Key result
Wavelet and neural network classifiers achieve >94% accuracy recognizing brain hemodynamic responses during mental tasks.
Why the study?
Accurate recognition of brain hemodynamic responses during mental tasks using near-infrared spectroscopy is important for various brain studies.
A wavelet and neural network-based methodology can accurately classify human brain hemodynamic responses during mental tasks with over 94% accuracy.
Supports noninvasive hemodynamic monitoring in research settings; leaves open clinical validation before adoption.
We investigate subjects’ brain hemodynamic activities during mental tasks using a nearinfrared spectroscopy. A wavelet and neural network-based methodology is presented for recognition of brain hemodynamic responses. The recognition is performed by a single layer neural network classifier according to a backpropagation algorithm with two error minimizing techniques. The performance of the classifier varied depending on the neural network model, but the performance was usually at least 90%. The classifier usually converged faster and attained a somewhat greater level of performance when an input was presented with only relevant features. The overall classification rate was higher than 94%. The study demonstrates the accurate classifiablity of human brain hemodynamic useful in various brain studies.
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Abibullaev et al. (2011) studied Brain hemodynamic responses during mental tasks. Wavelet and neural network-based classifier was evaluated on Overall classification rate of brain hemodynamic responses. A wavelet and neural network-based classifier achieved an overall classification rate higher than 94% for recognizing brain hemodynamic responses during mental tasks.
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