Adaptive common average reference for in vivo multichannel local field potentials

• Published in: Biomedical engineering letters Vol. 7; no. 1; pp. 7 - 15
• Main Authors: Xinyu, Liu, Hong, Wan, Shan, Li, Yan, Chen, Li, Shi
• Format: Journal Article
• Language: English
• Published: Korea The Korean Society of Medical and Biological Engineering 01.02.2017; Springer Nature B.V
• Subjects: Algorithms; Biological and Medical Physics; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Correlation analysis; Electrophysiological recording; Engineering; In vivo methods and tests; Medical and Radiation Physics; Original; Original Article; Local field potential; Common average reference; Microelectrode array; Spatially correlated artifacts; Adaptive noise canceling
• ISSN: 2093-9868; 2093-985X; 2093-985X
• DOI: 10.1007/s13534-016-0004-1

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.