Kernel-Based Relevance Analysis with Enhanced Interpretability for Detection of Brain Activity Patterns.

ABSTRACT: We introduce Enhanced Kernel-based Relevance Analysis (EKRA) that aims to support the automatic identification of brain activity patterns using electroencephalographic recordings. EKRA is a data-driven strategy that incorporates two kernel functions to take advantage of the available joint information, associating neural responses to a given stimulus condition. Regarding this, a Centered Kernel Alignment functional is adjusted to learning the linear projection that best discriminates the input feature set, optimizing the required free parameters automatically. Our approach is carried out in two scenarios: (i) feature selection by computing a relevance vector from extracted neural features to facilitating the physiological interpretation of a given brain activity task, and (ii) enhanced fea

SUBMITTER: Alvarez-Meza AM 

PROVIDER: S-EPMC5635061 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature