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| 8.
Direct Brain–Computer Communication |
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8.3.
Components of graz BCI
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8.3.1.
Parameter Estimation and Classification |
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8.3.1.4.
Hidden Markov Model |
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The two classification methods based on
autoregressive parameters or common spatial patterns classify
the ongoing EEG in short windows where stationarity is assumed.
Dynamic EEG changes such as, for example, patterns of desynchronization
cannot be modeled, and are therefore not considered for classification.
To overcome the problem of nonstationarity, the hidden Markov
models (HMMs) were introduced for modeling the dynamic
EEG changes.
An HMM describes a first-order time domain process which is
defined as a process where the conditional probability of the
current event merely depends on the most recent event.
The HMM itself could be seen as a finite automate containing discrete
states, emitting a feature vector at every time point.
Source:
Motor Imagery and Direct Brain–Computer Communication, Gert Pfurtscheller and Christa Neuper 
