Real-time detection of event-related brain activity

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Real-time detection of event-related brain activity
Authors: Gerwin Schalk, Eric C. Leuthardt, Peter Brunner, Jeffrey G. Ojemann, Lester A. Gerhardt, Jonathan R. Wolpaw
Citation: NeuroImage 43 (2): 245-249. 2008 November
Database(s): Google Scholar cites PubMed (PMID/18718544)
DOI: 10.1016/j.neuroimage.2008.07.037.
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Article: BASE Google Scholar PubMed
Restricted: DTU Digital Library
Other: NIF
Format: BibTeX Template from PMID

Real-time detection of event-related brain activity describes a systems called SIGFRIED (SIGnal modeling For Real-time Identification and Event Detection) for real-time analysis of intracranial electrocorticographic signals recorded from an array.

SIGFRIED is also described in Brain-computer interfaces (BCIs): detection instead of classification, but applied for EEG signals.

[edit] Method

  • Electrocorticography:
    • bandpass filtered (0.1–220 Hz)
    • sampled at 500 Hz
    • windowed with 400 ms
    • Frequency domain conversion with the maximum entropy method with model order of 25
    • different frequency bands when inputed to SIGFRIED
  • Subject instructions/conditions:
    • Gaussian mixture model estimated with Competitive-EM modeling (for each time point t) the vector of frequency domain amplitudes based on the data collected during resting.
    • Computation fo the likelihood that new data "was produced by the resting signal distribution"
  • Visualization with SUMA from AFNI.

The developed a plugin SIGFRIED plugin for the BCI2000 system.

[edit] Related papers

  1. Brain-computer interfaces (BCIs): detection instead of classification
  2. Brain-computer interfaces using electrocorticographic signals
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