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MLSP Competition, 2010: Description of first place method
Our winning approach to the 2010 MLSP Competition is based on a generative method for P300-based BCI decoding, successfully applied to visual spellers. Here, generative has a double meaning. On the one hand, we work with a probability density model of the data given the target/non target labeling, as opposed to discriminative (e.g. SVM-based) methods. On the other hand, the natural consequence of this approach is a decoding based on comparing the observation to templates generated from the data.
@inproceedings{6434, title = {MLSP Competition, 2010: Description of first place method}, journal = {Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010)}, abstract = {Our winning approach to the 2010 MLSP Competition is based on a generative method for P300-based BCI decoding, successfully applied to visual spellers. Here, generative has a double meaning. On the one hand, we work with a probability density model of the data given the target/non target labeling, as opposed to discriminative (e.g. SVM-based) methods. On the other hand, the natural consequence of this approach is a decoding based on comparing the observation to templates generated from the data.}, pages = {112-113}, publisher = {IEEE}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Piscataway, NJ, USA}, month = sep, year = {2010}, slug = {6434}, author = {Leiva, JM. and Martens, SMM.}, month_numeric = {9} }