APPLESEED

APPLESEED computes multiscale entropy (MSE) from electroencephalogram (EEG) data to quantify scale-wise signal irregularity and complexity for neuroscientific analyses.

Key Features:

• Multiscale Entropy Analysis: Computes multiscale entropy (MSE) to quantify EEG signal irregularity across multiple temporal scales.
• Standardized Preprocessing Pipeline: Implements a preprocessing pipeline that standardizes EEG data preparation prior to entropy estimation.
• Developmental and Cognitive Metrics: Produces scale-wise entropy estimates that reflect developmental stage and cognitive state differences in EEG signal complexity.

Scientific Applications:

• Developmental Neuroscience: Characterizing developmental changes in EEG signal complexity and identifying entropy-based biomarkers of developmental outcomes.
• Cognitive Processing Studies: Investigating neural mechanisms of cognition by analyzing EEG signal variability across temporal scales.
• Psychopathology Research: Comparing entropy-derived measures of nonlinearity and complexity to study alterations associated with psychological disorders.

Methodology:

Preprocessing and analysis explicitly include data cleaning (removal of artifacts and noise), signal segmentation (division into temporal segments), and application of multiscale entropy (MSE) to each segment.