Online binary decision decoding using functional near-infrared spectroscopy for the development of brain–computer interface

• Published in: Experimental brain research Vol. 232; no. 2; pp. 555 - 564
• Main Authors: Naseer, Noman, Hong, Melissa Jiyoun, Hong, Keum-Shik
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2014; Springer; Springer Nature B.V
• Subjects: Adult; Analysis; Biological and medical sciences; Biomedical and Life Sciences; Biomedicine; Brain; Brain mapping; Brain research; Brain-Computer Interfaces; Cerebral Cortex - physiology; Decision making; Decision Making - physiology; Discriminant Analysis; Female; Fundamental and applied biological sciences. Psychology; Hemodynamics; Hemoglobin; Hemoglobins - metabolism; Humans; Light; Male; Neurology; Neurosciences; Online Systems; Oxyhemoglobins - metabolism; Research Article; Spectroscopy, Near-Infrared; Spectrum analysis; Support Vector Machine; Support vector machines; Vertebrates: nervous system and sense organs; Young Adult; Yes/no decoding; Binary decision decoding; Functional near-infrared spectroscopy; Brain–computer interface; Central nervous system; Development; Brain―computer interface; Encephalon
• ISSN: 0014-4819; 1432-1106; 1432-1106
• DOI: 10.1007/s00221-013-3764-1

In this paper, a functional near-infrared spectroscopy (fNIRS)-based online binary decision decoding framework is developed. Fourteen healthy subjects are asked to mentally make “yes” or “no” decisions in answers to the given questions. For obtaining “yes” decoding, the subjects are asked to perform a mental task that causes a cognitive load on the prefrontal cortex, while for making “no” decoding, they are asked to relax. Signals from the prefrontal cortex are collected using continuous-wave near-infrared spectroscopy. It is observed and verified, using the linear discriminant analysis (LDA) and the support vector machine (SVM) classifications, that the cortical hemodynamic responses for making a “yes” decision are distinguishable from those for making a “no” decision. Using mean values of the changes in the concentration of hemoglobin as features, binary decisions are classified into two classes, “yes” and “no,” with an average classification accuracy of 74.28 % using LDA and 82.14 % using SVM. These results demonstrate and suggest the feasibility of fNIRS for a brain–computer interface.