Adaptive filtering for global interference cancellation and real-time recovery of evoked brain activity: a Monte Carlo simulation study

Abstract

The sensitivity of near-infrared spectroscopy (NIRS) to evoked brain activity is reduced by physiological interference in at least two locations: 1. the superficial scalp and skull layers, and 2. in brain tissue itself. These interferences are generally termed as "global interferences" or "systemic interferences," and arise from cardiac activity, respiration, and other homeostatic processes. We present a novel method for global interference reduction and real-time recovery of evoked brain activity, based on the combination of a multiseparation probe configuration and adaptive filtering. Monte Carlo simulations demonstrate that this method can be effective in reducing the global interference and recovering otherwise obscured evoked brain activity. We also demonstrate that the physiological interference in the superficial layers is the major component of global interference. Thus, a measurement of superficial layer hemodynamics (e.g., using a short source-detector separation) makes a good reference in adaptive interference cancellation. The adaptive-filtering-based algorithm is shown to be resistant to errors in source-detector position information as well as to errors in the differential pathlength factor (DPF). The technique can be performed in real time, an important feature required for applications such as brain activity localization, biofeedback, and potential neuroprosthetic devices.

A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology

“…Another finding was that the regression methodology improves the [O 2 Hb] signals more than [HHb] signals, which confirms a previous study of the same group (Gagnon et al, 2011). The method of Saager and Berger was further developed by Zhang et al (2007a) who considered also non-stationarities of the signal by replacing the LMMSE with a least mean squares (LMS) adaptive filtering algorithm. They validated their method using simulated (Zhang et al, 2007a) and real fNIRI data (Zhang et al, 2007b(Zhang et al, , 2009).…”

“…The method of Saager and Berger was further developed by Zhang et al (2007a) who considered also non-stationarities of the signal by replacing the LMMSE with a least mean squares (LMS) adaptive filtering algorithm. They validated their method using simulated (Zhang et al, 2007a) and real fNIRI data (Zhang et al, 2007b(Zhang et al, , 2009). An improvement of this method was presented in Y.…”

fNIRS-EEG study of focal interictal epileptiform discharges

“…A potential consequence of these filtering efforts is that the true sensitivity and specificity could have been misestimated. Improvements on this technique include the use of short source-detector separation (Zhang et al, 2007;Gagnon et al, 2011). However, due to the constraint of maintaining high spatial coverage and of instrumental gain limitations, short channels were not feasible in this study, but were considered to be included in future work.…”

Physiological Noise Removal from fNIRS Signals

“…For further details see [5]. Adaptive filtering (AF): This approach was introduced by Zhang et al [6] and the idea behind the method is using hemodynamic signals recorded from additional detectors (Near-Detectors; see Fig. 1A) close to the sources to estimate changes in the overlaying tissue layers.…”