![]() This issue could in part be alleviated by making analyses code an integral part of scientific publications, rather than treating a paper as the sole and most important part of the research project. The opacity of data processing, such as ill-specified, or inaccessible analysis pipelines, plays a major role in the crisis. This makes it inconvenient for researchers who might have to concurrently rely on a number of software packages to process and analyze multimodal data.Īdditionally, psychology and neuroscience face a “reproducibility crisis” (Maizey & Tzavella, 2019 Miłkowski et al., 2018 Nosek et al., 2015 Topalidou et al., 2015) which has lead to a profound reassessment of research practices (by researchers, publishers, funding agencies, etc.). Moreover, many software tools for neurophysiological analyses are limited to a single type of signal (for instance ECG). Unfortunately, these algorithms are often not distributed in a usable way (i.e., in the form of packaged code) which makes them inaccessible to researchers who do not have the time or experience to implement them. Moreover, the extraction of meaningful information from neurophysiological signals is facilitated by current advances in signal processing algorithms (Clifton et al., 2012 Roy et al., 2019). The latter include low costs (especially compared with other imaging techniques, such as MRI or MEG), ease of use (e.g., portability, setup speed), and the increasing availability of recording devices (e.g., wearables Yuehong et al., 2016). Their popularity is driven by theoretical motivations (e.g., the growth of embodied or affective neuroscience Kiverstein and Miller,: 2015) as well as practical reasons. These measurements include electroencephalography (EEG), electrocardiography (ECG), electromyography (EMG) and electrodermal activity (EDA). Neurophysiological measurements increasingly gain popularity in the study of cognition and behavior. ![]()
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