Paper Info
Title
Identification of moving limb using near infrared spectroscopic signals for brain activation
Abstract
A method is described for classifying near-infrared spectroscopy (NIRS) signals measured for motor imagery and/or execution using the left or right hand. The measurement time intervals and the signal channels are used as features. The signals are discriminated using a support vector machine. Experiments demonstrated that this method has a higher generalization capability than a previous method for classifying NIRS signals. Testing of its ability to classify the signals according to whether they are for right- or left-hand motor imagery and/or movement demonstrated that its classification of NIRS signals satisfies the two-category classification problem. A promising application is to brain-computer interfaces, a potential communication tool for paralyzed individuals.
Year
DOI
Venue
2009
10.1109/IJCNN.2009.5178833
IJCNN
Keywords
Field
DocType
measurement time interval,infrared spectroscopic signal,near-infrared spectroscopy,two-category classification problem,higher generalization capability,previous method,nirs signal,classifying nirs signal,right hand,left-hand motor imagery,motor imagery,brain activation,signal processing,support vector machine,neurophysiology,brain computer interfaces,data mining,support vector machines,electroencephalography,hidden markov models,near infrared,spectroscopy,kernel,near infrared spectroscopy,satisfiability,infrared spectroscopy,biomechanics,brain computer interface,time series analysis,infrared spectra
Kernel (linear algebra),Computer vision,Neurophysiology,Pattern recognition,Computer science,Near-infrared spectroscopy,Support vector machine,Brain–computer interface,Communication channel,Artificial intelligence,Hidden Markov model,Motor imagery
Conference
ISSN
Citations 
PageRank 
1098-7576
3
0.45
References 
Authors
3
3
Name
Order
Citations
PageRank
Wataru Niide130.45
Tadashi Tsubone2209.43
Yasuhiro Wada322562.58