Towards zero training for brain-computer interfacing.
Electroencephalogram (EEG) signals are highly subject-specific and vary considerably even between recording sessions of the same user within the same experimental paradigm. This challenges a stable operation of Brain-Computer Interface (BCI) systems. The classical approach is to train users by neuro...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2008-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC2500157?pdf=render |
| _version_ | 1811207768399413248 |
|---|---|
| author | Matthias Krauledat Michael Tangermann Benjamin Blankertz Klaus-Robert Müller |
| author_facet | Matthias Krauledat Michael Tangermann Benjamin Blankertz Klaus-Robert Müller |
| author_sort | Matthias Krauledat |
| collection | DOAJ |
| description | Electroencephalogram (EEG) signals are highly subject-specific and vary considerably even between recording sessions of the same user within the same experimental paradigm. This challenges a stable operation of Brain-Computer Interface (BCI) systems. The classical approach is to train users by neurofeedback to produce fixed stereotypical patterns of brain activity. In the machine learning approach, a widely adapted method for dealing with those variances is to record a so called calibration measurement on the beginning of each session in order to optimize spatial filters and classifiers specifically for each subject and each day. This adaptation of the system to the individual brain signature of each user relieves from the need of extensive user training. In this paper we suggest a new method that overcomes the requirement of these time-consuming calibration recordings for long-term BCI users. The method takes advantage of knowledge collected in previous sessions: By a novel technique, prototypical spatial filters are determined which have better generalization properties compared to single-session filters. In particular, they can be used in follow-up sessions without the need to recalibrate the system. This way the calibration periods can be dramatically shortened or even completely omitted for these 'experienced' BCI users. The feasibility of our novel approach is demonstrated with a series of online BCI experiments. Although performed without any calibration measurement at all, no loss of classification performance was observed. |
| first_indexed | 2024-04-12T04:10:23Z |
| format | Article |
| id | doaj.art-f209b4ebdce14d18b2608183b922e4d6 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T04:10:23Z |
| publishDate | 2008-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-f209b4ebdce14d18b2608183b922e4d62022-12-22T03:48:31ZengPublic Library of Science (PLoS)PLoS ONE1932-62032008-01-0138e296710.1371/journal.pone.0002967Towards zero training for brain-computer interfacing.Matthias KrauledatMichael TangermannBenjamin BlankertzKlaus-Robert MüllerElectroencephalogram (EEG) signals are highly subject-specific and vary considerably even between recording sessions of the same user within the same experimental paradigm. This challenges a stable operation of Brain-Computer Interface (BCI) systems. The classical approach is to train users by neurofeedback to produce fixed stereotypical patterns of brain activity. In the machine learning approach, a widely adapted method for dealing with those variances is to record a so called calibration measurement on the beginning of each session in order to optimize spatial filters and classifiers specifically for each subject and each day. This adaptation of the system to the individual brain signature of each user relieves from the need of extensive user training. In this paper we suggest a new method that overcomes the requirement of these time-consuming calibration recordings for long-term BCI users. The method takes advantage of knowledge collected in previous sessions: By a novel technique, prototypical spatial filters are determined which have better generalization properties compared to single-session filters. In particular, they can be used in follow-up sessions without the need to recalibrate the system. This way the calibration periods can be dramatically shortened or even completely omitted for these 'experienced' BCI users. The feasibility of our novel approach is demonstrated with a series of online BCI experiments. Although performed without any calibration measurement at all, no loss of classification performance was observed.http://europepmc.org/articles/PMC2500157?pdf=render |
| spellingShingle | Matthias Krauledat Michael Tangermann Benjamin Blankertz Klaus-Robert Müller Towards zero training for brain-computer interfacing. PLoS ONE |
| title | Towards zero training for brain-computer interfacing. |
| title_full | Towards zero training for brain-computer interfacing. |
| title_fullStr | Towards zero training for brain-computer interfacing. |
| title_full_unstemmed | Towards zero training for brain-computer interfacing. |
| title_short | Towards zero training for brain-computer interfacing. |
| title_sort | towards zero training for brain computer interfacing |
| url | http://europepmc.org/articles/PMC2500157?pdf=render |
| work_keys_str_mv | AT matthiaskrauledat towardszerotrainingforbraincomputerinterfacing AT michaeltangermann towardszerotrainingforbraincomputerinterfacing AT benjaminblankertz towardszerotrainingforbraincomputerinterfacing AT klausrobertmuller towardszerotrainingforbraincomputerinterfacing |