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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 548478.0, MPI für biologische Kybernetik / Biologische Kybernetik
Multitask Learning for Brain-Computer Interfaces
Authors:Alamgir, M.; Grosse-Wentrup, M.; Altun, Y.
Editors:Teh, Y. W.; Titterington, M.
Date of Publication (YYYY-MM-DD):2010-05
Title of Proceedings:Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010)
Start Page:17
End Page:24
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:Brain-computer interfaces (BCIs) are limited
in their applicability in everyday settings
by the current necessity to record subjectspecific
calibration data prior to actual use
of the BCI for communication. In this paper,
we utilize the framework of multitask
learning to construct a BCI that can be used
without any subject-specific calibration process.
We discuss how this out-of-the-box BCI
can be further improved in a computationally
efficient manner as subject-specific data
becomes available. The feasibility of the approach
is demonstrated on two sets of experimental
EEG data recorded during a standard
two-class motor imagery paradigm from
a total of 19 healthy subjects. Specifically,
we show that satisfactory classification results
can be achieved with zero training data,
and combining prior recordings with subjectspecific
calibration data substantially outperforms
using subject-specific data only. Our
results further show that transfer between
recordings under slightly different experimental
setups is feasible.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:6504
URL:http://jmlr.csail.mit.edu/proceedings/papers/v9/
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