Spatial Brain Control Interface using Optical and Electrophysiological Measures

Abstract

Functional imaging and electrical recordings were performed over posterior parietal cortex during a spatial attention task. Subjects had to covertly shift their attention with simultaneous visual stimulation and motor planning. The imaging response showed modulation for spatial conditions. Various analysis and decoding methods were assessed to extract a prediction signature from this brain activity. The Linear Support Vector Machine (LSVM) was the most appropriate for implementing a reliable brain-computer interface (BCI). The LSVM method was applied to the imaging data with various temporal parameters. All variations proved to be suitable to predict experimental parameters (left vs. right eye movement) from the hemodynamic response over PPC. However, due to the slow hemodynamic signal, performance for the LSVM reached only ~60% during the later task phases. Thus, the subject s response could not be predicted reliably before the actual movement. Electrophysiological recordings (single unit and local field potentials) were performed in the previously imaged regions to allow comparison with the hemodynamic response. These electrical signals especially the local field potentials proved to be fast and strongly tuned for the spatial parameters of the task. Thus, a reliable BCI that can predict upcoming movements or behaviors will need to combine signals from various sources.

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Document Details

Document Type
Technical Report
Publication Date
Aug 27, 2013
Accession Number
ADA595608

Entities

People

  • Barbara Heider

Organizations

  • Rutgers University–Newark

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Brain-Computer Interfaces
  • Computers
  • Data Analysis
  • Decoding
  • Department Of Defense
  • Engineering
  • Firing Rate
  • Frequency
  • Imaging Techniques
  • Information Science
  • Magnetic Resonance
  • Standards
  • Statistical Analysis
  • Students
  • Supervised Machine Learning

Readers

  • Image Processing and Computer Vision.
  • Neuroscience
  • Vision Science/Vision Psychology/Cognitive Neuroscience.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference