[Brain Vigilance Analysis Based on the Measure of Complexity]

Area of Science:

• Neuroscience
• Cognitive Psychology
• Signal Processing

Background:

• Vigilance, the sustained ability to maintain attention, is crucial for performance in many tasks.
• Understanding the neural correlates of vigilance variation is essential for optimizing work processes and preventing errors.
• Electroencephalogram (EEG) provides a window into brain activity, but effective analysis methods for vigilance are needed.

Purpose of the Study:

• To investigate the dynamic changes in brain vigilance during cognitive tasks.
• To evaluate Permutation Entropy (PE) as a measure of EEG changes related to vigilance.
• To compare the efficacy of PE with Sample Entropy (SE) for vigilance assessment.

Main Methods:

• Subjects performed an addition/subtraction task designed to alter vigilance levels.
• A Psychomotor Vigilance Task (PVT) was used in conjunction with EEG recording.
• EEG data from 11 subjects were analyzed using Permutation Entropy (PE) and Sample Entropy (SE).

Main Results:

• Permutation Entropy (PE) demonstrated a strong ability to reflect dynamic EEG changes as vigilance decreased.
• PE exhibited advantages including rapid computation, high noise immunity, and minimal requirements for EEG data length.
• A comparison indicated PE's suitability as a brain vigilance indicator.

Conclusions:

• Permutation Entropy (PE) is a viable and advantageous method for quantifying brain vigilance.
• The findings support the use of PE in analyzing EEG for vigilance monitoring in various applications.
• PE offers a robust and efficient tool for neuroscientific research on attention and cognitive load.