DecNefLab: A Modular and Interpretable Simulation Framework for Decoded Neurofeedback
This work addresses methodological bottlenecks for researchers in neuromedicine and cognitive neuroscience, offering a tool for in silico experimentation, though it is incremental as it builds on existing DecNef approaches.
The paper tackles the challenges in Decoded Neurofeedback (DecNef) research, such as subject variability and high experimental costs, by introducing DecNefLab, a simulation framework that models DecNef as a machine learning problem, enabling analysis of neurofeedback dynamics and protocol design.
Decoded Neurofeedback (DecNef) is a flourishing non-invasive approach to brain modulation with wide-ranging applications in neuromedicine and cognitive neuroscience. However, progress in DecNef research remains constrained by subject-dependent learning variability, reliance on indirect measures to quantify progress, and the high cost and time demands of experimentation. We present DecNefLab, a modular and interpretable simulation framework that formalizes DecNef as a machine learning problem. Beyond providing a virtual laboratory, DecNefLab enables researchers to model, analyze and understand neurofeedback dynamics. Using latent variable generative models as simulated participants, DecNefLab allows direct observation of internal cognitive states and systematic evaluation of how different protocol designs and subject characteristics influence learning. We demonstrate how this approach can (i) reproduce empirical phenomena of DecNef learning, (ii) identify conditions under which DecNef feedback fails to induce learning, and (iii) guide the design of more robust and reliable DecNef protocols in silico before human implementation. In summary, DecNefLab bridges computational modeling and cognitive neuroscience, offering a principled foundation for methodological innovation, robust protocol design, and ultimately, a deeper understanding of DecNef-based brain modulation.