  This article examined brain signals of people with disabilities using various
signal processing methods to achieve the desired accuracy for utilizing
brain-computer interfaces (BCI). EEG signals resulted from 5 mental tasks of
word association (WORD), Mental subtraction (SUB), spatial navigation (NAV),
right-hand motor imagery (HAND), and feet motor imagery (FEET) of 9 people with
central nervous system (CNS) tissue damage were used as input. In processing
this data, Butterworth band-pass filter (8-30 Hz) was used in the
preprocessing, and CSP, TRCSP, FBCSP methods were used in feature extraction,
and LDA, KNN, linear and nonlinear SVM were used in classification stages. The
training and testing process was repeated up to 100 times, and the random
subsampling method was used to select the training and test data. Mean accuracy
in 100 replications was reported as final accuracy. The classification results
of two classes of 5 mental tasks on the data of 9 people with central nerve
damage showed that the combination of FBCSP with KNN classifier has the highest
accuracy of 69+-1.1 which belongs to the two classes of word association and
feet motor imagery. The study of different feature extraction methods and
classification indicates that the proper feature selection method and
especially classifier is crucial in accuracy rate. Furthermore, it is necessary
to pay attention to successful signal processing methods in designing a
brain-computer interface to use in people with central nervous system.
