In peak classification of electroencephalogram (EEG) signals, angle modulated simulated
Kalman filter (AMSKF) and binary simulated Kalman filter (BSKF) algorithms have been …

Previously, an angle modulated simulated Kalman filter (AMSKF) algorithm has been
implemented for feature selection in peak classification of electroencephalogram (EEG) …

In the existing electroencephalogram (EEG) signals peak classification research, the
existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of …

This paper proposes a robust algorithm for adaptive modelling of EEG signal classification
using a modified Extended Kalman Filter (EKF). This modified EKF combines Radial Basis …

In previous paper, we proposed the novel method of nonlinear unsupervised feature
classification for EEG (Electroencephalography) signal based on HS (Harmony Search) …

With recent advances in signal processing and biomedical instrumentation, EEG 1 signals
can be used as a new communication channel between human and computers …

Artificial neural network approaches for classification of EEG signals using the widely known
back-propagation algorithm to train the network are reported in the literature. However, the …

The employment of peak detection algorithm is prominent in several clinical applications
such as diagnosis and treatment of epilepsy patients, assisting to determine patient …

Brain-computer interface (BCI) systems enable individuals to use a computer or assistive
technologies such as a neuroprosthetic arm by translating their brain electrical activity into …

For years the Extended Kalman Filter (EKF) has been the algorithm for non-linear systems
due to its simplicity and suitability to real time implementations. Because of its shortfalls …