TF-PCA

TF-PCA performs data reduction on time-frequency representations of electroencephalography (EEG) data to extract principal components that characterize temporal and spectral patterns without imposing strict a priori boundaries.

Key Features:

• Data reduction via PCA: Reduces dimensionality of time-frequency EEG representations while preserving essential time-frequency characteristics.
• Flexibility across developmental stages: Accommodates dynamic changes in timing and frequency observed in developmental EEG data.
• No strong a priori constraints: Operates without requiring predefined temporal or spectral boundaries, enabling exploratory detection of effects.

Scientific Applications:

• Developmental EEG analysis: Identifying time-frequency components that vary across developmental stages in neurocognitive research.
• Exploratory neurocognitive studies: Discovering novel temporal and spectral patterns in EEG across different populations without predefined windows.

Methodology:

Applies principal components analysis (PCA) to time-frequency representations of EEG data to perform dimensionality reduction and extract components without predefined temporal or spectral boundaries.