Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, People's Republic of China.
Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Ministry of Education), Fudan University, Shanghai, People's Republic of China.
J Neural Eng. 2021 Dec 13;18(6). doi: 10.1088/1741-2552/ac3cc5.
Closed-loop deep brain stimulation (DBS) with neural feedback has shown great potential in improving the therapeutic effect and reducing side effects. However, the amplitude of stimulation artifacts is much larger than the local field potentials, which remains a bottleneck in developing a closed-loop stimulation strategy with varied parameters.We proposed an irregular sampling method for the real-time removal of stimulation artifacts. The artifact peaks were detected by applying a threshold to the raw recordings, and the samples within the contaminated period of the stimulation pulses were excluded and replaced with the interpolation of the samples prior to and after the stimulation artifact duration. This method was evaluated with both simulation signals andclosed-loop DBS applications in Parkinsonian animal models.. The irregular sampling method was able to remove the stimulation artifacts effectively with the simulation signals. The relative errors between the power spectral density of the recovered and true signals within a wide frequency band (2-150 Hz) were 2.14%, 3.93%, 7.22%, 7.97% and 6.25% for stimulation at 20 Hz, 60 Hz, 130 Hz, 180 Hz, and stimulation with variable low and high frequencies, respectively. This stimulation artifact removal method was verified in real-time closed-loop DBS applications, and the artifacts were effectively removed during stimulation with frequency continuously changing from 130 Hz to 1 Hz and stimulation adaptive to beta oscillations.The proposed method provides an approach for real-time removal in closed-loop DBS applications, which is effective in stimulation with low frequency, high frequency, and variable frequency. This method can facilitate the development of more advanced closed-loop DBS strategies.
闭环深部脑刺激 (DBS) 结合神经反馈在提高治疗效果和减少副作用方面显示出巨大潜力。然而，刺激伪影的幅度比局部场电位大得多，这仍然是开发具有可变参数的闭环刺激策略的瓶颈。我们提出了一种用于实时去除刺激伪影的不规则采样方法。通过对原始记录施加阈值来检测伪影峰值，排除刺激脉冲污染期间的样本，并使用刺激伪影持续时间前后的样本进行插值来替换这些样本。该方法使用模拟信号和帕金森病动物模型中的闭环 DBS 应用进行了评估。不规则采样方法能够有效地去除模拟信号中的刺激伪影。在较宽的频率范围内（2-150 Hz），恢复信号和真实信号的功率谱密度之间的相对误差分别为 2.14%、3.93%、7.22%、7.97%和 6.25%，用于 20 Hz、60 Hz、130 Hz、180 Hz 的刺激以及低频和高频变化的刺激。在实时闭环 DBS 应用中验证了这种刺激伪影去除方法，并在频率从 130 Hz 连续变化到 1 Hz 以及刺激自适应于β振荡的刺激过程中有效地去除了伪影。所提出的方法为闭环 DBS 应用中的实时去除提供了一种方法，对于低频、高频和变频率刺激都非常有效。该方法可以促进更先进的闭环 DBS 策略的发展。
