Feature Extraction of EEG Signal upon BCI Systems Based on Steady-State Visual Evoked Potentials Using the Ant Colony Optimization Algorithm

S M Fernandez-Fraga,J C Pedraza-Ortega,M A Aceves-Fernandez,S Tovar-Arriaga

doi:10.1155/2018/2143873

Abstract

This work presents the use of swarm intelligence algorithms as a reliable method for the optimization of electroencephalogram signals for the improvement of the performance of the brain interfaces based on stable states visual events. The preprocessing of brain signals for the extraction of characteristics and the detection of events is of paramount importance for the improvement of brain interfaces. The proposed ant colony optimization algorithm presents an improvement in obtaining the key features of the signals and the detection of events based on visual stimuli. As a reference model, we used the Independent Component Analysis method, which has been used in recent research for the removal of nonrelevant and detection of relevant data from the brain’s electrical signals and also allows the collection of information in response to a stimulus and separates the signals that were generated independently in certain zones of the brain.

Highlights

A brain computer interface (BCI) enables direct communication between a brain and a computer translating brain activity into computer commands, providing no muscular interaction with the environment [1]
Extraction and feature selection steps are vital for the development of applications; the BCI systems based on steadystate visual evoked potentials (SSVEP) have the advantage over other BCI systems because they have a better signal-tonoise ratio (SNR) and faster transfer rate information (ITR) [2]
Algorithms based on swarm intelligence (SI) have recently emerged as a family-based wild populations of ant colony, bees, and swarm individuals algorithms which are capable of producing low give robust computational cost solutions to various complex and optimization problems [5, 6]

Summary

Introduction

A brain computer interface (BCI) enables direct communication between a brain and a computer translating brain activity into computer commands, providing no muscular interaction with the environment [1]. Extraction and feature selection steps are vital for the development of applications; the BCI systems based on steadystate visual evoked potentials (SSVEP) have the advantage over other BCI systems because they have a better signal-tonoise ratio (SNR) and faster transfer rate information (ITR) [2]. It does not require intensive training and requires fewer EEG channels for application development and immunity from artifacts (flicker, movement joints, etc.). Saidi et al (2004) showed that clustering in the ICA components obtained clusters biologically more reasonably clustered than clusters obtained by PCA (Principal Component Analysis)

Methods

Results

Conclusion