Functional Data Analysis for Decoding Working Memory Representations from EEG Signals
Electroencephalography (EEG) provides high-temporal-resolution measurements of brain activity, enabling the study of cognitive processes such as working memory (WM). A central challenge is decoding WM content from noisy, high-dimensional, and temporally structured signals. We propose a functional data analysis (FDA) framework that models EEG signals as continuous functions over time to capture their temporal dynamics. The study is based on data from 20 children performing a task involving visual, spatial, and verbal information encoding. We compare the FDA-based approach with standard classification methods commonly used in EEG analysis. Results illustrate the ability of FDA to characterize time-resolved neural patterns associated with different WM contents, providing an interpretable statistical framework for complex neurophysiological data.
Palabras clave: FDA EEG decoding