| dc.description.abstract | The detection of a hand movement beforehand
can be a beneficent tool to control a prosthetic hand for upper
extremity rehabilitation. To be able to achieve smooth control,
the intention detection is acquired from the human body,
especially from brain signal or electroencephalogram (EEG)
signal. However, many constraints hamper the development of
this brain-computer interface (BCI), especially for finger
movement detection. Most of the researchers have focused on
the detection of the left and right-hand movement. This article
presents the comparison of various pattern recognition method
for recognizing five individual finger movements, i.e., the
thumb, index, middle, ring, and pinky finger movements. The
EEG pattern recognition utilized common spatial pattern (CSP)
for feature extraction. As for the classifier, four classifiers, i.e.,
random forest (RF), support vector machine (SVM), k-nearest
neighborhood (kNN), and linear discriminant analysis (LDA)
were tested and compared to each other. The experimental
results indicated that the EEG pattern recognition with RF
achieved the best accuracy of about 54%. Other published
publication reported that the classification of the individual
finger movement is still challenging and need more efforts to
achieve better performance. | en_US |
