Neural Network Classification of Brain Hemodynamic Responses from Four Mental Tasks

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.

Keywords:

• brain-computer interface
• functional near infrared spectroscopy
• mental task classification
• neural networks
• wavelet transforms

ACKNOWLEDGEMENTS

This work was supported by the DGIST R&D Program of the Ministry of Education, Science and Technology of Korea (11-RS-01).