NeuroKit2: A Python']Python toolbox for neurophysiological signal processing

被引：616

作者：

Makowski, Dominique ^{[1
]}

Pham, Tam ^{[1
]}

Lau, Zen J. ^{[1
]}

Brammer, Jan C. ^{[2
]}

Lespinasse, Francois ^{[3
,4
]}

Pham, Hung ^{[5
]}

Schoelzel, Christopher ^{[6
]}

Chen, S. H. Annabel ^{[1
,7
,8
]}

机构：

[1] Nanyang Technol Univ, Sch Social Sci, HSS 04-19,48 Nanyang Ave, Singapore, Singapore

[2] Radboud Univ Nijmegen, Behav Sci Inst, Nijmegen, Netherlands

[3] Univ Montreal, Dept Psychol, Montreal, PQ, Canada

[4] Inst Univ Geriatr Montreal, Ctr Rech, Montreal, PQ, Canada

[5] Eureka Robot, Singapore, Singapore

[6] THM Univ Appl Sci, Life Sci Informat, Giessen, Germany

[7] Nanyang Technol Univ, Ctr Res & Dev Learning, Singapore, Singapore

[8] Nanyang Technol Univ, Lee Kong Chian Sch Med, Singapore, Singapore

来源：

BEHAVIOR RESEARCH METHODS | 2021年 / 53卷 / 04期

关键词：

Neurophysiology; Biosignals; !text type='Python']Python[!/text; ECG; EDA; EMG;

D O I：

10.3758/s13428-020-01516-y

中图分类号：

B841 [心理学研究方法];

学科分类号：

040201 ;

摘要：

NeuroKit2 is an open-source, community-driven, and user-centered Python package for neurophysiological signal processing. It provides a comprehensive suite of processing routines for a variety of bodily signals (e.g., ECG, PPG, EDA, EMG, RSP). These processing routines include high-level functions that enable data processing in a few lines of code using validated pipelines, which we illustrate in two examples covering the most typical scenarios, such as an event-related paradigm and an interval-related analysis. The package also includes tools for specific processing steps such as rate extraction and filtering methods, offering a trade-off between high-level convenience and fine-tuned control. Its goal is to improve transparency and reproducibility in neurophysiological research, as well as foster exploration and innovation. Its design philosophy is centred on user-experience and accessibility to both novice and advanced users.

引用

页码：1689 / 1696

页数：8

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