Adaptive common average reference for in vivo multichannel local field potentials

Liu Xinyu; Wan Hong; Li Shan; Chen Yan; Shi Li

doi:10.1007/s13534-016-0004-1

Adaptive common average reference for in vivo multichannel local field potentials

Biomed Eng Lett. 2017 Jan 11;7(1):7-15. doi: 10.1007/s13534-016-0004-1. eCollection 2017 Feb.

Authors

Liu Xinyu¹, Wan Hong^{1

2}, Li Shan¹, Chen Yan¹, Shi Li^{1

2

3}

Affiliations

¹ 1School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001 China.
² 2Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou, 450001 China.
³ 3Department of Automation, Tsinghua University, Beijing, 100084 China.

Abstract

For in vivo neural recording, local field potential (LFP) is often corrupted by spatially correlated artifacts, especially in awake/behaving subjects. A method named adaptive common average reference (ACAR) based on the concept of adaptive noise canceling (ANC) that utilizes the correlative features of common noise sources and implements with common average referencing (CAR), was proposed for removing the spatially correlated artifacts. Moreover, a correlation analysis was devised to automatically select appropriate channels before generating the CAR reference. The performance was evaluated in both synthesized data and real data from the hippocampus of pigeons, and the results were compared with the standard CAR and several previously proposed artifacts removal methods. Comparative testing results suggest that the ACAR performs better than the available algorithms, especially in a low SNR. In addition, feasibility of this method was provided theoretically. The proposed method would be an important pre-processing step for in vivo LFP processing.

Keywords: Adaptive noise canceling; Common average reference; Local field potential; Microelectrode array; Spatially correlated artifacts.