Skip to main content Accessibility help
×
Hostname: page-component-7c8c6479df-5xszh Total loading time: 0 Render date: 2024-03-28T19:54:38.944Z Has data issue: false hasContentIssue false

8 - Recent advances in EEG signal analysis and classification

Published online by Cambridge University Press:  06 October 2009

Richard Dybowski
Affiliation:
King's College London
Vanya Gant
Affiliation:
University College London Hospitals NHS Trust, London
Get access

Summary

Introduction

Electrical signals recorded from the scalp of human subjects, or electroencephalographic (EEG) signals, were first studied extensively by Berger (1929). Since these initial experiments, investigators in many branches of science, including physics, medicine, neuroscience, and psychology, have searched for meaningful patterns in EEG signals (Pilgreen 1995). For example, the analysis of patterns in EEG has for some time been extremely useful in the study and treatment of epilepsy (Kellaway & Petersen 1976).

Many of the traditional approaches to EEG pattern analysis that have been employed during the last six decades are based on visual inspection of graphs of voltage amplitude over time, or on the inspection of spectra showing the energy with which various frequencies appear in the signal. However, recent advances in signal analysis and classification using artificial neural networks have led to significant, new results in the filtering and interpretation of EEG signals. This chapter describes some of these new approaches as they are applied to EEG signals surrounding a response to a stimulus and to spontaneous EEG signals recorded while subjects perform mental tasks. Applications of these approaches include the study of sensory, motor and cognitive processing in the brain, and the development of brain–computer interfaces to provide a new avenue for communication with locked-in patients suffering from advanced anterolateral sclerosis (ALS).

Effect of attention on spectral dynamics of event related potentials

The event-related potential, or ERP, is simply the EEG recorded in response to a time-locked stimulus.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2001

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×