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Common spatial pattern patches - An optimized filter ensemble for adaptive Brain-Computer Interfaces

: Sannelli, C.; Vidaurre, C.; Müller, K.-R.; Blankertz, B.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Engineering in Medicine and Biology Society -EMBS-:
32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2010 : August 31 - September 4, 2010. Sheraton Hotel, Buenos Aires, Argentina
New York, NY: IEEE, 2010
ISBN: 978-1-4244-4123-5 (Print)
ISBN: 978-4244-4124-2 (Online)
pp.4351-4354 (Vol.7)
Engineering in Medicine and Biology Society (EMBS International Conference) <32, 2010, Buenos Aires>
Conference Paper
Fraunhofer FIRST ()

Laplacian filters are commonly used in Brain Computer Interfacing (BCI). When only data from few channels are available, or when, like at the beginning of an experiment, no previous data from the same user is available complex features cannot be used. In this case band power features calculated from Laplacian filtered channels represents an easy, robust and general feature to control a BCI, since its calculation does not involve any class information. For the same reason, the performance obtained with Laplacian features is poor in comparison to subject-specific optimized spatial filters, such as Common Spatial Patterns (CSP) analysis, which, on the other hand, can be used just in a later phase of the experiment, since they require a considerable amount of training data in order to enroll a stable and good performance. This drawback is particularly evident in case of poor performing BCI users, whose data is highly non-stationary and contains little class relevant informa tion. Therefore, Laplacian filtering is preferred to CSP, e.g., in the initial period of co-adaptive calibration, a novel BCI paradigm designed to alleviate the problem of BCI illiteracy. In fact, in the co-adaptive calibration design the experiment starts with a subject-independent classifier and simple features are needed in order to obtain a fast adaptation of the classifier to the newly acquired user's data. Here, the use of an ensemble of local CSP patches (CSPP) is proposed, which can be considered as a compromise between Laplacians and CSP: CSPP needs less data and channels than CSP, while being superior to Laplacian filtering. This property is shown to be particularly useful for the co-adaptive calibration design and is demonstrated on off-line data from a previous co-adaptive BCI study.