchical classification:</b> Workload further divided into Low, Moderate, and HighTask and Recording Design<b>Resting Phase:</b> 5 minutes (baseline condition).<b>Multitasking Phases:</b> Three stages of escalating difficulty (easy, medium, hard), each lasting 5 minutes.<b>Breaks:</b> 10 minutes between phases to simulate recovery and workload transitions.<b>Concurrent Cognitive Tasks:</b>Math problem-solvingN-back memory matchingObject trackingResponse inhibition<br>These were distributed across screen quadrants to induce <b>simultaneous cognitive load</b>.<b>Total Duration:</b> ~40 minutes per participant (5 min rest + 3 × 5 min multitasking + 2 × 10 min breaks).File Naming ConventionEach participant’s recordings are stored in a dedicated folder named <code>subXX</code> (e.g., <code>sub01</code>, <code>sub02</code>, …, <code>sub40</code>). Inside each folder, four <b>.EDF</b> files represent the workload conditions:<pre><pre>subxx_nw.EDF → No Workload (resting state) <br>subxx_lw.EDF → Low Workload (easy multitasking) <br>subxx_mw.EDF → Moderate Workload (medium multitasking) <br>subxx_hw.EDF → High Workload (hard multitasking) <br></pre></pre><b>Subjects 01–30:</b> Clean EEG recordings<b>Subjects 31–40:</b> Noisy EEG recordings with real-world artifactsThis structure ensures straightforward differentiation between clean vs. noisy data and across workload levels.ApplicationsThis dataset can be applied to a wide range of research areas, including:EEG signal denoising and artifact rejectionBinary and hierarchical <b>cognitive workload classification</b>Development of <b>robust Brain–Computer Interfaces (BCIs)</b>Benchmarking algorithms under <b>ideal and noisy conditions</b>Multitasking and mental workload assessment in <b>real-world scenarios</b>By combining controlled multitasking protocols with deliberately introduced environmental noise, <b>iNCog-EEG provides a comprehensive benchmark</b> for advancing EEG-based workload recognition systems in both clean and challenging conditions.