Work

Neural Network Classification of Brain Hemodynamic Responses from Four Mental Tasks

Year: 2011

Type: article

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.

Source: International Journal of Optomechatronics

DOI: 10.1080/15599612.2011.633209

Authors: Berdakh Abibullaev, Jinung An, Jeon-Il Moon

Institution: Daegu Gyeongbuk Institute of Science and Technology

Language: English

Topic: Optical Imaging and Spectroscopy Techniques

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: Ministry of Education, Science and Technology, Daegu Gyeongbuk Institute of Science and Technology