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Machine Learning Techniques for Quantifying Neural Synchrony: Application to the Early Diagnosis of Alzheimer's Disease from EEG
Justin Dauwels
SSG, MIT
We present a novel approach to measure the interdependence of multiple time series, referred to as "stochastic event synchrony" (SES). As a first step, "events" from the given time series are extracted, next, those events are aligned. The better the alignment, the more the time series are considered to be similar. The similarity measure is computed by performing statistical inference on a sparse graph. As an application, we consider the problem of detecting anomalies in EEG synchrony of Mild Cognitive Impairment (MCI) patients. We present some results and discuss ideas for future research.
This talk is based on joint work with F. Vialatte (RIKEN, Japan), Theophane Weber (MIT), and A. Cichocki (RIKEN, Japan).
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