Comparison of different feature classifiers for brain computer interfaces

F. Cincotti, A. Scipione, A. Timperi, D. Mattia, M. G. Marciani, J. Millán, S. Salinari, L. Bianchi, F. Babiloni

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Changes in EEG power spectra related to the imagination of movements may be used to build up a direct communication channel between brain and computer (brain computer interface; BCI). However, for the practical implementation of a BCI device, the feature classifier plays a crucial role. We compared the performance of three different feature classifiers for the detection of the imagined movements in a group of 6 normal subjects by means the EEG. The feature classifiers compared were those based on the hidden Markov models (HMM), the artificial neural network (ANN) and on the Mahalanobis distance (MD). Results show a better performance of the MD and ANN classifiers with respect to the HMM classifier.

Original languageEnglish
Title of host publicationInternational IEEE/EMBS Conference on Neural Engineering, NER
PublisherIEEE Computer Society
Number of pages3
ISBN (Print)0780375793
Publication statusPublished - 2003
Event1st International IEEE EMBS Conference on Neural Engineering - Capri Island, Italy
Duration: Mar 20 2003Mar 22 2003


Other1st International IEEE EMBS Conference on Neural Engineering
CityCapri Island


  • Artificial neural networks
  • Brain computer interfaces
  • Communication channels
  • Computer interfaces
  • Electrodes
  • Electroencephalography
  • Frequency estimation
  • Hidden Markov models
  • Scalp
  • Testing

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering


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