Effectiveness Evaluation of Real-Time Scalp Signal Separating Algorithm on Near-Infrared Spectroscopy Neurofeedback

Wei Chun Ung, Tsukasa Funane, Takusige Katura, Hiroki Sato, Tong Boon Tang, Ahmad Fadzil M. Hani, Masashi Kiguchi

研究成果: Article査読

16 被引用数 (Scopus)


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.

ジャーナルIEEE Journal of Biomedical and Health Informatics
出版ステータスPublished - 2018 7月

ASJC Scopus subject areas

  • バイオテクノロジー
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学
  • 健康情報管理


「Effectiveness Evaluation of Real-Time Scalp Signal Separating Algorithm on Near-Infrared Spectroscopy Neurofeedback」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。