Machine Learning and Brain Computer Interfaces in Normal and Diseased Conditions

Machine Learning for Objective Diagnosis of Dystonia

ML_SDClassification of spasmodic dysphonia from the normal state as well as discrimination between different phenotypes and genotypes of the disorder based on altered functional connectivity of cortical regions (Battistella et al., 2016, Eur J Neurol).

The diagnostic accuracy of dystonia is unreliable, and the consensus between physicians is generally hard to achieve. The development of an objective machine learning-based objective diagnosis of dystonia would be imperative in significantly reducing the burden of the physician’s rate of mis- and under-diagnosis, improving the patient’s quality of life, and greatly decreasing the overall costs associated with delayed treatment of this disorder. Funded by the National Institute on Deafness and other Communication Disorders, National Institutes of Health (NIDCD/NIH R01DC011805), our current studies combine large-scale multimodal neuroimaging (structural and functional MRI) with advanced machine-learning algorithms to develop objective and accurate diagnostic markers of spasmodic dysphonia. Supported by Amazon Web Services, we use convolutional neural networks for the development of automated tools of dystonia diagnosis.

Adaptive Joint Cognitive System for Decision Making

Davide_BCIEnvisioned architecture of human-AI teams for enhanced decision making (Valeriani and Poli, 2019, PLoS ONE)
 
Funded by the Department of Defense and in collaboration with the multicenter US and UK academic teams, we are working on the development of a novel architecture for complex group decision making that integrates, in an unprecedented way, the strengths of human and artificial intelligence team members while compensating for their respective weaknesses. Our team approach builds on many years of highly interdisciplinary research on group decision making assisted by brain-computer interfaces (BCIs) in human and human-machine teams, as well as state of the art machine-learning technology, neuroscience, human-factors and psychophysiologic knowledge on decision making in humans and human teams.