An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Decomposition methods for linear support vector machines
Neural Computation
Time domain parameters for online feedback fNIRS-based brain-computer interface systems
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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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.