Abstract: A behavioral state of a brain is classified by automatically selecting one or more sensors based on the signals received from each sensor and one or more selection criteria using one or more processors, calculating at least one measured value from the signal(s) of the selected sensor(s), classifying the behavioral state as: (a) an awake state whenever the measured value(s) for the selected sensor(s) is lower than a first threshold value, (b) a sleep state (N2) whenever the measured value(s) for the selected sensor(s) is equal to or greater than the first threshold value and the measured value(s) is not greater than a second threshold value, or (c) a slow wave sleep state (N3) whenever the measured value(s) from the selected sensor(s) is greater than the first threshold value and the measured value(s) is greater than the second threshold value, and providing a notification of the classified behavioral state.

Abstract: Occurrence of epileptic and other seizures are predicted or otherwise forecasted in ambulatory patients using a wrist-worn device and a trained machine learning algorithm. A multi-stage training process is used to train the machine learning algorithm. A first stage of the training process is implemented on EEG data obtained from bed-ridden, or otherwise non-ambulatory, subjects. A second stage of the training process may be implemented on EEG data obtained from ambulatory subjects. A third stage of the training process is implemented on a variety of data provided by a wrist-worn device. As an example, these data can include one or more of motion data (e.g., accelerometer data), skin temperature data, heart rate data, time of day, and so on. In some implementations, training data can be taken from early portions of each patient's wearable data, while testing results can be computed from the later portions, thereby skipping transfer learning steps.

Abstract: A cortical access system for delivering a medical tool into an epidural and/or subdural space and onto a patient's brain tissue through a cranium opening comprises: a turret including a proximal end portion, a distal end portion, and a first channel extending from an entrance opening to an exit opening, the first channel configured to guide the medical tool from the entrance opening to the exit opening for positioning on the patients brain tissue. The medical tool may be an electrode or an endoscope.

Abstract: A behavioral state of a brain is classified by automatically selecting one or more sensors based on the signals received from each sensor and one or more selection criteria using one or more processors, calculating at least one measured value from the signal(s) of the selected sensor(s), classifying the behavioral state as: (a) an awake state whenever the measured value(s) for the selected sensor(s) is lower than a first threshold value, (b) a sleep state (N2) whenever the measured value(s) for the selected sensor(s) is equal to or greater than the first threshold value and the measured value(s) is not greater than a second threshold value, or (c) a slow wave sleep state (N3) whenever the measured value(s) from the selected sensor(s) is greater than the first threshold value and the measured value(s) is greater than the second threshold value, and providing a notification of the classified behavioral state.

Abstract: Multiscale brain electrodes can be used for spatiotemporal mapping, probing, and therapeutic modulation of the human brain. The applications for such functional mapping and electrical stimulation modulation span, for example, neurological and psychiatric diseases, and brain rehabilitation.

Abstract: A multiscale recording and stimulation system for identifying and responding to epileptiform activity. The system includes the use of microelectrode arrays (e.g., 10-100 micron electrodes with 100-500 micron spacing) to monitor iEEG activity of submillimeter regions or islands in the epileptogenic zone of brain tissue (e.g., independent microdomains in the range of 100-1000 micron diameter).