Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals

Asrul Adam,Mohd Ibrahim Shapiai,Marizan Mubin,Zuwairie Ibrahim,Ismail Saad,Norrima Mokhtar

doi:10.1186/s40064-016-3277-z

Abstract

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 features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.

Highlights

The use of electroencephalogram (EEG) signals for measurements has become a growing interest in research for various applications such as brain-computer interface (NicolasAlonso and Gomez-Gil 2012), human–machine interface (Ramli et al 2015), diagnosing and monitoring epilepsy (Acir 2005), and tracking eye gaze (Adam et al 2014)
The first experiment aimed to investigate the classification performance of the individual Neural network with random weights (NNRW) under various number of hidden neurons. This experiment was evaluated the performance of the individual NNRW over the four existing peak models
The optimum number of hidden neurons was selected to perform the experiment of the proposed angle modulated simulated Kalman filter (AMSKF) technique

Summary

Introduction

The use of electroencephalogram (EEG) signals for measurements has become a growing interest in research for various applications such as brain-computer interface (NicolasAlonso and Gomez-Gil 2012), human–machine interface (Ramli et al 2015), diagnosing and monitoring epilepsy (Acir 2005), and tracking eye gaze (Adam et al 2014). A peak point is defined by a point that holds the highest value located at a specific time and location on EEG signals. A peak point can be observed in EEG signals because of the response of brain on human activities. Such responses of the brain on human activities that triggers a peak in EEG signals are eye movements, epilepsy, and event-related potentials.

Objectives

Methods

Results

Conclusion