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selection as follows, a wide frequency band is first divided into several sub-bands
which may overlap. For each sub-band, a r 2 -coefficient is calculated as a score using
the observations in this sub-band. Based on this score, one or several sub-bands can
be selected.
5 Translation Methods
After the features that reflect the intentions of the user are extracted, the next step is
to translate these discriminative features into commands that operate a device.
The translation methods employed in BCIs convert the features extracted into
device control commands [7]. These commands can be of discrete-value such as
letter selection, or of continuous-value such as vertical and horizontal cursor movement.
The translation methods in BCIs often employ machine learning approaches
in order to train a model from the training data collected. Translation methods can
be broadly classified into classification and regression methods. Sect. 5.1 describes
the former, which translates features into discrete-value commands, while Sect. 5.2
describes the latter, which translates features into continuous-value commands.
5.1 Classification Methods
A classification algorithm is defined as one that involves building a model from the
training data so that the model can be used to classify new data that are not included
in the training data [36].
The “No Free Lunch” theorem states that there is no general superiority of any
approach over the others in pattern classification; furthermore, if one approach
seems to outperform another in a particular situation, it is a consequence of its fit
to the particular pattern recognition problem [33]. Hence, a wide variety of classification
methods are used in BCIs. Prevailingly, the Fisher linear discriminant (FLD)
[37–41], support vector machine (SVM) [37, 39, 40, 41], Bayesian [42], and hidden
Markov model (HMM) [43] are commonly used as classification methods in BCIs.
FLD and SVM classification algorithms are described in the following subsections.
The classification algorithms use the training data that comprises n samples
denoted xj with class label yj where j = 1, ··· , n, to form the training model.
The classifier then calculates an estimate of the class label y of an unseen sample
using the training model. These algorithms (as well as many others) are implemented
for Matlab in the Statistical Pattern Recognition ToolBox (STPRTool) from
http://cmp.felk.cvut.cz/cmp/software/stprtool/.
5.1.1 Fisher Linear Discriminant
Linear discrimination is a method that projects the high dimensional feature data
onto a lower dimensional space. The projected data is then easier to separate into two
classes. In Fisher linear discriminant (FLD), the separability of the data is measured