Asynchronous Brain Computer Interface using Hidden Semi-Markov Models

Ideal Brain Computer Interfaces need to perform asynchronously and at real time. We propose Hidden Semi-Markov Models(HSMM) to better segment and classify EEG data. The proposed HSMM method was tested against a simple windowed method on standard datasets. We found that our HSMM outperformed the simp...

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Bibliographische Detailangaben

Hauptverfasser:	Oliver, G., Sunehag, P., Gedeon, T.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Algorithms Brain modeling Brain-Computer Interfaces Electroencephalography Electroencephalography - methods Feature extraction Hidden Markov models Humans Markov Chains Real-time systems Support vector machines Viterbi algorithm
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