%0 Journal Article
%@ 21682194
%A Ung, W.C.
%A Funane, T.
%A Katura, T.
%A Sato, H.
%A Tang, T.B.
%A Hani, A.F.M.
%A Kiguchi, M.
%D 2018
%F scholars:10209
%I Institute of Electrical and Electronics Engineers Inc.
%J IEEE Journal of Biomedical and Health Informatics
%K Brain; Feedback; Infrared devices; Near infrared spectroscopy; Separation, Brain activity; Cerebral hemoglobin concentration; Effectiveness evaluation; Feedback signal; Hemodynamic changes; Neurofeedback; Realtime processing; Separating algorithm, Brain computer interface, blood; brain computer interface; human; near infrared spectroscopy; neurofeedback; scalp; adult; algorithm; brain; female; male; middle aged; near infrared spectroscopy; neurofeedback; physiology; procedures; scalp; signal processing, Adult; Algorithms; Brain; Female; Humans; Male; Middle Aged; Neurofeedback; Scalp; Signal Processing, Computer-Assisted; Spectroscopy, Near-Infrared
%N 4
%P 1148-1156
%R 10.1109/JBHI.2017.2723024
%T Effectiveness Evaluation of Real-Time Scalp Signal Separating Algorithm on Near-Infrared Spectroscopy Neurofeedback
%U https://khub.utp.edu.my/scholars/10209/
%V 22
%X Near-infrared spectroscopy (NIRS), one of the candidates to be used in a neurofeedback system or brain-computer interface (BCI), measures the brain activity by monitoring the changes in cerebral hemoglobin concentration. However, hemodynamic changes in the scalp may affect the NIRS signals. In order to remove the superficial signals when NIRS is used in a neurofeedback system or BCI, real-time processing is necessary. Real-time scalp signal separating (RT-SSS) algorithm, which is capable of separating the scalp-blood signals from NIRS signals obtained in real-time, may thus be applied. To demonstrate its effectiveness, two separate neurofeedback experiments were conducted. In the first experiment, the feedback signal was the raw NIRS signal recorded while in the second experiment, deep signal extracted using RT-SSS algorithm was used as the feedback signal. In both experiments, participants were instructed to control the feedback signal to follow a predefined track. Accuracy scores were calculated based on the differences between the trace controlled by feedback signal and the targeted track. Overall, the second experiment yielded better performance in terms of accuracy scores. These findings proved that RT-SSS algorithm is beneficial for neurofeedback. © 2013 IEEE.
%Z cited By 17