Abstract
We present a methodology for isolating the underlying seizure activity from the scalp EEG recorded during seizure onset in a number of patients. We use the method of Independent Component Analysis (ICA) to isolate both the temporal and spatial aspects of the seizure activity. Seizure related activity in the independent components is identified through visually analysing the spatio-temporal components along with the spectrogram of the temporal components. Subjectively, in four seizure EEGs analysed we can identify what appear to be the relevant seizure components. Furthermore, artifactual components are isolated from the seizure activity. This means that the scalp EEG can either be ‘remapped’ using only the identified seizure components, or further analysis on the seizure can be undertaken on the spatio-temporal components directly. Although subjective, the preliminary results indicate that ICA can be of benefit on pre-processing the epileptiform EEG for further analysis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Comon P. Independent component analysis, a new concept? Signal Processing, 1994; 36: 287–314
Jutten C, Herault J. Blind separation of sources part I: An adaptive algorithm based on neuromimetic architecture. Signal Processing, 1991; 24:1–10
Vigario RN. Extraction of ocular artefacts from EEG using independent component analysis. Elctroenceph. clin. Neurophysiol., 1997; 103:395–404
James CJ, Kobayashi K, Lowe D. Isolating Epileptiform Discharges in the Unaveraged EEG using Independent Component Analysis. Medical Applications of Signal Processecing Colloquium, IEE, London, 6th October, 1999
Kobayashi K, James CJ, Nakahori T, Akiyama T, Gotman J. Isolation of epileptiform discharges from unaveraged EEG by independent component analysis. Electroenceph. clin. Neurophysiol. 1999; 110:1755–1763
Bell AJ, Sejnowski T.J. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 1995; 7:1129–1159
Makeig S, Jung TP, Bell AJ, Ghahremani D, Sejnowski TJ. Blind separation of auditory event-related brain responses into independent components. Proc. Natl. Acad. Sci. USA, 1997; 94:10979–10984
Hyvärinen A, Oja E. A fast fixed-point algorithm for independent component analysis. Neural Computation, 1997; 9:1483–1492
The FastICA MATLAB Package. Available at http://www.cis.hut.fi/projects/ica/fastica/, 1998
Nunez PL, Katznelson RD. Electric Fields of the Brain: The Neurophysics of EEG. New York: Oxford University Press, 1981
Scherg M, Von Cramon D. Two bilateral sources of the late AEP as identified by a spatio-temporal dipole model. Electroenceph. clin. NeurophysioL, 1985; 62:32–44
Lagerlund TD, Sharbrough FW, Jack CR, Erickson BJ, Strelow DC, Cicora KM, Busacker NE. Determination of 10-20 system electrode locations using magnetic resonance image scanning with markers. Electroenceph. clin. NeurophysioL, 1993; 86:7–14
Achim A, Richer F, Saint-Hilaire JM. Methodological considerations for the evaluation of spatio-temporal source models. Electroenceph. clin. NeurophysioL, 1991; 79:227–240
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag London
About this paper
Cite this paper
James, C., Lowe, D. (2000). Isolating Seizure Activity in the EEG with Independent Component Analysis. In: Malmgren, H., Borga, M., Niklasson, L. (eds) Artificial Neural Networks in Medicine and Biology. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0513-8_19
Download citation
DOI: https://doi.org/10.1007/978-1-4471-0513-8_19
Publisher Name: Springer, London
Print ISBN: 978-1-85233-289-1
Online ISBN: 978-1-4471-0513-8
eBook Packages: Springer Book Archive