Laser Upgrade for Enhanced Brain Imaging During Multisensory Decisions

Abstract

This request was made by an individual investigator who is a member of a Multiple University Research Initiative (MURI; Army Research Office under contract no. W911NF-16-1-0368 as part of the collaboration between the US DOD, the UK MOD and the UK Engineering and Physical Research Council under the Multidisciplinary University Research Initiative). The goals of this interdisciplinary effort are to develop new methodologies for modeling multi-modal neural activity underlying multisensory processing and decision making, and to use those methodologies to design closed-loop adaptive algorithms for optimized exploitation of multisensory data for brain-computer communication under various operational scenarios. We are motivated by the observation that military personnel routinely make decisions in time-pressured and stressful conditions based on a multiplicity of multisensory information. We aim to develop a closed-loop brain-computer interface (BCI) architecture for enhancing performance; the architecture will collect multi-modal neural, physiological (e.g., heart rate), and behavioral (e.g., facial expressions) information, decode mental states such as attention, orientation, multisensory evidence, and intended action, and use the decoded states as feedback to adaptively change the multisensory cues provided to the subject and to execute the intended action, thus closing the loop.

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Document Details

Document Type
Technical Report
Publication Date
Jul 29, 2022
Accession Number
AD1226797

Entities

People

  • Anne Churchland

Organizations

  • University of California, Los Angeles

Tags

Readers

  • Neural Network Machine Learning.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.
  • Technical Research and Report Writing.

Technology Areas

  • Directed Energy