Methods in Brain Connectivity Inference through Multivariate Time Series Analysis (PDF)

Koichi Sameshima studied electrical engineering and medicine at the University of Sao Paulo. He was introduced to cognitive neuroscience, brain electrophysiology, and time-series analysis during doctoral and postdoctoral training at the University of Sao Paulo and the University of California, San Francisco, respectively. His research themes revolve around neural plasticity, cognitive function, and information processing aspects of mammalian brain through behavioral, electrophysiological, and computational neuroscience protocols. He holds an associate professorship at the Department of Radiology and Oncology, Faculty of Medicine, University of Sao Paulo.

Luiz A. Baccala majored in electrical engineering and physics at the University of Sao Paulo and then furthered his study on time-series evolution of bacterial resistance to antibiotics in a nosocomial environment, obtaining an MSc at the same university. He has since been involved in statistical signal processing and analysis and obtained his PhD from the University of Pennsylvania by proposing new statistical methods of communication channel identification and equalization. His current research interests focus on the investigation of multivariate time-series methods for neural connectivity inference and for problems of inverse source determination using arrays of sensors that include fMRI imaging and multielectrode EEG processing.

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