Capturing dynamic patterns of task-based functional connectivity with EEG

Nader Karamzadeh, Andrei Medvedev, Afrouz Azari, Amir Gandjbakhche, Laleh Najafizadeh

Research output: Contribution to journalArticlepeer-review

57 Scopus citations


A new approach to trace the dynamic patterns of task-based functional connectivity, by combining signal segmentation, dynamic time warping (DTW), and Quality Threshold (QT) clustering techniques, is presented. Electroencephalography (EEG) signals of 5 healthy subjects were recorded as they performed an auditory oddball and a visual modified oddball tasks. To capture the dynamic patterns of functional connectivity during the execution of each task, EEG signals are segmented into durations that correspond to the temporal windows of previously well-studied event-related potentials (ERPs). For each temporal window, DTW is employed to measure the functional similarities among channels. Unlike commonly used temporal similarity measures, such as cross correlation, DTW compares time series by taking into consideration that their alignment properties may vary in time. QT clustering analysis is then used to automatically identify the functionally connected regions in each temporal window. For each task, the proposed approach was able to establish a unique sequence of dynamic pattern (observed in all 5 subjects) for brain functional connectivity.

Original languageEnglish (US)
Pages (from-to)311-317
Number of pages7
StatePublished - Feb 1 2013

All Science Journal Classification (ASJC) codes

  • Neurology
  • Cognitive Neuroscience


  • Clustering analysis
  • Electroencephalography (EEG)
  • Functional connectivity


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