A method for detection and classification of events in neural activity

Hemant S. Bokil, Bijan Pesaran, Richard A. Andersen, Partha P. Mitra

Research output: Contribution to journalArticlepeer-review


We present a method for the real time prediction of punctuate events in neural activity, based on the time-frequency spectrum of the signal, applicable both to continuous processes like local field potentials (LFPs) as well as to spike trains. We test it on recordings of LFP and spiking activity acquired previously from the lateral intraparietal area (LIP) of macaque monkeys performing a memory-saccade task. In contrast to earlier work, where trials with known start times were classified, our method detects and classifies trials directly from the data. It provides a means to quantitatively compare and contrast the content of LFP signals and spike trains: we find that the detector performance based on the LFP matches the performance based on spike rates. The method should find application in the development of neural prosthetics based on the LFP signal. Our approach uses a new feature vector, which we call the 2d cepstrum.

Original languageEnglish (US)
Article number1658163
Pages (from-to)1678-1687
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Issue number8
StatePublished - Aug 2006


  • Cepstral analysis
  • Decoding
  • Multitaper spectral analysis
  • Nervous system
  • Prediction methods

ASJC Scopus subject areas

  • Biomedical Engineering


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