Principal Component Analysis (PCA) is a fundamental tool for dimension reduction and exploratory analysis of multivariate data, yet standard implementations often provide limited support for ...
Some of the material on this web page is based upon work supported by the National Science Foundation under Grants SES-0350686, SES-0719055, and . Any opinions, findings and conclusions or ...
Reinforcement learning research in humans and other species indicates that rewards are represented in a context-dependent manner. More specifically, reward representations seem to be normalized as a ...
We developed a MATLAB-based toolbox for the analysis of inter-brain synchrony (IBS) and performed an experimental study to confirm its performance. To the best of our knowledge, this is the first ...
Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...
Many studies have been made on the double-fed induction generator wind turbine system (DFIG-WTS) in recent decades due to its power management capability, speed control operation, low converter cost, ...
In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of ...
The drift-diffusion model (DDM) is an important decision-making model in cognitive neuroscience. However, innovations in model form have been limited by methodological challenges. Here, we introduce ...
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