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A multimodal human-computer interaction dataset for neurocognitive user state evaluation.

Sai Zhang1, Xinyu Bai2, Charles Hartley-O'Dwyer2

  • 1Centre for Brain and Behaviour, Department of Psychology, School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK. z.sai@qmul.ac.uk.

Scientific Data
|June 26, 2026
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We present SENSE-42, a new dataset for studying user neurocognitive states during computer use. It captures physiological and behavioral data to understand alertness fluctuations in real-world tasks.

Area of Science:

  • Cognitive Science
  • Human-Computer Interaction
  • Neuroscience

Background:

  • Understanding user neurocognitive states is crucial for adaptive interfaces.
  • Spontaneous fluctuations in alertness impact performance and user experience.
  • Existing datasets often lack multimodal, real-world interaction data.

Purpose of the Study:

  • Introduce the Simulated Environment for Neurocognitive State Evaluation (SENSE-42) dataset.
  • Facilitate research on tonic alertness and neurocognitive state monitoring in computer users.
  • Provide a rich, multimodal dataset for analyzing user behavior and physiological responses.

Main Methods:

  • Collected 2-hour, multimodal recordings from 42 participants interacting with a simulated desktop environment.

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  • Acquired physiological data (EEG, ECG, respiration) and subjective alertness reports (Karolinska Sleepiness Scale, NASA-TLX).
  • Recorded behavioral data (keyboard, mouse, webcam) and gathered demographic/sleep quality information (PSQI, ESS).

    • Main Results:

    • The SENSE-42 dataset comprises high-resolution physiological, subjective, and behavioral data.
    • It captures spontaneous neurocognitive state fluctuations during routine computer tasks.
    • Includes detailed user characteristics and sleep quality metrics.

    Conclusions:

    • SENSE-42 offers a valuable resource for advancing user state monitoring.
    • The dataset supports research in behavioral analysis and physiological computing.
    • Enables deeper understanding of neurocognitive dynamics during computer interaction.