Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6282667 | Neuroscience Letters | 2013 | 6 Pages |
Abstract
This paper presents a study on functional near-infrared spectroscopy (fNIRS) indicating that the hemodynamic responses of the right- and left-wrist motor imageries have distinct patterns that can be classified using a linear classifier for the purpose of developing a brain-computer interface (BCI). Ten healthy participants were instructed to imagine kinesthetically the right- or left-wrist flexion indicated on a computer screen. Signals from the right and left primary motor cortices were acquired simultaneously using a multi-channel continuous-wave fNIRS system. Using two distinct features (the mean and the slope of change in the oxygenated hemoglobin concentration), the linear discriminant analysis classifier was used to classify the right- and left-wrist motor imageries resulting in average classification accuracies of 73.35% and 83.0%, respectively, during the 10Â s task period. Moreover, when the analysis time was confined to the 2-7Â s span within the overall 10Â s task period, the average classification accuracies were improved to 77.56% and 87.28%, respectively. These results demonstrate the feasibility of an fNIRS-based BCI and the enhanced performance of the classifier by removing the initial 2Â s span and/or the time span after the peak value.
Related Topics
Life Sciences
Neuroscience
Neuroscience (General)
Authors
Noman Naseer, Keum-Shik Hong,