H26: Deciphering natural cortical representations for building BMI

Paper Submission for H26

SMC2018:H26 submission site (external site)

Abstracts

Our natural experiences in daily environments (e.g., driving a car in a crowded city) involve processing of complex and dynamic sensory inputs and realization of rich and accurate motor outputs. Recent advancements in brain-activity measurement and machine learning techniques allow studying of the cortical representations underlying such natural and complex perceptions and actions. Quantitative understanding of rich representations opens the possibility for versatile brain-machine interfaces (BMI) both in sensory and motor domains, such as decoding of perceptual contents and cognitive states under naturalistic conditions, communicating via visual and semantic imaginations, and robust estimations of complex motor intentions. This special session aims to discuss recent developments in understanding natural cortical representations that could be a quantitative basis for building BMI.

Session Chairs

  • Shinji Nishimoto (nishimoto@nict.go.jp),
    National Institute of Information and Communications Technology, Japan