title: Cortical brain imaging by adaptive filtering of NIRS signals.
abstract: This paper presents an online brain imaging framework for cognitive tasks conducted with functional near-infrared spectroscopy (fNIRS). The measured signal at each channel is regarded as the output from a linear system with unknown coefficients. The unknown coefficients are estimated by using the recursive least squares estimation (RLSE) method. The validity of the estimated parameters is tested using the t-statistics. Contrary to the classical approach that is offline and applies the same preprocessing scheme to all channels, the proposed RLSE method for a linear model formulation provides an independent robust adaptive process for individual channels. The experiments carried out with two fNIRS instruments (continuous-wave and frequency-domain) have verified the potential of the proposed methodology which can facilitate a prompt medical diagnostics by providing real-time brain activation maps.
authors.affiliation: Department of Cogno-Mechatronics Engineering, Pusan National University, 30 Jangjeon-dong, Geumjeong-gu, Busan 609-735, Republic of Korea. aqil@pusan.ac.kr
authors.forename: Muhammad
authors.initials: M
authors.lastname: Aqil
authors.forename: Keum-Shik
authors.initials: KS
authors.lastname: Hong
authors.forename: Myung-Yung
authors.initials: MY
authors.lastname: Jeong
authors.forename: Shuzhi Sam
authors.initials: SS
authors.lastname: Ge
mesh_terms: D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000502:physiology*; D059907:Functional Neuroimaging / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008959:Models, Neurological; D015203:Reproducibility of Results; D012984:Software; D019265:Spectroscopy, Near-Infrared