Feature selection methods for zero-shot learning of neural activity

Carlos A. Caceres, Matthew J. Roos, Kyle M. Rupp, Griffin Milsap, Nathan E. Crone, Michael E. Wolmetz, Christopher R. Ratto

Research output: Contribution to journalArticlepeer-review

Abstract

Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows) have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set. While choices about feature selection can have a substantial impact when closed-set accuracy, open-set robustness, and runtime are competing design objectives, no systematic study of feature selection for these models has been reported. Instead, a relatively straightforward feature stability approach has been adopted and successfully applied across models and imaging modalities. To characterize the tradeoffs in feature selection for zero-shot learning, we compared correlation-based stability to several other feature selection techniques on comparable data sets from two distinct imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. While most of the feature selection methods resulted in similar zero-shot prediction accuracies and spatial/spectral patterns of selected features, there was one exception; A novel feature/attribute correlation approach was able to achieve those accuracies with far fewer features, suggesting the potential for simpler prediction models that yield high zero-shot classification accuracy.

Original languageEnglish (US)
Article number41
JournalFrontiers in Neuroinformatics
Volume11
DOIs
StatePublished - Jun 23 2017

Keywords

  • BCI
  • Electrocorticography
  • Feature selection
  • Semantics
  • Transfer learning
  • Zero-shot learning
  • fMRI

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Biomedical Engineering
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Feature selection methods for zero-shot learning of neural activity'. Together they form a unique fingerprint.

Cite this