Determination of neural state classification metrics from the power spectrum of human ECoG.

Matthew Kelsey, David Politte, Ryan Verner, John M. Zempel, Tracy Nolan, Abbas Babajani-Feremi, Fred Prior, Linda J. Larson-Prior

Research output: Contribution to journalArticle

Abstract

Brain electrical activity exhibits scale-free dynamics that follow power law scaling. Previous works have shown that broadband spectral power exhibits state-dependent scaling with a log frequency exponent that systematically varies with neural state. However, the frequency ranges which best characterize biological state are not consistent across brain location or subject. An adaptive piecewise linear fitting solution was developed to extract features for classification of brain state. Performance was evaluated by comparison to an a posteriori based feature search method. This analysis, using the 1/f characteristics of the human ECoG signal, demonstrates utility in advancing the ability to perform automated brain state discrimination.

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Power spectrum
Brain
Scaling laws

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

Determination of neural state classification metrics from the power spectrum of human ECoG. / Kelsey, Matthew; Politte, David; Verner, Ryan; Zempel, John M.; Nolan, Tracy; Babajani-Feremi, Abbas; Prior, Fred; Larson-Prior, Linda J.

In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 01.01.2012, p. 4336-4340.

Research output: Contribution to journalArticle

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