… -based algorithms like the simulated Kalman filter (SKF) [15]. … and feature selection for the
peak classification of EEG signals [… The data consist of binary and multiclass data, whereby the …

… optimal selection of a maximum of five features with a sequential feature selection method
and using … It has been a useful tool to analyze EEG signals, and given the difference of power …

… of features for use in the detection of a seizure, the EEG data … The EM process is an iterative
method that finds the maximum … research uses a wrapper feature selection process, which …

… predict the binary (left versus right) classification performance (… the maximum classification
accuracy for fNIRS signal happens… synthetic data, and our simulated working sessions use a 5 …

… series of discrete extended-Kalman filters and forks study the … represent the limit to which the
peaks and troughs of a wave … As compared with existing band feature selection method, the …

… using the performance parameters such as SNR, Peak SNR (… improve the SNR of the EEG
signals. To the knowledge of the … analyzed the approaches on simulated and real EEG data. …

… The feature selection process is done based on the best … In the post-processing step, the
Kalman filter is used to smooth … by simulating on MATLAB and comparing the real patient data …

… it can keep the EEG features to the maximum extent. Torabi et al. … for feature selection for
the same problem, while it is selected … simulating the noises which will corrupt the original EEG …

… In this work, several pre-processing filters have been tested on EEG data. An attempt has …
Feature selection using binary simulated Kalman filter for peak classification of EEG signals. …

… to artifact elimination from EEG signal is challenging and … with a validated simulation model
on the recorded EEG signal. In … Unlike cerebral activities, ocular waves have sharper peaks …