%Aigaion2 BibTeX export from Idiap Publications
%Saturday 21 December 2024 07:07:00 PM

@INPROCEEDINGS{wcni2001,
         author = {Morris, Andrew and Obermaier, Bernhard and Pfurtscheller, Gert},
       keywords = {EEG, multi-stream classification, robust recognition},
       projects = {Idiap},
          title = {EEG pattern recognition through multi-stream evidence combination},
      booktitle = {Proc. World Congress on Neuroinformatics},
           year = {2001},
        address = {Vienna University of Technology, Austria},
       crossref = {morris-rr-01-31},
       abstract = {EEG recordings provide an important means of brain-computer communication, but their classification accuracy is limited by unforeseeable variations in the signal due to artefacts or recogniser-subject feedback. A number of techniques were recently developed to address a related problem of recogniser robustness to uncontrollable signal variation which also occurs in automatic speech recognition (ASR). In this article we consider how some of the proved advantages of the "multi-stream combination" and "tandem" approaches in HMM/ANN hybrid based ASR can possibly be applied to improve the performance of EEG recognition.},
            pdf = {https://publications.idiap.ch/attachments/reports/2001/morris-2001-neuroinformatics.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2001/morris-2001-neuroinformatics.ps.gz},
ipdmembership={speech},
}



crossreferenced publications: 
@TECHREPORT{morris-RR-01-31,
         author = {Morris, Andrew and Obermaier, Bernhard and Pfurtscheller, Gert},
       keywords = {EEG, multi-stream classification, robust recognition},
       projects = {Idiap},
          title = {EEG pattern recognition through multi-stream evidence combination},
           type = {Idiap-RR},
         number = {Idiap-RR-31-2001},
           year = {2001},
    institution = {IDIAP},
       abstract = {EEG recordings provide an important means of brain-computer communication, but their classification accuracy is limited by unforeseeable variations in the signal due to artefacts or recogniser-subject feedback. A number of techniques were recently developed to address a related problem of recogniser robustness to uncontrollable signal variation which also occurs in automatic speech recognition (ASR). In this article we consider how some of the proved advantages of the "multi-stream combination" and "tandem" approaches in HMM/ANN hybrid based ASR can possibly be applied to improve the performance of EEG recognition.},
            pdf = {https://publications.idiap.ch/attachments/reports/2001/rr01-31.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2001/rr01-31.ps.gz},
ipdmembership={speech},
}