Use this URL to cite or link to this record in EThOS:
Title: Electroencephalogram signal acquisition in unshielded noisy environment
Author: Fatoorechi, Mohsen
ISNI:       0000 0004 5357 4059
Awarding Body: University of Sussex
Current Institution: University of Sussex
Date of Award: 2015
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Researchers have used electroencephalography (EEG) as a window into the activities of the brain. High temporal resolution coupled with relatively low cost compares favourably to other neuroimaging techniques such as magnetoencephalography (MEG). For many years silver metal electrodes have been used for non-invasive monitoring electrical activities of the brain. Although these electrodes provide a reliable method for recording EEG they suffer from noise, such as offset potentials and drifts, and usability issues, e.g. skin prepa- ration and short circuiting of adjacent electrodes due to gel running. Low frequency noise performance is the key indicator in determining the signal to noise ratio of an EEG sensor. In order to tackle these issues a prototype Electric Potential Sensor (EPS) device based on an auto-zero operational amplifier has been developed and evaluated. The absence of 1/f noise in these devices makes them ideal for use with signal frequencies ~10Hz or less. The EPS is a novel active electrode electric potential sensor with ultrahigh input impedance. The active electrodes are designed to be physically and electrically robust and chemically and biochemically inert. They are electrically insulated (anodized) and scalable. These sensors are designed to be immersed in alcohol for sterilization purposes. A comprehensive study was undertaken to compare the results of EEG signals recorded by the EPS with different commercial systems. These studies comprised measurements of both free running EEG and Event Related Potentials. Strictly comparable signals were observed with cross correlations of higher than 0.9 between the EPS and other systems.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: RC0346 Neurology. Diseases of the nervous system Including speech disorders