Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
ANC schemes for the enhancement of EEG signals in the presence of EOG artifacts
Computers and Biomedical Research
Detection of spikes with artificial neural networks using raw EEG
Computers and Biomedical Research
Dynamic models for nonstationary signal segmentation
Computers and Biomedical Research
A Data Mining Based Approach for the EEG Transient Event Detection and Classification
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
EEG Transient Event Detection and Classification Using Association Rules
IEEE Transactions on Information Technology in Biomedicine
A measurement policy in stochastic linear filtering problems
Computers & Mathematics with Applications
Computers in Biology and Medicine
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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.