Reduction of Onset Delay in Functional Near-Infrared Spectroscopy: Prediction of HbO/HbR Signals

Amad Zafar; Keum-Shik Hong

doi:10.3389/fnbot.2020.00010

Reduction of Onset Delay in Functional Near-Infrared Spectroscopy: Prediction of HbO/HbR Signals

Front Neurorobot. 2020 Feb 18:14:10. doi: 10.3389/fnbot.2020.00010. eCollection 2020.

Authors

Amad Zafar^{1

2}, Keum-Shik Hong^{1

3}

Affiliations

¹ School of Mechanical Engineering, Pusan National University, Busan, South Korea.
² Department of Electrical Engineering, University of Wah, Wah Cantonment, Pakistan.
³ Department of Cogno-Mechatronics Engineering, Pusan National University, Busan, South Korea.

Abstract

An intrinsic problem when using hemodynamic responses for the brain-machine interface is the slow nature of the physiological process. In this paper, a novel method that estimates the oxyhemoglobin changes caused by neuronal activations is proposed and validated. In monitoring the time responses of blood-oxygen-level-dependent signals with functional near-infrared spectroscopy (fNIRS), the early trajectories of both oxy- and deoxy-hemoglobins in their phase space are scrutinized. Furthermore, to reduce the detection time, a prediction method based upon a kernel-based recursive least squares (KRLS) algorithm is implemented. In validating the proposed approach, the fNIRS signals of finger tapping tasks measured from the left motor cortex are examined. The results show that the KRLS algorithm using the Gaussian kernel yields the best fitting for both ΔHbO (i.e., 87.5%) and ΔHbR (i.e., 85.2%) at q = 15 steps ahead (i.e., 1.63 s ahead at a sampling frequency of 9.19 Hz). This concludes that a neuronal activation can be concluded in about 0.1 s with fNIRS using prediction, which enables an almost real-time practice if combined with EEG.

Keywords: brain-machine interface (BMI); functional near-infrared spectroscopy (fNIRS); hemodynamic response; prediction; tracking; vector phase analysis.