• DocumentCode
    1017795
  • Title

    Spatiotemporal Linear Decoding of Brain State

  • Author

    Parra, Lucas C. ; Christoforou, Christoforos ; Gerson, Adam D. ; Dyrholm, Mads ; Luo, An ; Wagner, Mark ; Philiastides, Marios ; Sajda, Paul

  • Volume
    25
  • Issue
    1
  • fYear
    2008
  • fDate
    6/30/1905 12:00:00 AM
  • Firstpage
    107
  • Lastpage
    115
  • Abstract
    This review summarizes linear spatiotemporal signal analysis methods that derive their power from careful consideration of spatial and temporal features of skull surface potentials. BCIs offer tremendous potential for improving the quality of life for those with severe neurological disabilities. At the same time, it is now possible to use noninvasive systems to improve performance for time-demanding tasks. Signal processing and machine learning are playing a fundamental role in enabling applications of BCI and in many respects, advances in signal processing and computation have helped to lead the way to real utility of noninvasive BCI.
  • Keywords
    brain; computer interfaces; decoding; learning (artificial intelligence); medical signal processing; neurophysiology; spatiotemporal phenomena; BCI; brain; machine learning; neurological disabilities; skull surface potentials; spatiotemporal linear decoding; Analysis of variance; Brain computer interfaces; Decoding; Electrodes; Electroencephalography; Scalp; Signal analysis; Skull; Spatiotemporal phenomena; Surface discharges;
  • fLanguage
    English
  • Journal_Title
    Signal Processing Magazine, IEEE
  • Publisher
    ieee
  • ISSN
    1053-5888
  • Type

    jour

  • DOI
    10.1109/MSP.2008.4408447
  • Filename
    4408447