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classification as we achieved 75.2 % mean accuracy
corresponding to the Kappa value of 0.67 on testing
dataset for 64 features. 72.5 % Accuracy with Kappa
value of 0.63 is achieved using 32 features only. Our
results are the best compared to all methods listed in
Table 3. Performance of ANN and Deep learning ap-
proaches can be further improved but these methods
require large amount of data and high computational
cost. Secondly, the results comparison with different
techniques which have been proposed in recent times,
has shown that the proposed method comes up with
best results for Kappa value as well as for classifica-
tion accuracy. Additionally, proposed technique gain
the dominance over other listed techniques is compu-
tational cost. We have achieved better results for less
number of features as compared to other methods.
4 CONCLUSION
A method for classifying MI signals of EEG for BCI
systems has been proposed in this paper and results
have been compared with different state of the art al-
gorithms. Proposed methodology presented in this
paper encapsulates 4 band-pass filters for preprocess-
ing. FBCSP algorithm is developed as feature ex-
traction technique to classify EEG signals for 4-class
motor imagery. Many classification algorithms have
been tested on our methodology with feature set of
different lengths where SVM has produced best clas-
sification results among all the listed approaches. Re-
sults are compared with some latest techniques of the
literature witnessing that the proposed methodology
addressed best results among all the listed techniques.
We have came with the conclusion that using a very
less number of CSP features extracted from only mu
and beta rhythms can provide a reliable feature ex-
traction source for multi class EEG data classifica-
tion. Since we have utilized the CSP features of a
limited number for classification task by a computa-
tionally very inexpensive classification technique, the
proposed algorithm can be applied for on-line classi-
fication of MI tasks in real-time BCI applications.
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