Abstract
Sensing-enabled neurostimulators are an advanced technology for chronic observation of brain activities, and show great potential for closed-loop neuromodulation and as implantable brain-computer interfaces. However, local field potentials (LFPs) recorded by sensing-enabled neurostimulators can be contaminated by electrocardiogram (ECG) signals due to complex recording conditions and limited common-mode-rejection-ratio (CMRR). In this study, we propose a solution for removing such ECG artifacts from local field potentials (LFPs) recorded by a sensing-enabled neurostimulator. A synchronized monopolar channel was added as an ECG reference, and two pre-existing methods, i.e., template subtraction and adaptive filtering, were then applied. ECG artifacts were successfully removed and the performance of the method was insensitive to residual stimulation artifacts. This approach to removal of ECG artifacts broadens the range of applications of sensing-enabled neurostimulators.
Highlights
Sensing-enabled neurostimulation has emerged as a technology for long-term observation of brain activities and has paved the way for development of closed-loop neuromodulators and implantable brain-computer interfaces (Stanslaski et al, 2012; Qian et al, 2014; Martini et al, 2020; Ramirez-Zamora et al, 2020)
We propose a method for the removal of ECG artifacts from local field potentials (LFPs)
The residual deep brain stimulation (DBS) (2.5 V, 150 Hz, 60 μs) artifacts in the contaminated LFP are more than two orders of magnitude greater than in the clean LFP
Summary
Sensing-enabled neurostimulation has emerged as a technology for long-term observation of brain activities and has paved the way for development of closed-loop neuromodulators and implantable brain-computer interfaces (Stanslaski et al, 2012; Qian et al, 2014; Martini et al, 2020; Ramirez-Zamora et al, 2020). Using sensing-enabled neurostimulators, a seminal series of studies made enormous advances in the mechanisms of deep brain stimulation (DBS) (Trager et al, 2016; Neumann et al, 2017), the effects of closed-loop neuromodulation (Meidahl et al, 2017; Velisar et al, 2019), and the feasibility of implantable brain-computer interfaces (Vansteensel et al, 2016; Golshan et al, 2020). The researchers had to choose those LFP channels free of ECG contamination (Swann et al, 2018), which limited the precision of the targeted recording positions
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