Abstract
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.
Original language | English |
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Pages (from-to) | 340-359 |
Number of pages | 20 |
Journal | International Journal of Optomechatronics |
Volume | 5 |
Issue number | 4 |
DOIs | |
Publication status | Published - Oct 1 2011 |
Keywords
- brain-computer interface
- functional near infrared spectroscopy
- mental task classification
- neural networks
- wavelet transforms
ASJC Scopus subject areas
- Instrumentation
- Mechanical Engineering
- Electrical and Electronic Engineering