Implantable medical devices (IMDs) are described. The IMDs are configured to wirelessly receive power from an electromagnetic field provided by an external charger. The IMDs include a conductive case and a header that is typically non-conductive, and which houses a three dimensional antenna structure configured to couple with the external magnetic field. Currents induced in the antenna structure are used to provide power to the IMD. The three dimensional antenna structure may be configured as a cage structure comprising a first loop antenna proximate and parallel to the front of the header, a second loop antenna proximate and parallel to the back of the header, and a third loop antenna proximate and parallel to the top of the header. The three dimensional antenna structure allows the IMD to effectively receive power from different directions, for example, if the orientation of the IMD is flipped or otherwise shifted within the patient's body.

Systems and methods for high-bandwidth, minimally invasive brain-computer interfaces (BCIs) are disclosed. The BCIs are configured for deployment and operation in conjunction with a comprehensive interventional electrophysiology procedural suite. Three primary methods of minimally invasive electrode array delivery are disclosed: (1) cortical surface delivery, (2) ventricular delivery, and (3) endovascular delivery. Additionally, systems and methods for interacting with such high-bandwidth electrode arrays are discussed, including real-time imaging, signal processing, and neural decoding. Systems and methods for architectures for accelerating the underlying computational processes (such as graphics processing units or tensor processing units) are also discussed. Multiple applications of BCIs are discussed, with emphasis on restoration, rehabilitation, and augmentation of neurologic function.

A method includes configuring, by a computing device, stimulation settings for each electrode in an at least one electrode array in physical contact with a patient, the stimulation settings having adjustable parameters comprising frequency, pulse width, and amplitude, obtaining, by the computing device, feedback information from the patient, and automatically adjusting, by the computing device, at least one of the adjustable parameters based on the feedback information from the patient.

The disclosure relates to a reduced Larsen Effect electrode. Specifically, the disclosure relates to an electrode with an insulation-coated electrode wire coaxially surrounded over a substantial portion thereof, by predetermined assembly of alternating rigid and isolating layers.

This document discusses, among other things, systems and methods for managing pain of a subject. A system includes one or more physiological sensors configured to sense a physiological signal indicative of patient brain activity. The physiological signals may include an electroencephalography signal, a magnetoencephalography signal, or a brain-evoked potential. The system may extract from the brain activity signal one or more signal metrics indicative of strength or pattern of brain electromagnetic activity associated with pain, and generate a pain score using the one or more signal metrics. The pain score can be output to a patient or a process. The system may select an electrode configuration for pain-relief electrostimulation based on the pain score, and deliver a closed-loop pain therapy according to the selected electrode configuration.

A photovoltaic unit that includes a biological interface for sensing an electrical signal from the biological tissue, the biological interface including a multilayered piezoelectric amplifier including a composite impulse generating layer including a matrix of a piezo polymeric material and dispersed phases including piezo nanocrystals and carbon nanotubes. The photovoltaic unit also includes a transducer structure comprising a fiber substrate having quantum dots present on a receiving end of the fiber. The receiving end of the fiber receiving the electrical signal. The quantum dots converts the electrical signal to a light signal.

Embodiments of the disclosure are drawn to implantable stimulator with machine learning based classifier. An implantable system includes sensors which provide sensor information to an implantable unit. The implantable unit uses a classifier on the sensor information to select a stimulation procedure which is applied via a stimulation electrode. The classifier may be generated by a trained machine learning model. The classifier may be trained on an external unit which is not implanted in the subject. The classifier may be trained based on sensor information from the implanted sensors as well as symptom information.

Various embodiments concern delivering electrical stimulation to the brain at a plurality of different levels of a stimulation parameter and sensing a bioelectrical response of the brain to delivery of the electrical stimulation for each of the plurality of different levels of the stimulation parameter. A suppression window of the stimulation parameter can be identified as having a suppression threshold as a lower boundary and an after-discharge threshold as an upper boundary based on the sensed bioelectrical responses. A therapy level of the stimulation parameter can be set for therapy delivery based on the suppression window. The therapy level of the stimulation parameter may be set closer to the suppression threshold than the after-discharge threshold within the suppression window. Data for hippocampal stimulation demonstrating a suppression window is presented.

A method of localizing brain regions for the purpose of guiding placement of electrodes and related implants is disclosed. The inventive method involves effecting a pulse in a patient's brain, temporally aligning readings taken from an electrode at various depths, measuring local field potentials at each depth during interstimulus intervals, performing a coherence analysis comparing the local field potential measurements of the different depths, and determining a corresponding brain region for the depths compared.

A device for locating target points on a magnetic resonance image of the brain of a subject includes a trained neural network configured to receive as input a 3D MR image of the brain of a subject, and to output the location, on the image, of at least one determined brain target point. The neural network includes a plurality of processing stages. Each processing stage processes an image at a respective resolution, and the processing stage of lowest resolution outputs an estimate of the location of each target point. Each other processing stage is configured to receive, from a lower resolution processing stage, an estimate of the locations of the target points, crop the input image to a smaller region surrounding each estimated target point, determine an updated estimate of the location of each target point, and provide the updated estimation to the processing stage of the next higher resolution.