Investigations on certain energy efficient algorithms for wireless electroencephalography sensor networks

Abstract

Electroencephalography is a non-invasive technique that provides essential monitoring of brain signals. With significant advancement in Wireless EEG Sensor Network, there exist several technique to track, interpret and evaluate the low-power wireless EEG networks. These methods offers extreme reduction in monitoring the signals and detecting the artifacts present in the EEG signal even in high frequencies. However most of the technique suffers from higher energy consumption, increased communication cost and increased signal to noise ratio. Most of the distributed algorithms helps in optimal reduction of these constraints using its multi-channel algorithms. With such motivation, the present study aims to remove the presence of eye blink artifacts in WESN channels with bandwidth as its major constraint. The study also develops a distributed solution that helps in exploitation of spatio-temporal correlation in various modules with energy and severe bandwidth as its constraint in WESN. It is further designed to reduce the consumption of energy by proper removal of eye blink artifacts from the EEG signals, thereby reducing the presence of noise in the EEG signals. The present study aims at the removal of eye blink artifact in EEG signal by the process of spatial filtering over WESN channels. The study mainly aims at removal of artifacts with bandwidth as its constraints at each channel, and the removal of artifacts is carried out using spatio-temporal correlation structure. In this study two different methods are adopted to remove the artifacts from EEG signals during the pre-processing operation prior the interpretation of EEG signals, which includes: Hessian Multi-Set Canonical Correlation and Hierarchical Fully-Connected Topology and Ad-Hoc Nearest Neighbour Topology. newline