[Electroencephalogram analysis using normalized empirical functions for distributing probabilities]

Area of Science:

• Neuroscience
• Biophysics
• Signal Processing

Context:

• The central nervous system's functional state is crucial for diagnosing neurological conditions.
• Current diagnostic methods often rely on complex analyses or subjective interpretations.

Purpose:

• To establish a quantitative basis for evaluating central nervous system function.
• To develop novel numerical indices for improved diagnostic accuracy.

Summary:

• This research details the methodology for obtaining numerical indices through secondary processing of electroencephalogram (EEG) spectra.
• Normalized empirical functions of probability distribution are utilized to derive these indices.
• The derived indices provide a quantifiable measure of the central nervous system's functional state.

Impact:

• Offers a potentially more objective and efficient method for assessing neurological function.
• May aid in the early detection and monitoring of central nervous system disorders.
• Provides a foundation for further research into EEG-based diagnostic tools.