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employing a small number of electrodes –ideally
two, or even a single, electrode(s). It may be
possible to obtain higher classification rates by
utilising a single electrode that is more relevant to
the specific mental task rather than using a
combination of electrodes, all of which are not as
relevant to the task. This is also advantageous as it
decreases the feature dimensionality.
5 CONCLUSIONS
This paper presents the results of initial
investigations in the search for appropriate features
and classifier towards the development of a thought-
to-speech converter. The results indicate that the use
of an SVM for the classification of AR coefficients
is more appropriate than LDA and NN and will be
utilised in the development of the proposed system.
The proposed system is promising as it offers the
ability to communicate more efficiently via direct
conversion of thoughts into speech. In order to
ensure optimal operation other aspects of the system
must also be investigated. Firstly, a more extensive
set of features and classifiers will be examined such
that the optimal combination in terms of maximising
accuracy is determined – computational efficiency is
not a consideration as the system will be customised
and capable of parallel processing. Secondly, these
investigations suggest that different combinations of
mental tasks seem to be more appropriate for
different subjects. We are going to look into finding
a combination of tasks that are more intuitive and
more closely related to the concept of MC, as this
could improve classification accuracy and facilitate
easier operation.
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