Tootfinder

On using AI for EEG-based BCI applications: problems, current challenges and future trends
Thomas Barbera, Jacopo Burger, Alessandro D'Amelio, Simone Zini, Simone Bianco, Raffaella Lanzarotti, Paolo Napoletano, Giuseppe Boccignone, Jose Luis Contreras-Vidal
https://arxiv.org/abs/2506.16168

On using AI for EEG-based BCI applications: problems, current challenges and future trends
Imagine unlocking the power of the mind to communicate, create, and even interact with the world around us. Recent breakthroughs in Artificial Intelligence (AI), especially in how machines "see" and "understand" language, are now fueling exciting progress in decoding brain signals from scalp electroencephalography (EEG). Prima facie, this opens the door to revolutionary brain-computer interfaces (BCIs) designed for real life, moving beyond traditional uses to envision Brain-to-Speech, Brain-to-…

Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives
Brian Liu, Rahul Mazumder, Peter Radchenko
https://arxiv.org/abs/2506.20114

Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives
Tree ensembles are non-parametric methods widely recognized for their accuracy and ability to capture complex interactions. While these models excel at prediction, they are difficult to interpret and may fail to uncover useful relationships in the data. We propose an estimator to extract compact sets of decision rules from tree ensembles. The extracted models are accurate and can be manually examined to reveal relationships between the predictors and the response. A key novelty of our estimator…