Beyond Discrete Features: Functional Analysis of Event-Related Potentials

Event-Related Potentials (ERPs) studies are powerful and widespread tools in neuroscience. The standard pipeline foresees the individuation of relevant components, and the computation of discrete features characterizing them, as latency and amplitude. Nonetheless, this approach only evaluates one aspect of the signal at a time, without considering its overall morphology; consequently being highly susceptible to low signal to noise ratio. In this context, we resort to Functional Data Analysis: a statistical methodology designed for the examination of curves and functions. Treating functions as statistical units enables the extraction of features that encompass the complete signal morphology. Functional Principal Component Analysis addresses whole ERPs as statistical units, allowing for the extraction of interpretable and comprehensive features. Exploiting this method, we compute three functional features from ERPs registered during an image categorization task. To validate our approach, firstly we examine the correlation between functional and discrete features to address the amount of overlapping information, and we consider the consistency of the obtained insights with previous literature. Moreover, we assess the effectiveness of our method by evaluating the classification performance achieved when using our extracted features to identify the object observed during the ERP recording. Such performance is compared to state-of-the-art feature extraction methods, using multiple metrics, classification algorithms, and datasets. The functional features consistently perform better, or analogously, across metrics, algorithms, and datasets they also embed additional information and provide insights coherent with previous literature, proving the usefulness of Functional Data Analysis in the context of ERP studies.