[On the method of practical symbolic dynamics for EEG analysis]

Area of Science:

• Neuroscience
• Nonlinear Dynamics
• Biomedical Signal Processing

Background:

• Electroencephalogram (EEG) signal analysis is crucial for understanding brain activity.
• Symbolic entropy provides a reliable measure of nonlinear signal complexity.
• Existing methods for EEG symbolization may have limitations.

Purpose of the Study:

• To introduce a novel difference symbolization method for EEG signal analysis.
• To evaluate the effectiveness of symbolic entropy using this new method.
• To assess the capability of the method in distinguishing different physiological states of EEG data.

Main Methods:

• Proposed a new difference symbolization technique for EEG signals.
• Analyzed EEG signal dynamics on the tangent space using the proposed method.
• Calculated and compared symbolic entropy values based on the difference symbolization parameter.
• Applied comparative analysis to EEG datasets from different physiological states.

Main Results:

• The difference symbolization method effectively analyzes EEG signal dynamics.
• Symbolic entropy values varied significantly across different physiological states.
• The method successfully distinguished EEG complexity between normal and epileptic subjects.
• The method also differentiated between eyes-open and eyes-closed states.

Conclusions:

• Difference symbolization is a promising new direction for EEG analysis.
• Symbolic entropy, using this method, offers objective criteria for EEG evaluation.
• The approach facilitates fine quantitative analysis of EEG signals.
• This method holds significance for clinical applications and research in neuroscience.