Deep Brain Stimulation Technologies Pty Ltd

The Bionics Institute of Australia

Cortical Dynamics Limited

Nicholas Campbell Sinclair

Hugh Joseph McDermott

Arthur Wesley Thevathasan

Kristian Bulluss

James Bernard Fallon

Thushara Perera

Peter John Blamey

David Tibor Julian Liley

Joel Villalobos

Hugh Mcdermott

Kyle Damon Slater

Hugh Joseph Mcdermott

Systems and Methods for Monitoring Neural Activity A method of monitoring neural activity responsive to a stimulus in a brain or a subject under general anaesthetic, the method comprising: applying the stimulus to one or more of at least one electrode implanted in a target neural structure of the brain; detecting a resonant response from the target neural structure evoked by the stimulus at one or more of the at least one electrode in or near the target neural structure of the brain; and determining one or more waveform characteristics of the detected resonant response.

Systems and methods for monitoring neural activity

A method of monitoring neural activity responsive to a stimulus in a brain, the method comprising: applying the stimulus to one or more of at least one electrode implanted in a target neural structure of the brain; detecting a resonant response from the target neural structure evoked by the stimulus at one or more of the at least one electrode in or near the target neural structure of the brain; and determining one or more waveform characteristics of the detected resonant response.

A method for monitoring neural activity responsive to a stimulus in a brain, the method comprising: a. applying a first stimulus to one or more of at least one electrode implanted in the brain, the first stimulus comprising a first plurality of bursts of stimulation, b. detecting high frequency oscillations (HFOs) between about 200 Hz and about 500 Hz due to neuronal activity at one or more of the at least one electrode implanted in the brain at least partially during application of the first stimulus; c. determining one or more waveform characteristics of the HFOs; and d. generating a second stimulus comprising a second plurality of bursts of stimulation, wherein one or more waveform characteristics of the second stimulus is dependent on the one of more waveform characteristics of the HFOs; and e. applying the second stimulus to one or more of the at least one electrode implanted in the brain.

Visual prosthesis apparatus includes an image receiver, a processor coupled to the image receiver, a stimulation device coupled to the processor, and an eye monitoring device coupled to the processor. The image receiver receives a sequence of images and the processor produces a stimulation signal at least partially in accordance with the received images. The stimulation device receives the stimulation signal and stimulates visual neurons of a user accordingly to provide the user with a visual percept. The eye monitoring device monitors one or more ocular parameters at an eye region of the user, wherein one or more control functions are associated with the monitored ocular parameters. The processor controls the visual prosthesis apparatus in accordance with the monitored ocular parameters and the associated control functions. The one or more ocular parameters can include pupil size, state of closure of the user's eyelid and direction of eye gaze.

Visual prosthesis apparatus

A method of displaying the activity of a brain, the method including the steps of: (i) obtaining an electroencephalogram (EEG) signal from the brain; (ii) segmenting said EEG signal into either contiguous or overlapping segments comprised of a sequential number of samples of said EEG signal; (iii) representing said EEG segments as a fixed order autoregressive moving average (ARMA) signal representation with an autoregressive order between 8 and 13 and a moving average order between 5 and 11; (iv) rewriting in z-domain notation said fixed order ARMA signal representation to obtain a z-domain representation; (v) generating AR coefficient data and MA coefficient data for said segments of said EEG signal for said fixed order ARMA signal representation: (vi) determining the poles and zeros of said z-domain representation for said segments of said EEG signal by substituting said coefficient data for said segments of said EEG signal into said z-domain representation; (vii) calculating the sum of the number of poles determined in step (vi); (viii) calculating the sum of the number of zeros determined in step (vi); (ix) representing said ARMA signal representation as an infinite order autoregressive (AR) model in z-domain notation; (x) determining autoregressive coefficient data for said infinite AR model from the AR and MA coefficient data generated in step (v) of said fixed order ARMA representation; (xi) determining the sum of the poles for said infinite order AR model for said segments of said EEG signal as either: (a) the first autoregressive coefficient of said finite order AR model; or (b) the difference of the sum of poles and the sum of zeros as determined respectively in steps (vii) and (viii); (xii) determining an index value representing the activity of the brain for said segments of said EEG signal by applying a discriminating function to the sum of the poles of said infinite AR model for said segments of said EEG signal as determined in step (xi); and (xiii) displaying said index value on display means.

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Surrey Hills, Australia

Nicholas Campbell Sinclair