A Kalman filter based methodology for EEG spike enhancement
Computer methods and programs in biomedicine, 2007•Elsevier
In this work, we present a methodology for spike enhancement in electroencephalographic
(EEG) recordings. Our approach takes advantage of the non-stationarity nature of the EEG
signal using a time-varying autoregressive model. The time-varying coefficients of
autoregressive model are estimated using the Kalman filter. The results show considerable
improvement in signal-to-noise ratio and significant reduction of the number of false
positives.
(EEG) recordings. Our approach takes advantage of the non-stationarity nature of the EEG
signal using a time-varying autoregressive model. The time-varying coefficients of
autoregressive model are estimated using the Kalman filter. The results show considerable
improvement in signal-to-noise ratio and significant reduction of the number of false
positives.
In this work, we present a methodology for spike enhancement in electroencephalographic (EEG) recordings. Our approach takes advantage of the non-stationarity nature of the EEG signal using a time-varying autoregressive model. The time-varying coefficients of autoregressive model are estimated using the Kalman filter. The results show considerable improvement in signal-to-noise ratio and significant reduction of the number of false positives.
Elsevier
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