NeuroKit2 is an open-source, community-driven, and user-friendly Python package dedicated to neurophysiological signal processing with an initial focus on bodily signals (e.g., ECG, EDA, EMG, EOG, PPG etc.). Its design philosophy is centred on user-experience and accessibility to both novice and advanced users. The package provides a consistent set of 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 dedicated to specific processing steps such as rate extraction and filtering methods, offering a trade-off between efficiency and fine-tuned control to the user.Rather than focusing on specific signals, NeuroKit2 was developed to provide a comprehensive means for a simultaneous processing of a wide range of signals. Its goal is to improve transparency and reproducibility in neurophysiological research, as well as foster exploration and innovation.

While electroencephalography (EEG) signals are commonly examined using conventional linear methods, there has been an increasing trend towards the use of complexity analysis in quantifying neural activity. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.

Semantic processing is the ultimate goal of language communication. Chinese characters and Japanese kanji both contain semantic clues in their semantic radicals, However, as Japanese is learned phonologically instead of morphologically nowadays, these clues may be more conducive to Chinese comprehension. It is therefore plausible that these inherent language differences could contribute to differential neural substrates but this has not been directly examined. To address this research gap, the current meta-analysis conducted direct contrasts between foci reported in published Chinese and Japanese fMRI studies to seek convergent activation across studies. It was found that Chinese evoked increased right hemispheric activation than Japanese, suggesting that semantic radicals might be more beneficial to Chinese than Japanese comprehension. The involvement of left supramarginal gyrus in spoken Japanese but not in spoken Chinese suggested that Japanese was processed more like alphabetic languages even though it is visually represented by characters. It might be further inferred that orthographic processing was essential for Chinese comprehension whereas phonological processing was more relevant for Japanese. The findings deepen our understanding of how linguistic characteristics shape our brains in processing semantics.