Using complexity measure to characterize information transmission of human brain cortex

Area of Science:

• Neuroscience
• Computational Neuroscience
• Information Theory

Background:

• Understanding brain function relies on analyzing communication between cortical regions.
• Electroencephalography (EEG) provides a valuable tool for measuring electrical activity in the brain.

Purpose of the Study:

• To compute and characterize information transmission between cortical areas.
• To investigate the relationship between brain signal complexity and functional states.

Main Methods:

• Utilized the theory of mutual information to quantify information flow.
• Analyzed electroencephalogram (EEG) time series data from normal human subjects.
• Applied "complexity" measures to characterize transmission intensities.

Main Results:

• Successfully computed information transmission patterns across cortical regions.
• Demonstrated that complexity measures are sensitive indicators of brain function.
• Established a link between signal transmission complexity and human functional conditions.

Conclusions:

• Mutual information and complexity measures are effective tools for analyzing brain connectivity.
• Complexity of neural signals is a key determinant of human functional states.
• This approach offers potential for assessing neurological health and function.