A MultiModal Vigilance (MMV) dataset during RSVP and SSVEP brain-computer interface tasks.

Area of Science:

• Neuroscience and Cognitive Science
• Biomedical Engineering
• Data Science

Background:

• Sustained attention, or vigilance, is critical for task reliability and performance.
• Existing datasets may not fully capture the complexity of vigilance across diverse tasks.
• Brain-Computer Interface (BCI) research requires robust physiological data for performance analysis.

Purpose of the Study:

• To introduce and describe the MultiModal Vigilance (MMV) dataset.
• To provide a comprehensive resource for research on vigilance and physiological signal analysis.
• To facilitate the development of advanced vigilance estimation techniques.

Main Methods:

• Acquisition of seven physiological signals: EEG, EOG, ECG, PPG, EDA, EMG, and eye movement.
• Data collected during two BCI tasks: RSVP and SSVEP.
• Dataset organized into raw, pre-processed, trial, and feature data stages for 18 subjects over four sessions.

Main Results:

• The MMV dataset offers a rich, multi-faceted resource for vigilance research.
• Data is structured to support direct use in vigilance estimation algorithms.
• The dataset encompasses a wide range of physiological signals relevant to cognitive states.

Conclusions:

• The MMV dataset is a valuable contribution to the field of physiological signal-based vigilance research.
• It enables flexible reuse and caters to diverse research needs in BCI and cognitive monitoring.
• Facilitates advancements in understanding and estimating human vigilance.