NDLI: Delta band contribution in cue based single trial classification of real and imaginary wrist movements

Content Provider	Springer Nature Link
Author	Vuckovic, Aleksandra Sepulveda, Francisco
Copyright Year	2008
Abstract	The aim of this study was to classify different movements about the right wrist. Four different movements were performed: extension, flexion, pronation and supination. Two-class single trial classification was performed on six possible combinations of two movements (extension–flexion, extension–supination, extension–pronation, flexion–supination, flexion–pronation, pronation–supination). Both real and imaginary movements were analysed. The analysis was done in the joint time–frequency domain using the Gabor transform. Feature selection was based on the Davis-Bouldin Index (DBI) and feature classification was based on Elman’s recurrent neural networks (ENN). The best classification results, near 80% true positive rate, for imaginary movements were achieved for discrimination between extension and any other type of movement. The experiments were run with 10 able-bodied subjects. For some subjects, real movement classification rates higher than 80% were achieved for any combination of movements, though not simultaneously for all six combinations of movements. For classification of the imaginary movements, the results suggest that the type of movement and frequency band play an important role. Unexpectedly, the delta band was found to carry significant class-related information.
Starting Page	529
Ending Page	539
Page Count	11
File Format	PDF
ISSN	01400118
Journal	Medical and Biological Engineering and Computing
Volume Number	46
Issue Number	6
e-ISSN	17410444
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-04-17
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Brain computer interface (BCI) EEG Motor tasks Movement imagery Right hand Neurosciences Computer Applications Imaging Radiology Human Physiology Biomedical Engineering
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering Computer Science Applications

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