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Updated: Jun 6, 2026

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings
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Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

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Finding stationary brain sources in EEG data.

Paul von Bunau1, Frank C Meinecke, Simon Scholler

  • 1TU Berlin (Berlin Institute of Technology), Dept. Computer Science, Franklinstr. 28/29, 10587, Germany. buenau@cs.tu-berlin.de

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
Summary
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Stationary Subspace Analysis (SSA) identifies stable brain activity from electroencephalography (EEG) signals. Focusing on these stationary sources enhances brain-computer interface (BCI) performance and reveals spatial brain characteristics.

Area of Science:

  • Neuroscience
  • Signal Processing
  • Biomedical Engineering

Background:

  • Neurophysiological measurements like EEG and fMRI are non-stationary due to time-varying brain processes.
  • Brain-Computer Interfaces (BCI) performance often degrades because calibration parameters become suboptimal during application.
  • Changes in user state (e.g., fatigue) or experimental conditions contribute to non-stationarity in brain signals.

Purpose of the Study:

  • To investigate the utility of Stationary Subspace Analysis (SSA) for analyzing high-dimensional EEG data.
  • To determine if SSA can differentiate between stationary and non-stationary brain sources.
  • To assess the impact of utilizing stationary brain sources on BCI performance.

Main Methods:

  • Applied Stationary Subspace Analysis (SSA), a time series analysis technique, to high-dimensional EEG measurements.

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Related Experiment Videos

Last Updated: Jun 6, 2026

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings
08:20

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

  • Identified distinct stationary and non-stationary brain sources using SSA.
  • Restricted BCI operation to the stationary sources identified by SSA.

    • Main Results:

    • SSA successfully identified underlying stationary and non-stationary brain sources from EEG data.
    • Restricting BCI to stationary sources significantly improved performance (bitrate).
    • SSA generated topographic maps highlighting the spatial characteristics of both stationary and non-stationary sources.

    Conclusions:

    • Stationary Subspace Analysis is effective for decomposing non-stationary EEG signals into stationary and non-stationary components.
    • Leveraging stationary brain sources via SSA offers a promising strategy to enhance BCI performance and robustness.
    • SSA provides valuable insights into the spatial localization of different brain source dynamics.