Kelly John W, Collinger Jennifer L, Degenhart Alan D, Siewiorek Daniel P, Smailagic Asim, Wang Wei
Dept of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:7908-11. doi: 10.1109/IEMBS.2011.6091950.
This paper presents a method for filtering line noise using an adaptive noise canceling (ANC) technique. This method effectively eliminates the sinusoidal contamination while achieving a narrower bandwidth than typical notch filters and without relying on the availability of a noise reference signal as ANC methods normally do. A sinusoidal reference is instead digitally generated and the filter efficiently tracks the power line frequency, which drifts around a known value. The filter's learning rate is also automatically adjusted to achieve faster and more accurate convergence and to control the filter's bandwidth. In this paper the focus of the discussion and the data will be electrocorticographic (ECoG) neural signals, but the presented technique is applicable to other recordings.
本文提出了一种使用自适应噪声消除（ANC）技术来滤除线路噪声的方法。该方法能有效消除正弦干扰，同时实现比典型陷波滤波器更窄的带宽，且不像传统ANC方法那样依赖噪声参考信号的可用性。取而代之的是数字生成一个正弦参考信号，该滤波器能有效跟踪围绕已知值漂移的电力线频率。滤波器的学习率也会自动调整，以实现更快、更精确的收敛，并控制滤波器的带宽。在本文中，讨论的重点和数据将是皮层脑电图（ECoG）神经信号，但所提出的技术也适用于其他记录。
