Given a spatial filtering algorithm that has allowed us to identify task-relevant EEG sources, we present a simple approach for monitoring the activity of these sources while remaining relatively robust to changes in other (task-irrelevant) brain activity. The idea is to keep spatial *patterns* fixed rather than spatial filters, when transferring from training to test sessions or from one time window to another. We show that a fixed spatial pattern (FSP) approach, using a moving-window estimate of signal covariances, can be more robust to non-stationarity than a fixed spatial filter (FSF) approach.
| Author(s): | Hill, NJ. and Farquhar, J. and Lal, TN. and Schölkopf, B. |
| Book Title: | Proceedings of the 3rd International Brain-Computer Interface Workshop and Training Course 2006 |
| Journal: | Proceedings of the 3rd International Brain-Computer Interface Workshop and Training Course 2006 |
| Pages: | 20-21 |
| Year: | 2006 |
| Month: | September |
| Day: | 0 |
| Editors: | GR M{\"u}ller-Putz and C Brunner and R Leeb and R Scherer and A Schl{\"o}gl and S Wriessnegger and G Pfurtscheller |
| Publisher: | Verlag der Technischen Universit{\"a}t Graz |
| Bibtex Type: | Conference Paper (inproceedings) |
| Address: | Graz, Austria |
| Event Name: | 3rd International Brain-Computer Interface Workshop and Training Course 2006 |
| Event Place: | Graz, Austria |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@inproceedings{4244,
title = {Time-Dependent Demixing of Task-Relevant EEG Signals},
journal = {Proceedings of the 3rd International Brain-Computer Interface Workshop and Training Course 2006},
booktitle = {Proceedings of the 3rd International Brain-Computer Interface Workshop and Training Course 2006},
abstract = {Given a spatial filtering algorithm that has allowed us to identify task-relevant EEG sources, we present a simple approach
for monitoring the activity of these sources while remaining relatively robust to changes in other (task-irrelevant) brain activity. The idea is to keep spatial *patterns* fixed rather than spatial filters, when transferring from
training to test sessions or from one time window to another. We show that a fixed spatial pattern (FSP)
approach, using a moving-window estimate of signal covariances, can be more robust to non-stationarity than a fixed spatial filter (FSF) approach.},
pages = {20-21},
editors = {GR M{\"u}ller-Putz and C Brunner and R Leeb and R Scherer and A Schl{\"o}gl and S Wriessnegger and G Pfurtscheller},
publisher = {Verlag der Technischen Universit{\"a}t Graz},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
address = {Graz, Austria},
month = sep,
year = {2006},
slug = {4244},
author = {Hill, NJ. and Farquhar, J. and Lal, TN. and Sch{\"o}lkopf, B.},
month_numeric = {9}
}