University of Toronto

Gerard O'Leary

Roman Genov

There is provided a system, system architecture, and method for neural cross-frequency coupling analysis. In an embodiment, the method includes: receiving neural signals; extracting a phase frequency signal and an amplitude frequency envelope signal from each of the neural signals; determining a first measure of cross-frequency coupling comprising a mean vector length modulation index (MVL-MI), determining the MVL-MI comprises determining a magnitude of an averaged complex-valued time series from a plurality of samples of the neural signals to extract a phase-amplitude coupling measure, each sample associated with a respective one of the amplitude frequency envelope signals and the phase frequency signals; and outputting at least one measure of the cross-frequency coupling.

System, system architecture, and method for neural cross-frequency coupling analysis

There is provided a system and method for classifying time series data for state identification. The method including: training a machine learning model to classify occurrences of the state; receiving a new time series data stream; determining whether a current sample in the new time series data stream is an occurrence of the state by determining a classified feature vector, the classified feature vector determined by passing the current sample and samples in at least one continuous sampling window into the trained machine learning model, each continuous sampling window including a plurality of preceding samples from the time series data, an epoch for each respective continuous sampling window determined according to a respective exponential decay rate; and outputting the determination of whether the current sample is an occurrence of the state.

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Toronto, Canada

Gerard O'Leary