PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multi-task Learning for Brain-Computer Interfaces
Morteza Alamgir, Moritz Grosse-Wentrup and Yasemin Altun
In: AISTATS 2010(2009).


Brain-computer interfaces (BCIs) are limited in their applicability in everyday settings by the current necessity to record subject-specific 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. Combining prior recordings with subject-specific calibration data substantially outperforms using subject-specific data only. Our results show that transfer between recordings under slightly different experimental setups is feasible.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:6120
Deposited By:Yasemin Altun
Deposited On:08 March 2010