Abstract: Time-efficient identification of a brain network input-output (IO) dynamics model for brain stimulation includes generating an input stochastic-switched noise-modulated waveform characterized by at least one parameter modulated according to a stochastic-switched noise sequence, inputting the input stochastic-switched noise-modulated waveform to a clinical brain-response system, recording one or more time-correlated outputs of the clinical brain-response system responsive to the input stochastic-switched noise-modulated waveform, and identifying a brain network IO dynamics model that optimally correlates the input stochastic-switched noise-modulated waveform to the one or more time-delimited outputs of the clinical brain-response system. A desired brain response to an input electrical signal may be obtained using the model, such as by modulating the input electrical signal using a closed-loop control algorithm based on the brain network IO dynamics model.

Abstract: Time-efficient identification of a brain network input-output (IO) dynamics model for brain stimulation includes generating an input stochastic-switched noise-modulated waveform characterized by at least one parameter modulated according to a stochastic-switched noise sequence, inputting the input stochastic-switched noise-modulated waveform to a clinical brain-response system, recording one or more time-correlated outputs of the clinical brain-response system responsive to the input stochastic-switched noise-modulated waveform, and identifying a brain network IO dynamics model that optimally correlates the input stochastic-switched noise-modulated waveform to the one or more time-delimited outputs of the clinical brain-response system. A desired brain response to an input electrical signal may be obtained using the model, such as by modulating the input electrical signal using a closed-loop control algorithm based on the brain network IO dynamics model.

Abstract: Time-efficient identification of a brain network input-output (IO) dynamics model for brain stimulation includes generating an input stochastic-switched noise-modulated waveform characterized by at least one parameter modulated according to a stochastic-switched noise sequence, inputting the input stochastic-switched noise-modulated waveform to a clinical brain-response system, recording one or more time-correlated outputs of the clinical brain-response system responsive to the input stochastic-switched noise-modulated waveform, and identifying a brain network IO dynamics model that optimally correlates the input stochastic-switched noise-modulated waveform to the one or more time-delimited outputs of the clinical brain-response system. A desired brain response to an input electrical signal may be obtained using the model, such as by modulating the input electrical signal using a closed-loop control algorithm based on the brain network IO dynamics model.

Abstract: The subject matter disclosed herein provides methods and apparatus for closed loop operation of a wireless system implementing multiple input and multiple output (MIMO). In one aspect, there is provided a method. The method may provide channel estimation feedback to a base station by a user equipment in a wireless communication system. The method includes receiving downlink data from the base station, calculating a digital portion representing a channel parameter estimation of the downlink data, calculating an analog portion representing an error estimation of the digital portion and providing, as feedback, the digital portion and the analog portion to the base station.