%Aigaion2 BibTeX export from Idiap Publications
%Thursday 21 November 2024 11:51:52 AM

@INPROCEEDINGS{millan:2004:ijcnn,
         author = {Mill{\'{a}}n, Jos{\'{e}} del R.},
       projects = {Idiap},
          month = {7},
          title = {On the Need for On-Line Learning in Brain-Computer Interfaces},
      booktitle = {Proceedings of the International Joint Conference on Neural Networks},
           year = {2004},
        address = {Budapest, Hungary},
           note = {IDIAP-RR 03-30},
       crossref = {rr03-30},
       abstract = {In this paper we motivate the need for on-line learning in brain-computer interfaces (BCI) and illustrate its benefits with the simplest method, namely fixed learning rates. However, the use of this method is supported by the risk of hampering the user to acquire suitable control of the BCI if the embedded classifier changes too rapidly. We report the results with 3 beginner subjects in a series of consecutive recordings, where the classifiers are iteratively trained with the data of a given session and tested on the next session. Interestingly, performance improved over sessions significantly for 2 of the subjects. These results show that on-line learning improves systematically the performance of the subjects. Moreover, performance with on-line learning is statistically similar to that obtained training the classifier off-line with the same amount of data.},
            pdf = {https://publications.idiap.ch/attachments/reports/2004/millan_2004_ijcnn.pdf},
ipdmembership={learning},
}



crossreferenced publications: 
@TECHREPORT{rr03-30,
         author = {Mill{\'{a}}n, Jos{\'{e}} del R.},
       projects = {Idiap},
          title = {On the Need for On-Line Learning in Brain-Computer Interfaces},
           type = {Idiap-RR},
         number = {Idiap-RR-30-2003},
           year = {2003},
    institution = {IDIAP},
        address = {Martigny, Switzerland},
           note = {Published in ``Proc. of the Int. Joint Conf. on Neural Networks'', 2004},
       abstract = {In this paper we motivate the need for on-line learning in BCI and illustrate its benefits with the simplest method, namely fixed learning rates. However, the use of this method is supported by the risk of hampering the user to acquire suitable control of the BCI if the embedded classifier changes too rapidly. We report the results with 3 beginner subjects in a series of consecutive recording, where the classifiers are iteratively trained with the data of a given session and tested on the next session. At the end of these sessions 2 of the subjects reach a suitable performance that is close to allow them to start operating a brain-actuated device.},
            pdf = {https://publications.idiap.ch/attachments/reports/2003/rr03-30.pdf},
ipdmembership={learning},
}