Off-line removal of TMS-induced artifacts on human electroencephalography by Kalman filter

Fabio Morbidi, Andrea Garulli, Domenico Prattichizzo, Cristiano Rizzo, Paolo Manganotti, Simone Rossi

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper we present an off-line Kalman filter approach to remove transcranial magnetic stimulation (TMS)-induced artifacts from electroencephalographic (EEG) recordings. Two dynamic models describing EEG and TMS signals generation are identified from data and the Kalman filter is applied to the linear system arising from their combination. The keystone of the approach is the use of time-varying covariance matrices suitably tuned on the physical parameters of the problem that allow to model the nonstationary components of the EEG-TMS signal. This guarantees an efficient deletion of TMS-induced artifacts while preserving the integrity of EEG signals around TMS impulses. Experimental results show that the Kalman filter is more effective than stationary filters (Wiener filter) for the problem under investigation.

Original languageEnglish
Pages (from-to)293-302
Number of pages10
JournalJournal of Neuroscience Methods
Volume162
Issue number1-2
DOIs
Publication statusPublished - May 15 2007

Keywords

  • EEG-TMS combination
  • Electroencephalography
  • Kalman filter
  • TMS-induced artifact
  • Transcranial magnetic stimulation

ASJC Scopus subject areas

  • Neuroscience(all)

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