Identifying task-relevant spectral signatures of perceptual categorization in the human cortex.

ABSTRACT: Human brain has developed mechanisms to efficiently decode sensory information according to perceptual categories of high prevalence in the environment, such as faces, symbols, objects. Neural activity produced within localized brain networks has been associated with the process that integrates both sensory bottom-up and cognitive top-down information processing. Yet, how specifically the different types and components of neural responses reflect the local networks' selectivity for categorical information processing is still unknown. In this work we train Random Forest classification models to decode eight perceptual categories from broad spectrum of human intracranial signals (4-150 Hz, 100 subjects) obtained during a visual perception task. We then analyze which of the spectral features

SUBMITTER: Kuzovkin I 

PROVIDER: S-EPMC7217881 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature